This PDF file contains the front matter associated with SPIE Proceedings Volume 11149, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Grassland occupies a large proportion of utilised agricultural area, especially in mountainous regions. Despite its importance current and reliable data on grassland yields and cutting frequencies with a sufficient spatial coverage are lacking. Both are essential for optimizing the use of grassland, nature conservation and policy consultation. Model approaches for the assessment of grassland yields take cutting dates and frequency into account despite environmental and cultivation factors. The European Earth Observation programme Copernicus provides large quantities of spatial and temporal high resolution data collected by a set of Sentinel satellites. The freely and openly accessible Sentinel-1 radar data form a valid basis for automated satellite and ground data processing methods to detect cutting events. These cutting frequencies are a fundamental information source for further analysis – the computation of grassland yields with different model approaches. In this study we like to present our overall approach integrating and analyzing data of different sources. A comparison between two different automated data processing methods to detect cutting frequencies from radar satellite data in three different regions in Bavaria is included. The common statistical detection represents a robust and reliable way by analysing time series of Sentinel-1 radar images of the same acquisition geometry with time intervals of 6 days. In contrast, machine learning techniques offer the opportunity to increase the accuracy and limit cutting dates to more precise time intervals.

Crop monitoring at a fine scale is critical from an environmental perspective since it provide crucial information to combine increased food production and sustainable management of agricultural landscapes. The recent Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series offer a great opportunity to monitor cropland (structure, biomass and phenology) due to their high spatial and temporal resolutions. In this study, we assessed the potential of Sentinel data to derive Wet Biomass (WB), Dry biomass (DB), water content and crop Phenological Stages (PS). This study focuses on wheat and rapeseed, which represent two of the most important seasonal crops of the world in terms of occupied area. Satellites and ground data were collected over two French temperate agricultural landscapes, in northern France and Brittany. Spectral bands and vegetation indices were derived from the S-2 images and backscattering coefficients and polarimetric indicators from the S-1 images. We used linear models to estimate the Crop Parameters (CP) of wheat and rapeseed crops. Satellite images were then classified using a random forest incremental procedure based on the importance rank of the input features to discriminate PS. Results showed that S-1 features were more efficient than S-2 features to estimate CP of rapeseed while S-2 features were better for wheat. We demonstrated the high potential of S-1 and 2 to predict principal PS (kappa=0.75) while secondary PS were misclassified. For wheat, the succession of PS predicted was consistent, further research is required to confirm the potential of S-1 and 2.

This paper presents the results of a study aiming to identify targets in Synthetic Aperture Radar (SAR) images having different properties in terms of microwave scattering and temporal stability using the interferometric SAR coherence and the Principal Component Analysis (PCA). Coherence maps used in this analysis are generated starting from a time series of Sentinel-1 images. A flat area in Padan plain (Italy), characterized by agricultural fields with different crops, urban settlements and water surfaces is chosen as study area.

Accurate and reliable information on spatio-temporal extent of surface water is critical for various agriculture/environmental applications such as drought, flood monitoring, and understanding the availability of surface water for irrigation. Remote sensing (Optical as well as SAR) datasets are extremely useful to monitor sur- face water at massive scale. In monsoon months the optical remote sensing observations over semi-arid Indian sub-continent are obstructed due to cloud cover. Synthetic Aperture Radar (SAR) is a useful alternative for year-round monitoring of the surface water bodies. Sentinel-1A and 1B are very useful to monitor the changes at very high spatial resolution and frequently due to its high spatiotemporal resolution. The main objective is to establish an operational methodology for estimation of spatiotemporal variations in the surface water availability using Sentinel-1A and 1B observations. The study has been carried out in four districts of Coastal Andhra Pradesh, India viz. Guntur, Krishna, East Godavari, and West Godavari. Training data for water vs. non-water (vegetation, forest, settlements, and barren lands) classes have been obtained from field visits and high-resolution Google Map overlay in Google Earth Engine. We divided the dataset into 70% data for model training and 30% for validation and evaluated the performance of tuned random forest classifier on the validation dataset. Results show the classification accuracy of 94.32%. Further, current and historical weather observations such as rainfall were used to assess the validity of spatiotemporal surface water layers. We found a good agreement between the rainfall and surface water availability. We observed the increase in the surface water area during July-August months due to rainfall as well as flooding in the rice fields during transplanting. We propose to use the crop area map, spatiotemporal surface water layers and weather observations for drought assessment i.e., historical drought events and areas prone to agricultural drought.

We present an algorithm for estimating gross primary production (GPP) capacity based on chlorophyll indexes and light-response curves. Leaf chlorophyll content is a major determinant of GPP capacity. The initial slope of light-response curves and maximum GPP capacity under light saturation were determined using flux and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The initial slope values were fixed for seven plant types. In a previous study, maximum GPP capacity under light saturation was determined based on the linear relationship between chlorophyll index-green CI_green and GPP capacity at 2000 μmolm-² s^-1(GPP_capacity(2000)). In the relationship, vegetation was divided into three types: open shrubland, savannah, grassland, and cropland (rice paddy); deciduous and evergreen woody forest except for tropical evergreen forest; and tropical evergreen forest. Open shrublands and grasslands had higher slope values in the relation between CI_green and GPP_capacity(2000) than deciduous and evergreen woody forest except for tropical evergreen forest. Global classification results in vegetation types may have errors, and the error may propagate to GPP capacity estimation when the estimation formula is not general in vegetation types. For estimating GPP capacity globally, generic formulae in different vegetation types is better to estimate GPP capacity. Peng et al. (2017) analyzed the relationship between canopy chlorophyll content and vegetation index for maize and soybean using hyperspectral radiometer data, and found that the relationships were the same among crops with different canopy structures under a CI of 740 nm, but not when CI_green was used. We examined differences in GPP capacity estimations between CI_green and the red-edge band chlorophyll index (CI_red-edge) derived from flux and Sentinal-2/MSI sensor data. CI_red-edge at 705 nm had a stronger relationship with GPP_capacity(2000) than did CI_green.

This paper presents a prototype crop production monitoring pipeline which identifies agricultural fields planted with small grains over 19 countries in the Middle East and North Africa (MENA) and monitors those crops over the growing season. The technical approach employs an boundary-based image segmentation algorithm to define units of consistent land use, and clusters Sentinel-2 normalized difference vegetation index (NDVI) time series within the fields to identify small grains, without requiring labeled examples. The small grain fields are then monitored over the growing season on a monthly basis using time-integrated NDVI beginning at an interval from the planting date to the end of the target month. Classification accuracy is estimated at 82% for the test case, and crop deviations from the mean and/or reference year(s) have been detected within 1-2 months of planting, and are reliably detected several months before harvest.

The present study aims at analyzing the role of spatial resolution in Ecosystem Functional Types (EFT) time series’ patterns by comparing their spatial variability, retrieved at 300 m (MERIS products) and 30 m (Landsat products) of spatial resolution, with time series of Fluxnet in situ measurements, such as gross primary production (GPP) and evapotranspiration (ET). EFT maps at both spatial resolutions for 2005 and 2009 year in Sudan study region were generated, with a classification system of 64 categories. However, the 50% of the study area was covered by four representative EFTs. The main result at fine spatial resolution, related to the 2005-2009 comparison, shows a clear pattern of vegetation with high productivity and low seasonality in both years. At coarse spatial resolution, EFTs located in shrublands or forests regions are more difficult to be clearly detected. The presented methodology is absolutely replicable for using Sentinel-2 (MSI) images and Lansat-8 (OLI) in cases where the data availability of fluxnet database reaches the Sentinel-2 and Landsat-8 active period.

With the recent launch of Sentinel-2, the possibilities of image analysis increased especially in the field of land cover and crops mapping. The identification of different crop types is important particularly for agricultural field management and water resource monitoring. This paper aims to select a limited number of acquisition dates, from Sentinel-2 time series, to improve the accuracy of crop mapping method in the Haouz plain Marrakech, Morocco. First, the collected reference data was exploited to build the phenological shifts of the different crops present in the area of interest, namely: Apricot, Citrus, Olive, Cereals, watermelon and bare soil/fallow, using the derived NDVI layers. As a second step, the NDVI profiles were employed to distinguish different periods based on the spectral separability between the thematic classes. Then, crop classification independently for the 20acquired images, per period, was performed and evaluated in order to extract Overall Accuracy (OA) and kappa coefficient temporal evolution. The optimal acquisition dates per period were determined, relying on the calculated OA values, and classified based on the corresponding NDVI time series. By minimizing the number of optimal acquisition dates, different combinations were tested and analysed (3 dates per period, 2 dates per period, 1 date per period). The classification results, proved that the proposed approach found out that three acquisition dates, well distributed through the agricultural season achieve the best differentiation between five crops and bare soil with an OA of 85%.

The oil palm is an important agro-industrial commodity in Colombia and this kind of crops is severely affected by different diseases. Specifically, the But Rot is the most common disease in oil palm crops. Traditionally, the diagnosis and control of diseases in oil palm crops is an invasive process that requires a time-consuming analysis. Therefore, an automatic classification task is required for identifying this kind of disease. On one hand, hyperspectral images (HSI) are used as a remote sensing tool that allows the identification of different features in a land cover, including diseases in crops and materials. However, the HSI classification is a challenging task, due to the spectral signature is typically associated with a unique class, and consequently, it causes low accuracy on classification results. In other words, most of HSI classification methods do not consider the contributions of one or more materials in the measured spectral pixel, leading to a multiclass model at sub-pixel levels. To address this limitation, spectral unmixing (SU) approach has been used to estimate the contributions of the different materials covered by a pixel. On the other hand, convolutional neural networks (CNN) have demonstrated great a performance in classification tasks using HSI. In this work, a classification approach of the But Rot disease in oil palm is proposed by using SU and CNN through HSI. The architecture of the proposed classifier contains 5 layers which are two convolutional layers, two ReLu layer, and one full connected layer. Simulations results show that the proposed classification method exhibits the best classification performance in terms of the overall accuracy, achieving up 88:7861% using only 20% of the training samples, compared to traditional classification approaches.

The miniaturisation of multispectral sensors in recent years have resulted in a proliferation of applications particularly in vegetation-focused studies using lightweight drones. Multi-camera arrays (MCAs), capable of capturing information over different wavelength intervals using separate cameras with specific band-pass filters, are now commonplace in this field. However, data from MCAs require a considerable amount of geometric and radiometric corrections if high quality reflectance products are to be delivered. Some aspects of this workflow can be handled by commercial software packages (e.g. Pix4D and Agisoft Metashape), using black box algorithms, however radiometric uncertainties within products are not reported to the end-user by the software. We present the results of two experiments using a low-cost MCA complete with irradiance sensor (Parrot Sequoia), which set out to assess the accuracy and consistency of hemispherical-conical surface reflectance factors from MCA data. Using reference panels in the field, we found that the empirical line method (ELM) generated the smallest RMSEs (0.0037) when compared to simplified single-panel based workflows; while for the latter there was little difference between using a calibrated Spectralon® panel or grey card imaged prior to the flight (0.0215 vs 0.0154 average over the four bands). Errors for a vegetated target within the survey flight were larger and comparable for all cases. Furthermore, a study on median vegetation index values for single vegetation canopies showed that illumination correction using irradiance data still yields significant differences in resulting values between two acquisitions during changing direct and diffuse irradiance conditions. We therefore highlight the importance of critical assessment prior to integrating drone derived MCA-measured reflectance factors into further geospatial workflows.

Information about NPK content in farm soil is important for further application of the necessary dosage of fertilizer. At present, laboratory-based chemical analysis is being used to assess the status of soil nutrients, but these methods are complex, tedious, costly, and poor in in-situ condition. While, for precise farming, in-situ assessment of soil fertility status is of prime importance. Hyper-spectral remote sensing bears the potential of a detailed investigation of soil through analysis of its spectral absorption features. Therefore, this study aims to estimate quantitatively the content of fertilizer in a sample of soil making use of spectral properties of macro soil nutrients. For addressing the abovementioned issues, this study has used Derivative Analysis for Spectral Unmixing (DASU), approach consisting of spectral unmixing and spectral derivative analysis. The study reveals that the spectral region 993.2nm provides a unique feature. It leads to the development of a model for estimation of NPK fractional abundance in a soil sample. Further, this model has been validated for the good number of soil samples in a laboratory. Thus, the key contribution of this study is to underpin that; the hyper-spectral remote sensing may be used in-situ to estimate soil fertility of farm soil. Although, the fractional abundances of individual components of NPK may be considered as future scope of work.

Leaf Area Index (LAI) is a key variable for the monitoring the ability of crops to intercept solar energy for biomass production. In addition, LAI has the high seasonal variability and differ among phenophases. Therefore, accurately monitoring LAI at the vegetative, reproductive and ripening stage is central for improving yield. The aim of the study is to identify the performance of different retrieval methods on the estimation of LAI at different phenophases of rice from sentinel 2 simulated bands. To achieve this, the research seeks to answer the following research objectives (i) compare the performance of piecewise model based on the specific phenophases with single models generated from the entire active season, (ii) to determine whether phenophase models are advantageous for LAI estimation in rice. The retrieval models were developed and tested on proximal hyperspectral bands, with focus on the sentinel 2 spectral bands. The machine learning regression models (MLRAs) presented the highest retrieval accuracy during the entire growing season (R2=0.65-0.74). The hybrid model’s retrieval performance was similar to vegetation indices but with much higher errors during the entire growing season. The phenophases estimation of LAI saw a decline in the overall retrieval performance of MLRA, hybrid and VI models. The models performed much better during the vegetative stage compared to the reproductive and ripening stages. The study shows MLRA as the best model for estimating LAI during the entire growing and vegetative stages of rice growth. The hybrid models were only suited for the entire growing season (R2>0.5) but generally low when estimating for different phenohases. Finally, VI models were identified to be the best for estimating LAI during the ripening stages of irrigated rice.

Crop phenology is a key parameter for precision farming and necessary in crop models for improving water and nutrients management in space and time. It has been traditionally determined at field, with observations of biophysical parameters in correspondence with different time-scales, but depending on plot size, it is not full representative of the variability inside it and of the variation between plots. Remote sensing techniques may increase the accessibility of high frequency spatialized data that in combination with meteorological information provide a tool for monitoring crops. Particularly, an important variety of sensors suited for agricultural applications on board of unmanned aerial systems, including spectral bands coherent with satellites and field radiometry, are being applied to describe within plot variability. With this purpose, an experiment along the year 2017 has been designed, to study the behavior of more than forty varieties of barley and wheat in an intensive experiment composed of 576 micro-plots of 1.4 × 10 m size. They have been monitored at field, registering the phenology in the BBCH scale and a complete set of soil and plant biophysical parameters in coincidence to sixteen multispectral very high-resolution images, using the number of days after sowing (NADS), the accumulated growing degree days (AGDD) and accumulated reference evapotranspiration (AcETo) as temporal scales, for studying the spatio-temporal distribution response of crop. The images were supplied with a parrot sequoia® multispectral camera, at a ground sample distance of 10 cm which allow to determine the variability into the micro-plots and obtaining representative results between them. The meteorological parameters were registered in a weather station close to the experimental area. The results show that the vegetation index in combination to the growing degree days scale or the accumulate reference evapotranspiration can set the emerging, flowering and maturity stages that are crucial inputs for management and crop models. AGDD and AcETo show a better-defined plateau between flag leave and early maturity. The ratio between the TNDVI along the reproductive phase (from BBCH = 55 to 89) and the growing cycle for barley show values of 0.31 ± 0.05 in NADS, 0.45 ± 0.03 in AGDD and 0.48 ± 0.03 in AcETo. For durum wheat the 0.32 ± 0.05 (NADS), 0.46 ± 0.03 (AGDD) and 0.49 ± 0.05 (AcETo). In case of bread wheat, the values are 0.27 ± 0.03 (NADS), 0.53 ± 0.06 (AGDD) and 0.54 ± 0.05 (AcETo). These results show that proximal remote sensing is very useful in intensive experiments as prospective techniques to explore new crop varieties that could be implanted in the experimental area and setting up the tools for satellite applications.

Plant Leaf Area Index (LAI) is a key variable for several land surface processes related to vegetation dynamics. Satellite systems offer the opportunity to estimate LAI by remote, allowing to reduce costs for crop monitoring over large areas. Indeed, LAI, is a process variable of several models used for the prediction of crop biomass accumulation, yield and canopy coverage over space and time. Uncertainty of LAI estimation by means of Vegetation Indices extrapolated from multispectral satellite images is related to several factors: (i) crop type (due to the variability of plant architectures, the light extinction coefficient highly variable among crop species), (ii) the effect of environmental covariates, which affects the crop development together with the farm management. This study aimed at evaluating how the ground-LAI (Leaf Area Index) measurements and satellite vegetation indexes from Sentinel 2 reflectance were affected by the variety of crop type and the uncertainties related to different farm managements. We used Sentinel-2 imagery spanning across the growth seasons, over three agricultural sites located in the Mediterranean agroecosystem (Central Italy) and we produced satellite NDVI index to estimate LAI by remote over the sites. In the same period, with a monthly stratified random ground sampling design, the three agricultural farms with prevalent herbaceous crop cover were monitored in terms of ground- LAI variations. The novelty and the added value extracted from the results was that the uncertainty induced by farm management (FARM) variability can be relevant as much as the one caused by crop type (CROP) and seasonality (TIME) variations when ground-LAI measurements and satellite NDVI were analyzed by a set of non-liner regression models.

In this article, a multiple dimensional Look-Up-Table (LUT) is generated based on the PROSAIL model for multiple crops LAI estimation from unmanned aerial vehicle hyperspectral data. Based on Taylor expansion method (TEM) and computational geometry model (CGM), a scale transfer model considering both difference between inter-class and intraclass is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, 1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on Baotou test site, with correlation coefficient R² of 0.85 and root mean square error RMSE of 0.41m²/m². 2) The scale effect of LAI is becoming obvious with the decrease of resolution, and scale difference between inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. 3) Further research could focus on multiple scale remote sensing products validation based on the scale transfer model mentioned above.

Agricultural monitoring is of growing importance due to an increasing world population, slowing growth of agricultural output and concerns regarding food security. Remote sensing and dynamic crop modeling are powerful tools for yield prediction and frequently applied in literature. A large question arising in this context is the assimilation of remote sensing data into the model process. We present a novel technique employing Particle Swarm Optimization in an updating scheme flexibly incorporating different sources of uncertainty in both the model simulation and remote sensing observations. We tested the technique with the AquaCrop-OS model for winter wheat yield prediction by updating canopy cover obtained from remote sensing datasets. Preliminary results showed that the new method can outperform both a simple replacement update and a Kalman filter approach. It succeeded in removing the bias from field-level yield predictions and reducing the RMSE from 1.32 t/ha to 0.89 t/ha.

The temperature of the plant canopy is closely related with its transpirative status, and therefore, its stomatal conductance and cooling capacity. Ear and leaf temperature can provide useful information for monitoring crop water status, irrigation management and yield assessment. Previous studies have shown differences in temperature between ears and leaves, with higher ear temperatures than leaf temperatures observed. By employing a high resolution thermal radiometric camera for proximal imaging, temperature differences can be used for segmentation as well as for temperature estimation. This work uses thermal images taken from above the canopy at between 0.8 and 1m distance. Measurements were acquired after solar noon. The field trials were carried out in three experimental sites and two crop seasons in Spain: Aranjuez (2016/2017), Sevilla (2015/2016) and Valladolid (2016/2017). A set of 24 varieties of durum wheat in two growing conditions, irrigated and rainfed, were used to build the thermal imagery database. The algorithm uses a pipeline system to filter the low temperatures and enhance the local contrast in order to segment the ear regions in each thermal image. Finally, using the full thermal radiometric information, the algorithms provide the temperature for each ear automatically detected. The results show high correlation values between the ear temperatures manually measured (using the thermal camera software) and the ear temperatures automatically measured using an automatic image processing pipeline.

Irrigation requirements are mostly determined by estimating the atmospheric evaporative demand, in combination with precipitation data to estimate the irrigation need per field. However, in case of a high groundwater table, the contribution of capillary rise is often not taken into account. Nevertheless, this flux can contribute significantly to the actual evaporation. Ignoring this flux in irrigation practices might lead to over-irrigation and reduced yields. The significance of the groundwater flux in a furrow irrigated sugarcane plantation in Mozambique with a shallow groundwater table is presented here. Groundwater levels in a sugarcane plantation in Xinavane in Mozambique were recorded in several fields for a duration of six months. The groundwater recordings, potential evaporation estimates from satellite remote sensing, and field data were combined in the Vegetation-AtMosPhere-Soil water model (VAMPS) that was set up to understand the effect of groundwater contribution on the actual evaporation of a sugarcane field. With the hydrological field representations set up, we analyzed whether the current furrow irrigation requirement in the plantation, of 1350 mm/year for furrow irrigation, is efficient. The results show that groundwater contribution to the transpiration flux reduces the need for irrigation in the study area. As such, we conclude that the current irrigation requirement is leading to over-irrigation. The incorporation of the groundwater contribution is needed to provide adequate estimations for irrigation. A reduction in irrigation for these fields will lead to a higher water productivity in the study area.

Seed germination corresponds to the first and crucial stage of a plant’s life cycle. It is directly affected by water availability and soil characteristics, notably porosity. The seeds of many plant species are known to be photoblastic, i.e., their germination is also significantly affected by light exposure. A comprehensive understanding about the interconnected effects of these abiotic factors on seed germination is essential for the success of a broad range of applied research initiatives in agriculture and ecology. These initiatives include, for example, studies involving the germination of stress-adapted seeds in arid regions, like perennial desert habitats and desertified landscapes, and the germination of weed seeds in arable fields that may be covered by sand-textured soils (commonly referred to as natural sands). The germination of photoblastic seeds depends not only on the amount, but also on the spectral quality of the impinging light. This radiometric parameter can be expressed in terms of the ratio between red and far-red light reaching these seeds. In this research, we unveil the impact of variations in the porosity of sand-textured soils on their red to far-red ratios of transmitted light. Although one may expect that porosity can affect these ratios and, consequently, the germination of photoblastic seeds in natural sands, no systematic study about these putative connections has been reported in the literature to date. To some extent, this can be attributed to testing limitations posed by the actual handling of these granular materials, such as grain breakage and pore space disturbance, during investigations based on traditional experimental procedures. Moreover, the scant available information on these connections has been mostly derived from analyses performed on laboratory-prepared samples, which often present morphological characteristics that conspicuously differ from those of naturally-occurring deposits of these soils. In order to overcome these constraints, we employ an in silico investigation framework to carry out controlled light transmission experiments considering realistic characterizations of dry and water-saturated samples of natural sands. This framework is supported by measured spectral data and the use of a first-principles light transport model that explicitly accounts for the particulate structure of these materials. Our in silico experimental results provide a comprehensive depiction of the changes in the red to far-red ratios of light transmitted through natural sand layers of variable thickness due to variations on their porosity. Moreover, they also show that these changes are markedly modulated by the presence of water in these layers. Thus, our findings establish a predictive relationship between porosity and the light-elicited germination of photoblastic seeds in sand-textured soils subject to distinct degrees of water saturation. Accordingly, they are expected to contribute to the development of innovative technologies aimed at the predictive assessment (in situ or remote) of the compound impact of these abiotic factors on seed germination. These technologies, in turn, are likely to lead to new costeffective solutions for ongoing challenges in agriculture (e.g., crop yield enhancement) and ecology (e.g., invasive plant detection and vegetation restoration), particularly with respect to regions susceptible to extreme environmental conditions.

Many approaches for land cover classification rely on the spectral characteristics of the elements on the surface using one single multispectral image. Some land cover elements, as the vegetation and, in particular crops, are changing over seasons and over the growing cycle and may be characterized by their spectral temporal variability. In such cases, the spectral temporal variability can be used to model the crop’s phenology and predict the crop type using both spatial and temporal spectral data. In this paper we aim to exploit the temporal dimension on the crop type classification using multi-temporal multispectral data and machine learning techniques. The high revisiting frequency of Sentinel-2 satellite opens new possibilities on the exploitation of high temporal resolution multispectral data. In this investigation, we evaluated the K-nearest neighbor (KNN), Random Forest (RF) and Decision Tree (DT) methods, for mapping 18 summer crops using Sentinel-2 data. Each method was applied to three different combinations of bands: a) all Sentinel-2 spectral bands (except band 10); b) vegetation indices (NDVI, EVI), Water Indices (NDWI, NDWI2, Moisture Index) and Normalized Image Indices and Brightness and c) the combination of the spectral bands and the indices. The best precisions we achieved were 98,6% with KNN, 98,9% with RF and 98.0% using a DT.

In this paper the problem of studying the presence of different vegetation species and artificial structures in the riversides by using multispectral remote sensing information is studied. The information provided contributes to control the water resources in a region in northern Spain called Galicia. The problem is solved as a supervised classification computed over five-band multispectral images obtained by an Unmanned Aerial Vehicle (UAV). A classification scheme based on the extraction of spatial, spectral and textural features previous to a hierarchical classification by Support Vector Machine (SVM) is proposed. The scheme extracts the spatial-spectral information by means of a segmentation algorithm based on superpixels and by computing morphological operations over the bands of the image in order to generate an Extended Morphological Profile (EMP). The texture features extracted help in the classification of vegetation classes as the spatial-spectral features for these classes are not discriminant enough. The classification is computed over segments instead of pixels, thus reducing the computational cost. The experimental results over four real multispectral datasets from Galician riversides show that the proposed scheme improves over a standard classification method achieving very high accuracy results.

Managers and policy makers demand information on agriculture dynamics and distribution for the establishment of plans and strategies. For this purpose, the use of remote sensing data constitute an essential key to follow-up the agricultural systems dynamics. The aim of this study is to define a method based on fitted Normalized Difference Vegetation Index (NDVI) time series extracted from Moderate Resolution Imaging Spectroradiometer (MODIS), trend analysis tests and machine learning approaches for assessing and monitoring farming systems in a semi-arid region of Morocco. NDVI time series were smoothed using TIMESAT software for the period between 2000 and 2018. Then, three trend analysis tests were conducted which are: monotonic trend (Mann-Kendall), Man-Kendall significance and median trend (Theil-Sen). In addition, Random Forest (RF) classification methods were performed to classify the main agricultural cover type over the study area for the 2017/2018 cropping season. The results demonstrated the ability of fitted NDVI data and RF classification to identify the main agricultural systems, which are: 1) irrigated annual crop, 2) irrigated perennial crop, 3) rainfed areas and 4) fallow. Analysis of trend patterns based on fitted NDVI values shows high variability over the farming systems. Irrigated annual and perennial crops present high improvement of biomass activity with a small inter-variability with significant trend. For the Rainfed area and fallow, these classes show a non-significant trend with low degradation of productivity. In addition, these results can constitute a relevant means of control and spatio-temporal monitoring of farming systems. Overall, the results are relevant for managers and policy makers to develop procedures and actions in order to prevent environmental and agricultural events resulted from the spatio-temporal changes in farming systems.

Wetland, an ecosystem of utmost importance, providing numerous beneficial resources to both flora and fauna is the most vital component of our environment. Wetlands perform a range of environmental functions and provide numerous socio-economic benefits to the local communities. Due to increased anthropogenic pressure the fragile yet diverse ecosystem is under constant threat requiring comprehensive monitoring in order to identify and analyze the changes occurring in the wetland area. Thus, the present research was conducted to study and visualize the spatio-temporal changes occurring in the wetland and upland areas in Renuka wetland, a Ramsar site since 2005 by Land Use and Land Cover Change (LULCC). The study focuses on monitoring the status of changes taking place in land use and land cover in the area (pre-Ramsar, 1994, 2000 and post-Ramsar, 2010, 2018) using Remote Sensing (RS) and Geographic Information System (GIS). For conducting the study, identification and delineation of five major classes’ viz., forest, agriculture, water, barren and built-up was carried out using Google Earth Engine and Quantum GIS using LANDSAT images for four different years (1994, 2000 and 2010, 2018). LULC maps identifying the seasonal changes for the years 1994 and 2018 and annual changes in the area were generated for pre-Ramsar (1994, 2000) and post-Ramsar years (2010, 2018). From the land use and land cover classification it was concluded that there has been a dynamic change in the extent of urban area and the dependency on natural resources in the wetland area has increased. Urbanization and other activities are major cause for the change, thereby requiring concerted management strategies and constant monitoring. The study also suggested that remote sensing and GIS is an efficient tool for studying the variation across various seasons and years as it helped in identifying the past and present scenarios in the area which will further help in decision making and will provide options for better and efficient designing of monitoring programs. Also, the study proved that the remote sensing and GIS tools are helping in simplification and visualization by incorporating datasets and easing the study of complex ecological processes.

Machine learning and spectral index (SI) thresholding approaches have been tested for fire-severity mapping from local to regional scales in a range of forest types worldwide. While index thresholding can be easily implemented, its operational utility over large areas is limited as the optimum index may vary with forest type and fire regimes. In contrast, machine learning algorithms allow for multivariate fire classifications. This study compared the accuracy of fire-severity classifications from SI thresholding with those from Random Forests (RF). Reference data were from 3730 plots within the boundaries of eight major wildfires across the six temperate forest ‘functional’ groups of Victoria, south-eastern Australia. The reference plots were randomly divided into training and validation datasets (60/40) for each fire-severity class (unburnt, low, moderate, high) and forest functional group. SI fire-severity classifications were conducted using thresholds derived in a previous study based on the same datasets. A RF classification algorithm was trained to derive fire-severity levels based on appropriate spectral indices and their temporal difference. The RF classification outperformed the SI thresholding approach in most cases, increasing overall accuracy by 11% on a forest-group basis, and 16% on an individual wildfire basis. Adding more predictor variables into the RF algorithm did not improve classification accuracy. Greater overall accuracies (by 12% on average) were achieved when in situ data (rather than data from other fires) were used to train the RF algorithm. Our study shows the utility of Random Forest algorithms for streamlining fire-severity mapping across heterogeneous forested landscapes.

Small Woody Features (SWF) represent some of the most stable vegetated linear and small landscape features providing numerous ecological and socio-cultural functions, which can be grouped in four main categories: soil and water conservation, climate protection and adaptation, support to biological diversity, and cultural identity. Copernicus Land monitoring service, through the High-Resolution Layers, aims to map those SWFs at Pan- European level (39 countries, 6 million square kilometers) with the use of more than 37,000 Very High Spatial Resolution (VHSR) Earth Observation (EO) scenes. This unprecedented mapping exercise is focused on the extraction of SWF with a maximum width of 30m and a minimum length of 50m for linear features and a minimum and maximum area of 200 and 5,000m respectively. The main outputs are vector and raster products from the Pan-European coverage of the VHSR image data available from the European Space Agency (ESA) Copernicus Space Component Data Access (CSCDA) VHR IMAGE 2015 dataset. To fulfill this goal with a semi-automated approach, we developed a classification processing chain with the goal of very high computer efficiency and accuracy, using Object Based Image Analysis (OBIA) approach and Cloud-computing solutions. This highly efficient methodology based on differential attribute profiles (DAP) and classical classifier such as Random-Forest is particularly adapted to this exercise since it combines the use of spatial information and the spectral signature of each pixel. In this paper, we present the detailed methodology validated at Pan-European scale with various VHSR data source and landscape characteristics as well as full production results and internal validation.

Quantification and correction of error in Satellite-based Precipitation Estimates (SPEs) is indispensable for accurate climate predictions and reliable hydrological applications. The present study aims to evaluate the performance of two widely used bias-correction algorithms, i.e., Quantile mapping based on an empirical distribution (QME) and Linear scaling (LS), for application on SPEs. The performance of real-time and gauge-corrected versions of Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TRMM 3B42 and TRMM 3B42RT) are analyzed over India for 13 years (2001-2013) duration. The total bias in the TRMM datasets are initially estimated and further subjected to error components analysis, wherein, the total bias is disintegrated into three components: hit bias (H), missed precipitation (M) and false precipitation (F). Further, the QME and monthly LS algorithms are developed and applied to the TRMM datasets. The bias-corrected TRMM datasets are later subjected to error component analysis and the actual reduction in the magnitude of error components after bias correction is investigated. The results of the study highlight the presence of significant bias in TRMM datasets over India. Among the bias correction methods, the LS method outperformed the QME method in representing the average bias over India. The QME method reduced the missed precipitation error significantly; however, it increased the false alarm error in the SPEs, especially over regions of high rainfall. The Hit bias was positive in case of QME method and negative in case of the LS method. The present study highlights the significance of reduction of bias by considering individual error components, rather focusing the same on total error.

The main technical problem that may arise when trying to monitor hydrological ecosystems is that the number of desired images of a region of interest in a specific space-time window is not always available and some of the available images need to be discarded due to insufficient quality which means a considerable decrease in the size of the sample set. For this reason, the number of image samples can be multiplied by the number of satellite sensors that have detection bands in the same spectral range, available in the same space-time window. Prior to this fusion of samples, a comparison is made of the sensors placed in the available satellite platforms to validate the compatibility of them due to the difference in the characteristics of the satellite vehicle, the characteristics of each sensor, and the difference in time when the pictures were taken. In our case, the area of interest is the Gulf of California because it is an enormous biological rich ecosystem, making it an excellent scenario for climate change monitoring. This work also exposes the effect of anomalous data, such as the negative values and those outside the expected range in the images, the reason why they appear and the strategies used to minimize their effect. Finally, the scope and limitations of performing a combined use of the data coming from different satellites, thus increasing the number of available images and thus making more precise estimates of optical parameters of seas and oceans.

Erratic rainfall with varying intensity and duration has raised the risks of crop failure in semi-arid areas of south and south-east Asia. In subsistence irrigation cropping systems often it’s difficult to schedule the irrigation, i.e. when and how much water to irrigate. Therefore there is a need for a regional real / near real-time updated database on vegetation greening and browning to facilitate the irrigation scheduling decisions. With the advent of open archives of remote sensing from United States Geological Survey (USGS) and European Space Agency (ESA) have proven a unique set of long-term historical and near real-time observations. In this study, an attempt has been made to understand the vegetation greening and browning patterns using time series of remote sensing observations for irrigation water management. The main objective is to study the greening and browning of natural vegetation (i.e., grasslands and forests) and agricultural areas of Indian sub-continent for understanding the breaks in the rainfall spells and integrated approach for irrigation scheduling. The time series of vegetation indices have been extracted for predefined grid locations from Sentinel 2 remote sensing sensor. Further, an algorithm based on time series analysis were evaluated for estimating the vegetation growth stages. The estimated vegetation growth stages was compared with the agro-climatic zones. A methodology for subsistence irrigation scheduling has been proposed based on regional vegetation growth stages (i.e. onset, peak and end of the season). The estimated vegetation growth stages showed poor alignment with the agro-climatic zones. The integrated approach based on vegetation growth stages is promising for scheduling subsistence irrigation. The proposed methodology for vegetation growth stage identification has potential applications in drought risk assessment and in establishing key indicators for agro-climatic zones.

The focus of this paper will be on an innovative technology that provides a comprehensive dam safety surveillance alternative for use when conventional alignment and settlement surveys are not adequate and/or where karst features are a concern. This paper documents the results of a study of the movement of a major dam and detection of karst features in the vicinity of the dam using satellite based Interferometric Synthetic Aperture Radar (InSAR) surveys. This technology has the capability to provide one hundred percent coverage of structures and detect surface movements related to some subsurface defects before they become major performance issues. Three years of satellite radar analyses are directly compared with field investigation of collapse features and surface movements. Extensive field investigations were performed and subsurface borings were made to investigate potential karst features through targeting of satellite detected surface settlement not detectable through conventional surveys or direct inspection. The correlation between detected surface movements and karst features was exceptional. This technology is cost competitive with conventional surveys and is available and can be implemented almost anywhere in the world where conditions are favorable.

Digital Elevation Model (DEM) constitutes an essential topographic dataset, which have been used to derive hydrological features forming inputs to various models. Elevation datasets are available from several major sources (remote sensing and land survey-based techniques) and at different spatial resolutions. In particular, the resolution of DEM plays an important role in securing the expected accuracy of flood plain inundation maps as one of the major determinants. In general, most of floodplain inundation maps are created using high resolution topographic datasets such as the DEMs derived from Light Detection and Ranging data. Although these datasets provide high accuracy (1.5 m grid size), they are relatively more expensive and not fully available in Turkey covering greater areas. Therefore, coarser topographic datasets with less accuracy are used to create the floodplain inundation maps. This study applied a methodology framework in order to determine and quantify the "loss of attribute" issue when reducing the resolution of DEM as well as to understand the impact of changing DEM resolution on the floodplain hydraulic model outputs. In the study, HEC-RAS was used for the Iyidere Basin and the results clearly indicated that varying the resolution of DEMs would make significant impacts on both the depth and the extent characteristics observed in the corresponding floodplain for the river.

Water use efficiency (WUE), the ratio of gross primary production (GPP) over evapotranspiration (ET), is a critical index for understanding and predicting terrestrial ecosystem carbon-water interactions and their responses to climate change. However, the spatiotemporal variation in WUE and its controlling mechanism are poorly understood till now at regional/global scales, due to either limited data availability or uncertainties in current data streams. For overcoming such limitations, a remote sensing (RS)-based two-leaf Jarvis-type canopy conductance (RST-Gc) model was incorporated into a revised distributed hydrological model ESSI (version 3) to simulate ET. The hydrological model was validated against ET estimates from eddy covariance (EC) data and streamflow observations. A well validated global GPP dataset from Vegetation Photosynthesis Model (VPM) was then incorporated to estimate WUE for the period 2000- 2016 over Northeast China. Variations of the simulated ET, GPP and WUE were quantified and their responses to potential drivers (e.g., precipitation, temperature, net radiation, vapor pressure deficit, and lead area index) were analyzed at different timescales (monthly and inter-annual) across various ecosystems.

Lacustrine wetlands are wetlands always associated with lakes and play a major role in functioning of landscape, water and nutrients cycle, water purification, rich biodiversity and provide a wide array of useful and appreciated ecosystem services. Variation in quantity of water in lake is one of the most important criteria to understand its utilization and pressure it might be facing due to over exploitation by various sources. Among various Ramsar lakes, the lakes existing in the lesser Himalayan region are more prone to degradation as they occur in one of the most fragile zone. This creates a need to study such lakes to know the present scenario and also to identify the factors leading to its deterioration. This study aimed to analyses the variation in the water quantity and extent of Ramsar lakes in lesser Himalayan region and also, understanding utilization and application of Normalized Difference Water Index (NDWI). In this study, two Ramsar lakes (Renuka lake and Pong Dam lake) in the lesser Himalayan region were identified. To study the variation, LANDSAT images were analyzed and NDWI for each was calculated on ArcGIS interface. Study suggests that remote sensing proves to be the best tool in detecting and monitoring of the lakes in the Himalayan region which are located at high altitude and rugged terrain conditions. Results also outlined that adapting remote sensing techniques will be helpful in studying and efficiently suggesting best management practices for the wetlands which are in need of conservation or already are being conserved.

Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23 with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management.

In Japan, a sustainable supply of rice should be guaranteed since it is the staple food. Therefore it is important to stabilize rice farmers’ incomes by giving them appropriate compensation even at the time of adverse weather, e.g. cold summer. In the case of compensating farmers’ incomes based on a comparison between the usual yield and estimated yield, an accurate and quick estimation of rice yield is significant to help farmers at the time of disaster. However, a manual survey requires a huge amount of effort and time to investigate all the fields where damage has been declared, especially when a largescale cool summer occurs; therefore, we propose a yield estimation system using satellite images and part of the yield data. Our system provides an accurate and quick yield estimation for the vast extent of the fields at a reasonable cost. Many rice yield estimation methodologies utilizing satellite images have been studied over the years. For example, a crop growth model-based method and deep learning based method that utilize many satellite images taken multiple times at different times have been proposed. Although we can get much information about fields from many satellite images, systems using plural images cost much and need exhaustive calibration before the estimation. Therefore, in this study we propose a yield estimation method using a single image that is taken just before the harvest time. First, we extracted the spectral values from the satellite image using field GIS data and then used a mixed model to perform rice yield estimation. Mixed model is expanded linear regression model and able to take the difference between rice varieties, such as Yumepirika and Nanatsuboshi, into accounts. In addition, we introduced two vegetation indexes, normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), into our model as feature values. Generally, NDVI and GNDVI have a positive correlation with the volume of the plant on the field and are used for yield estimation. Of course, we could use machine learning methods, for example random forest and support vector regression. However, we adopted a mixed model considering the explainability of the results and tha fact that the number of input feature values is small. The area of interest is Asahikawa-City, Hokkaido Province, Japan. We demonstrated our method on two datasets and evaluated the performance of our model based on mean absolute error (MAE) using 10-fold cross-validation. One dataset was damaged field data acquired in 2018 (2170 fields). The other was undamaged field data acquired in 2017 (1358 fields). We used RapidEye and SPOT-6 satellite images in 2017 and 2018, respectively. Our experimental results show that our model reduces the MAE of the estimated yield by over 2.5% percent compared to conventional regression methods in each damaged field and undamaged field case.

Climate change is an environmental threat, and to keep global warming under 2°C, actions are needed to prevent escalating negative effects like droughts and flooding. Cutting emissions might not suffice, and other methods might be needed. One suggested method is Oceanic Iron Fertilization (OIF), where oceans are fertilized with iron to increase primary production and sequestration of CO₂ from atmosphere. OIF has been criticized as risky due to unknown sideeffects. Fertilization research was explored in-depth to identify risks and opportunities, and how it could be assisted by remote sensing. The Southern Ocean has been a focus of research for OIF. This region is productive, but limited by the short growth season and lack of iron. One factor in this Southern Ocean region is whale numbers. Whales are effective fertilizers, diving deep to hunt and bringing important nutrients to the surface. Whales have been focused on this paper, as OIF within their natural habitat could resemble natural fertilization. Locating whales can be done with remote sensing. Satellites can also play a role in iron fertilization by tracking resulting algal blooms. The Southern Ocean has highest potential for OIF, and the option should be explored with remote sensing and whale knowledge.

Satellite Land surface temperature (LST) is defined as the radiative skin temperature of the land. It has been widely used in many aspects of the geosciences, e.g., studies of net radiation budget at the Earth surface, monitoring state of crops and vegetation. It is an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Thus, LST has been listed as an Essential Climate Variable (ECV) in the Global Climate Observation System (GCOS). LST is one of the baseline products for the GOES-R series satellites measured from the Advanced Baseline Imager (ABI). The algorithm derivation was developed at NOAA/NESDIS center for SaTellite Applications and Research (STAR), based on a traditional split-window technique. It is primarily estimated from the top-of-atmosphere (TOA) brightness temperature (BT) at one ABI thermal infrared channel and corrected by the BT difference to the near-by thermal infrared channel. Quality of the LST estimation may vary depending on cloud fraction, water vapor, view zenith angle, etc. Such quality information, recorded as quality flags and metadata, is provided with the LST product for user reference, product monitoring and evaluation analysis. Comprehensive evaluation of the GOES-R LST product has been conducted using radiative transfer simulation datasets and proxy ABI data, before the launch of the first GOES-R satellite (i.e. GOES-16). Since then we have performed its evaluation using over one year of the on-orbit ABI SDR and LST dataset, towards its beta and provisional validated maturity levels. Quality flags and metadata of the LST product are tested and verified with local independent computation. LST retrievals were compared to in-situ LST data derived from the SURFRAD station measurements. This presentation shows our evaluation results, as well as the ABI LST derivation details, which are helpful in users’ product applications

The aim of this study was to analyze the LST on pasture areas with different systems of production and management based on survey with farmers and remote sensing data during four development cycles (2013-2017). The study was carried out on western Sao Paulo state, Brazil, which is known as its traditional pastureland areas. We analyzed different type of production system such as extensive, semi-extensive and intensive pasture. The band 10 from Landsat 8 Thermal Infrared Sensor (TIRS) was used to retrieve the LST, and to verify the inter-seasonal productivity variation in function of management and temperature we calculated Net Primary Production - NPP. By the survey and visual analysis, we could classify the pasture areas as degraded and non-degraded area. For this classification, only pasture in degradation or maintenance phase was identified. Degraded pasture in the first or second cycle was recovered after proper management. However, the inverse effect was also verified, areas in maintenance became degraded pasture. It occurred not only because of the management practices but also because of extreme meteorological conditions. For those considered degraded pasture we verified an average temperature of 27°C - 29°C, and areas with the proper management the average temperature where 24°C - 26°C. On farms which were verified lost of productivity in function of degradation the temperature raised around 2°C. For this study, the use of remote sensing data to retrieve Land Surface Temperature shows to be an additional tool for monitoring pasture areas in function of management and productivity.

Pink bollworm (Pectinophora gossypiella (Saunders), PBW) is a major pest of cotton worldwide. The system of cotton pest management adopted in Egypt is described in relation to the economic thresholds of infestation (infested green boll3%). The best way to minimize impacts of a pest outbreak is to regularly monitor the crop to detect the onset of pest and to take timely action if the pest is present. Monitoring should be done on the basis of local knowledge and up-to-date and reliable global information. Reduction in losses caused by pests by timely and effective control measures will considerably add to food production in the country. Monitoring of this pest is generally undertaken through regular field surveys, which is labour intensive, time consuming and error prone. Alternately, radiometry is a reliable technique for rapid and non-destructive assessment of plant health. The purpose of this research was to develop a new method to detect infested cotton plant with PBW without any losses to boll. Thus, a study was conducted to characterize reflectance spectra of cotton plants with known PBW infestation, and seek to identify specific narrow wavelengths sensitive to PBW damage. Reflectance measurements were made in the spectral range of 350–2500 nm using a hyperspectral radiometer. Reflectance sensitivity analysis of the hyperspectral data to PBW damage also determined. Results of this study could suggest potential usage of remote sensing in monitoring spatial distribution of the PBW, and thereby enable effective planning and implementation of site-specific pest management practices. The study shows that it is feasible to detect PBW infestation using the hyperspectral data and recognize its level, which could be utilized to monitor trade and predictions.

In green extensive context, RPAS (Remotely Piloted Aerial Systems) can provide information with a high geometric resolution. The photogrammetric survey shows the possibility of measuring morphometric parameters of forest stand or individual trees. The free accessibility to Copernicus Sentinel-2 (S2) data addresses to hypothesize scenarios where satellite spectral information and high geometric resolution of RPAS photogrammetric survey, jointly used, determine a deeper knowledge of tree characteristics. Study area is located within the “La Mandria” park (NW Italy). Survey was operated by a DJI-Phantom4 RPAS (GSD images = 5 cm). Image photogrammetric processing was achieved by AGISOFT Photoscan v1.2.4. The resulting point cloud was filtered and a raster DSM (Digital Surface Model) was generated with a GSD = 10 cm. The correspondent CHM (Canopy Height Model) was computed by difference using a DTM (Digital Terrain Model) available from the regional cartographic archive. An object-based approach (watershed segmentation) aimed at bordering tree crowns as vector polygons was run. Some tree stability parameters were obtained from CHM by zonal statistics for each crown that was also spectrally characterized (to explore its vigor) using a S2 image time series. The proposed method finds applications in the arboricultural field (ornamental context) for the survey of tree inventory data; the detected parameters can be used as input data for tree risk assessment/management models, especially in extensive contexts representing a new approach to single tree risk management based on innovative technologies and algorithms that can reduce costs of ground control/survey campaigns.

Climate variability is one of the greatest risks for farmers. The ongoing increase of natural calamities suggests that insurance strategies have to be more dynamic than previously. In this work a remote sensing-based service prototype is presented aimed at supporting insurance companies by defining an operative tool to objectively calibrate insurance annual fares, tending to cost reduction able to attract more potential customers. Methodology was applied to an agriculture devoted area located in the Vercelli province (Piemonte - NW Italy). COPERNICUS Sentinel-2 Level 2A image time series were used for this purpose jointly with MODIS data. High resolution Sentinel-2 data (GSD = 10 m) were used to map local spatial differences of crop performance, aimed at locally tuning insurance risk and fares around the average one estimated with reference to MODIS data on a longer period. The agricultural seasons 2018 were considered for this purpose. Although the work with MODIS data was carried out by authors in previous works, their integration with S2 data proved to locally tune at single field and crop type level the agronomic performances of insured areas.

Siziwang banner desert steppe is a typical arid inland region, which is also one of the most ecologically vulnerable regions in the Inner Mongolia plateau. In this article, the Siziwang banner is taken as a typical case, for studying the spatiotemporal dynamic of NPP in desert steppe based on the Carnegie-Ames-Stanford Approach (CASA) model from 2003 to 2017, and analyzing the response of grassland NPP to the change of key climate factor, including temperature and precipitation. The results indicate that, 1) in terms of time, the grassland NPP in Siziwang banner desert steppe decreases first and then rises, while in terms of space, it decreases from south to north. 2) the NPP is more sensitive to precipitation with average partial correlation coefficient of 64.74%, which is higher than that of temperature. 3) the average of lagged time for NPP responding to temperature is 1.78 months and the average of lagged time for NPP responding to precipitation is 3.17 months. Precipitation at the early stage of vegetation growing season is particularly important for the growth of grassland vegetation in desert steppe.

State forest inventory (SFI) program has been adopted for obtaining and updating forest inventory data in the territory of the Russian Federation. In the course of SFI, circular permanent sample plots (PSP) of constant radii are laid out. The number of PSP depends on the representation of plantations in the work object. Currently, high resolution satellite images are used within the framework of SFI mainly for updating the cartographic basis of forest inventory. We propose a method for joint processing of multispectral and panchromatic satellite images of high spatial resolution in order to retrieve the species composition and age classes of mixed forest stands. The method consists of several steps. The preprocessing step includes calibration, correction, matching satellite and ground data. The next step is obtaining regional specific training data from PSP measurements. For the retrieval of forest parameters, we propose the recognition method based on the modified ECOC (Error Correcting Output Codes) classifier and regularized stepwise forward feature selection. This allows us to combine spectral and texture features more effectively. The last postprocessing step is the correction of classification results using the methods of mathematical morphology. The proposed method contributes to the automation of updating data on species composition and age classes of forest stands and allows improving the efficiency of SFI works. The accuracy of the retrieval of the species composition of mixed forests using high-resolution satellite images is comparable with the accuracy of the standard archival ground inventory data.

The corn cultivation area in South Korea has been steadily decreasing every year since 1990. Although field cultivation of corn is continuously decreasing, cultivation in the paddy field is slightly increasing every year. In particular, field cultivation has decreased rapidly, from 25,207 ha in 1990 to 17,131 ha in 2004 and 14,183 ha in 2018. On the other hand, the cultivation of paddy field grew rapidly, from 780 ha in 1990 to 1087 ha in 2004 and 1289 ha in 2018. This phenomenon is interpreted as a result of the rapid development of alternative crops in response to the FTA (Free Trade Agreement), the opening of the agricultural products market, as well as the increase of rice production and the decrease of domestic rice consumption due to the decrease of rice farm income. The consumption of waxy corn has increased rapidly, but the cultivation area decreased rapidly from 1990 to 2008. In addition, as the importance of waxy corn as a health food and high-income crop has become emphasized, the area of waxy corn cultivation has increased, and research on the advancement of related cultivation technology has been actively carried out. Unlike other corn exporting countries, South Korea cultivates a variety of crops in a narrow cultivation area. Most of the corn cultivated has been grown mainly in field soils, but some farmers prefer to cultivate high yield crops in field soils. However, in order to increase the area of corn cultivation, these efforts are somewhat limited. Therefore, in this study, we try to understand growth information by combining unmanned aerial vehicle (UAV) and sensor technology as an experimental study to expand the cultivation area of corn and paddy soils. In this study, periodic monitoring of the corn cultivation area was carried out using a UAV and a multi spectral and thermal sensor. Corn growth was closely related to the NDVI (normalized difference vegetation index) and CWSI (crop water stress index). According to the growth of corn, high NDVI points were determined to have a low CWSI, indicating an inverse correlation. In particular, crops that grow rapidly and require moisture and nutrient absorption, such as corn, are sensitive to the vegetation index as well as CWSI, which is sensitive to water shortage. Therefore, in order to increase the yield of corn, it is important to properly supply nutrients and water at the optimum time. In the future, there will be a need for ways to manage crop growth and pests through continuous monitoring of growth conditions, as well as the NDVI and CWSI. Through this study, it was found that the use of UAVs and sensors can be very useful to determine size and growth status according to the growth of corn. In recent years, the growing cultivation of corn in paddy fields has become very important to establish the irrigation conditions and the drainage of water, so the utilization of NDVI and CWSI will be very useful.

The results of modeling and evaluation of processes, features and indicators of protective aircraft spraying using unmanned aerial vehicles of helicopter type, analytical expressions for the determination of individual integrated characteristics are presented. Questions of cost of performance of protective treatments by various multicopters and offers on perspective production of protective treatments with application of such devices are considered in this article.

The Special Natural Reserve of Malpaís de Güímar is a recent volcanic enclave, colonized by typical vegetation formations of the basal floor of the south of Tenerife (Canary Islands, Spain). The beekeeping that is currently carried out in this area requires a reliable mapping of the flora that conforms it. This work analyses the potential of hyperspectral images of 10 cm, captured from a UAV in June 2018, to make a preliminary thematic map of the species of interest. The study focuses on a reduced area of 6 ha, in order to assess the feasibility of the methodology and then apply it to the whole of the protected area. The results of applying a traditional algorithm to two sets of spectral bands obtained from the original 150, between 400 and 1000 nm, were compared. The first, result of a study of spectral separability and the second from the Principal Components Analysis. The best-classified species was the Cardon with an omission error of 17.70% and a commission error of 12.26%.

Remote sensing provides the ability for relatively rapid and early detection of the spatial distribution of a plant disease using methods based on thermography and canopy reflectance in visual and near-infrared wavebands. By remote mapping using color imaging and subsequent data analysis routines, it is possible to realize early detection, identification, and quantification of different relevant plant diseases. In the present work, an applicability of remote sensing with digital color imaging for detecting plant disease is demonstrated. The color and morphological features are used for analysis and classification purposes. As a case study, the image processing is applied to the color photos of banana fields obtained by a manned aerial vehicle equipped with a high resolution camera. Major banana diseases (e.g. panama etc.) exhibit symptoms on leaf area in their earlier stage of infection. Change of color and morphology features act as criteria used to identify and classify the disease. The results of plant disease recognition and identification are demonstrated.

Remote sensing has been increasingly used in monitoring and analyzing agricultural activities. Crop identification based on analysis of individual pixel response, without considering neighboring pixels, may lead to poor results. In this paper, we investigate the use of superpixels generated by the Simple Linear Iterative Clustering (SLIC) algorithm to delineate homogeneous regions in images to help crop identification. The proposed classification strategy consists of combining pixel-level classification probabilities, estimated using the pixel spectral response, with pooled probability values of the pixels located inside superpixels. Weighting both probability contributions produce a hybrid classification. We test this idea to map the interim-harvest of corn and cotton in an agricultural area in Mato Grosso State, Brazil, characterized by the presence of large farms. For this, we use a cloudfree multispectral image captured by the wide-field imaging camera onboard the China-Brazil Earth Resources Satellite 4 (CBERS-4), that acquires four bands in the visible and near-infrared with a pixel spatial resolution of 64 m/pixel. The two main crops in our study area where identified with an overall accuracy of about 85%. Encouraging results suggest that the proposed method may be used as part of a remote sensing-based crop identification system.

The main problem faced by farmers in Indonesia is the diminishing area of agricultural land due to land conversion. Therefore, farmers must be able to choose the most productive types of agricultural plants based on their capability and land suitability in their agricultural areas. This study aims to develop an agroecosystem zone in Temanggung Regency based on remote sensing image processing and geographic information systems (GIS) for evaluating land suitability. The stages of this research are: (1) inventory of land biophysical conditions using primary data (interpretation of multispectral satellite images) and secondary (maps, statistics, results of previous studies); (2) study of agroecosystem components to map agroecosystem zone in the research area; and (3) evaluation of land suitability to determine the most productive types of agricultural crops for each agroecosystem zone. This research uses quantitative empirical methods based on landscape ecological approaches. The field sample was determined by stratified random sampling. Data used include: Sentinel-2 image, geological map, soil map, slope map, rainfall data, and land suitability tables for various types of agricultural crops. Image processing techniques begin with the process of rectification, sharpening, and multispectral classification to produce landform maps and land uses. Agroecosystem zones were produced from the overlay process of the agroecosystem components resulting from image and map analysis. Field surveys on predetermined samples were used for obtaining information about the bio-geophysical conditions of the land and the existing conditions of agricultural crops and their productivity, as material for preparing the land suitability map of the research area.

Thirty-three years ago, an accident at the Chernobyl Nuclear Power Plant released huge amounts of radioactive materials like cesium, strontium and plutonium into the atmosphere, which spread over a vast area. It is known that its effects can cause damage do the DNA of living beings, leading to death or altered fitness of the biota, but the effects of land abandonment under such conditions are not, as the vicinities of the power plant were permanently evacuated. Thus, the objective of this study was to analyze the state of primary production of the vegetation in the Chernobyl Exclusion Zone after the accident, comparing with its previous state. Through Normalized Difference Vegetation Index (NDVI) and radiation measurements, the relationship between these variables was assessed using Analysis of Variance (ANOVA) and Generalized Additive Models (GAM), to understand how the vegetation responded to the radiation exposure. The data considered was Landsat satellite imagery and sampled areas registered on the field. The results show that the NDVI increased over the years after the accident and that it is independent of the current radiation measured. This suggests that to some level of radiation exposure, the positive effects of land abandonment and/or the negative effects of radiation on abundance of herbivore species surpass the long term negative effects of radiation exposure on the vegetation. Nonetheless, above some radiation threshold level, primary production is expected to be negatively affected.

Although Rwanda has accomplished significant improvements in food production in recent years, one fifth of its population remains food insecure. Agricultural information is currently collected through seasonal agricultural surveys, but more frequent and timely data collection is needed to adequately inform public and private decision-makers about the status of crops during the growing season. Sentinel-1 and -2 data are freely available with new images provided every 4-5 days. While analysis of these multispectral images has been used for agricultural applications, there are few applications to smallholder agriculture. Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers. The models were applied to satellite images of the whole country of Rwanda and validated using a test dataset from the UAV images. The Sentinel-2 model had the user’s accuracy for maize classification of 75%, while the Sentinel-1 model overestimated the maize area resulting in a user’s accuracy of <50%.

Biogeochemical ecosystem models describe the energy and mass exchange processes between natural systems and their environment. They normally require a large amount of inputs that present important spatial variations and require a parameterization. Other simpler ecosystem models focused on a single process only need a reduced amount of inputs usually derived from direct measurements and can be combined with the former models to calibrate their parameters. This study combines the biogeochemical model Biome-BGC and a production efficiency model (PEM) optimized for the study area to calibrate a key parameter for the simulation of the ecosystem water balance by Biome-BGC, the rooting depth. Daily gross primary production (GPP) time series for the 2005-2012 period are simulated by both models. First, the optimized PEM is validated against GPP derived from four eddy covariance (EC) towers located at different ecosystems representative of the study area. Next, GPP time series simulated by both models are combined to optimize rooting depth at the four sites: different values of rooting depth are tested and the one that results in the lowest root mean square error (RMSE) between the two GPP series is selected. Explained variance and relative RMSE between Biome- BGC and EC GPP series are respectively augmented between 3 and 14 percentage points (pp) and reduced between 1 and 33pp. Finally the methodology is extrapolated for the whole study area and an original rooting depth map for peninsular Spain, which is coherent with the spatial distribution of vegetation type and GPP in the study area, is obtained at 1-km spatial resolution.

Climate changes and rapid urbanization are the main factors affecting forest vegetation land cover around the globe. Satellite remote sensing data provide important information to detect changes in forest landscapes over long time periods in contrast to conventional approaches. Satellite remote sensing provides a useful tool to capture the temporal dynamics of forest vegetation change in response to climate shifts, at spatial resolutions fine enough to capture the spatial heterogeneity. In this paper, we present an integrated, standardized approach that aims at combining remote sensing data provided by different sensors, available for a long-term period (2000-2018). This multi-sensor and multi-temporal approach detects Cernica-Branesti periurban forest vegetation dynamics based on derived biophysical parameters within the highly dynamic city of Bucharest, as a test case. Landsat TM/ETM/OLI, MODIS Terra/Aqua, and Sentinel 2 data are combined in an integrated procedure to locate forest disturbances in relation with potential climate and anthropogenic drivers. To apply the approach for detecting forest land cover changes, the MODIS Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI), and Leaf Area Index (LAI) data are used to provide forest vegetation change detection information in relation with land surface temperature (LST) and climate stressors and to monitor forest vegetation phenological variations. Correlations between NDVI/EVI time series and climatic variables were computed. Forest vegetation dynamics at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system.

The aim of the study is to give assessment of and monitor the vegetation’s condition of the forest areas in which territories the predominantly forest species of the plantations is Eastern Mysian beech (Fagus orientalis), by combinative approach of Remote Sensing’s methods and generation of different vegetation indices (NDVI, NDGI). SAR and optical data of the Sentinel when the phenophase of the forest vegetation is the most active, from April to July, respectively, for the years of the selected period were chosen. Tasseled Cap Orthogonal Transformation is applied to the selected images, resulting in three components - TCT component of the "brightness", TCT component of the "wetness" and the TCT component of the "greenness". In the present research, the TCT component of the "greenness" was used, which is giving more accurate and precise data on the current state of the forest vegetation. A comparative analysis of the processed data obtained from the applied different methods and vegetation indices has been made, in order to select the higher quality and more precise results with purpose the analysis and assessment of the state of forest vegetation on the territory of the Natural Park.

One of the main issues that concerns mankind today is the problem of domestic waste and how it affects climate change, air pollution and the environment. In the present work the heat pollution from the waste disposal site is tracked at various time points. The waste disposal site near Vidin was selected for the purpose of the research. Optical satellite data from the Sentinel 2 multi-spectral instrument (MSI) and synthetic – aperture radar (SAR) data from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. The Landsat 5 -7 (ETM) and Landsat 8 (OLI / TIRS) sensors were used to calculate the surface thermal pollution of the waste disposal sites. Orthogonalization of satellite imagery was made to trace the dynamics of the main components of the Earth's surface - vegetation, moisture and soil. On this basis, a correlation is made to trace the link between the different components of the Earth's surface at different time points. Climate data on average air temperature, evapotranspiration, radiation and rainfall was used and a comparative analysis of surface temperature from landfill and climatic data was made.

As a commercial activity, agriculture is aimed primarily at production and relies on the availability of natural resources. The development of commercial activities has brought new environmental pressures on the natural capital stock. Technological progress and the desire to maximize returns and minimize costs have produced a marked intensification in agriculture over the last 40 years. Intensification can lead to degradation of soil, water and air. Water scarcity and related with it droughts have now emerged as a major challenge – and climate change is expected to make matters worse. In the last decades, Bulgarian agricultural sector is also negatively impacted by climate changes and water scarcity. Vegetation growing is limited by water scarcity and it is necessary to figure out the vegetation dynamic changes and responses to climate change to estimate the quality of ecosystems and maintain optimal ecosystem functioning. Water status can be effectively monitored by utilizing spectral indices and spectral transformation methods. Vegetation, water stress, and soil moisture indices are important to assess the crop state and its response of changing environmental conditions and to determine irrigation scheduling. The spectral transformation methods are very effective for interpretation and analysis of phenomena and processes related to the dynamics of change of the main components of the Earth surface. In the study Tasselled Cap model and obtained from its application Normalized Difference Greenness Index (NDGI) and Normalized Difference Wetness Index (NDWnI) will be applied. Microwave and optical satellite data, acquired by the sensors Sentinel 1 and Sentinel 2 of the European Space Agency Program for Earth Observation “Copernicus”, as well as climate data will be used.

The aim of the study is to obtain a quantitative assessment of soil type impact on the soil-vegetation system through the respective models. To achieve this goal, the vegetation cover of a habitat under Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora (Habitats Directive) for the 2016-2019 time period and the influence of climatic factors (precipitation and temperature) on the vegetation cover by applying Interim Ecological Monitoring (IEM) based on remotely-sensed data. Multispectral satellite data (MSI) from Sentinel-2 have been used for quantification of the soil-vegetation system. Various types of Vegetation Indices like Normalized Differential Vegetation Index (NDVI), Soiled-adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI2), Normalized Difference Infrared Index (NDII), have been estimated to determine the actual state of the vegetation cover. For tracking the “soil-vegetation” relation Normalized Differential Greenness Index (NDGI) has been applied based on orthogonalization of satellite data from Sentinel-2. The results obtained are validated by interim ecological monitoring methodology (MIEM) for habitats and are expected to review habitat(s) trends for the indicated period and such for a near - future period. The study will demonstrate the benefits of IEM application for the purposes of reporting under art. 17 from Habitats Directive.