This PDF file contains the front matter associated with SPIE Proceedings Volume 7104, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Natural fluctuations in the availability of critical stopover sites coupled with anthropogenic destruction of wetlands, land-use change, and anticipated losses due to climate change present migratory birds with a formidable challenge. Space based technology in concert with bird migration modeling and geographical information analysis yields new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. At the NASA Goddard Space Flight Center, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. We use an individual-based bird biophysical migration model, driven by remotely sensed land surface data, climate and hydrologic data, and biological field observations to study migratory bird responses to environmental change in North America. Simulation allows us to study bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy budget states of migrating birds. We illustrate our approach by simulating the spring migration of pectoral sandpipers from the Gulf of Mexico to Alaska. Mean stopover length and trajectory patterns are consistent with field observations.

A method has been developed for mapping mangrove forests using two unique features of mangroves. (1) Reflected radiance of mangrove forests in short-wave-infrared bands is lower than that of ordinary vegetation. (2) Mangroves can form forests only in the intertidal zones between the mean and the highest sea levels. A Landsat/ETM+ image and a digital elevation model were used to extract mangrove forests in Iriomote Island, Okinawa, Japan. The extracted results agreed well with ground truth data.

The purpose of this paper is to assess the influence of several biotic and abiotic factors on the abundance of waterbirds in the Grado-Marano Lagoon. The Grado-Marano Lagoon is situated in the Northeast of the Adriatic Sea with an extension of approximately 160 km². ASTER satellite images were utilized to classify different types of morphologies and habitat, including sea grass meadows. Four abiotic factors (total nitrogen, total phosphorous, alkalinity and sediment texture) and three biotic factors (benthic community, sea grass meadows and waterbird guild abundance) were integrated into a GIS. A regular UTM grid of square cells (Operational Geographic Units, OGUs), 1km x 1km, was superimposed on the entire lagoon. Using the Hierarchical Cluster Analysis (HCA) technique it was possible to delineate ecological units (clusters of OGUs) and Principal Component Analysis (PCA) was used to reduce the dimensionality of the factors considered. Subsequently, Correspondence Analysis (CA) was used to examine the relationship between waterbird guild abundance and ecological units.

The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lake's limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

The surface extent of a lake reflects its water storage variations. This information has important hydrological and operational applications. However, there is a lack of information regarding this subject because the traditional methodologies for this purposes (ground surveys, aerial photos) requires high resources investments. Remote sensing techniques (optical/radar sensors) permit a low cost, constant and accurate monitoring of this parameter. The objective of this study was to determine the surface variations of Lake Izabal, the largest one in Guatemala. The lake is located close to the Caribbean Sea coastline. The climate in the region is predominantly cloudy and rainy, being the Synthetic Aperture Radar (SAR) the best suited sensor for this purpose. Although several studies have successfully used SAR products in detecting land-water boundaries, all of them highlighted some sensor limitations. These limitations are mainly caused by roughened water surfaces caused by strong winds which are frequent in Lake Izabal. The ESA's ASAR data products were used. From the set of 9 ASAR images used, all of them have wind-roughened ashore waters in several levels. Here, a chain of image processing steps were applied in order to extract a reliable shoreline. The shoreline detection is the key task for the surface estimation. After the shoreline extraction, the inundated area of the lake was estimated. In-situ lake level measurements were used for validation. The results showed good agreement between the inundated areas estimations and the lake level gauges.

During the last four decades, Greece has suffered from an enormous internal immigration. The majority of small villages were abandoned and the population has been gathered into urban areas, usually into the prefectural capital cities. Because of the significant increase of population, the urban expansion was excessive and in some cases catastrophic. A lot of changes have been occurred to the landforms, drainage networks and landuse. The Institute of geology and Mineral Exploration of Greece (I.G.M.E.), in the frame of CSF 2000 - 2006 (Community Support Framework 2000-2006), has been implementing the pilot project titled "Collection, Codification and Documentation of geothematic information for urban and suburban areas in Greece - pilot applications". Four different cities (Drama - North Greece, Nafplio & Sparta -Peloponnesus and Thrakomakedones - Attica) were selected as pilot areas.For these cities we have tried to detect and map the urban extent and expansion and estimate their growth rate, using GIS and remote sensing techniques. Multitemporal and multiresolution satellite data covering the period 1975-2007 and topographic maps at a scale of 1:5.000 were used for the urban growth mapping and observation.The qualitative and quantitative results for the cities of Nafplio & Sparta are presented in this study.

The increase of satellite images resolution and the development of computer classification methods bring new extraction information methods for urban/periurban vegetation based on synergy of low resolution images and high-resolution and field investigation for urban vegetation distribution studies. The spatio-temporal distribution of vegetation is an important component of the urban/periurban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is a fundamental aspect in landcover/landuse analysis. In order to assess de urban green dynamics the aim of this paper is to explore the potential of fractional vegetation cover (FVC) extracting from Landsat TM, ETM and IKONOS remotely sensed data and in-situ measurements for Bucharest town, Romania. Based on the assumption that pixel has a mosaic structure, have been introduced sub-pixel models for FVC estimation and a combined approach of these based on landcover classification. The experimental result indicates that the accuracy of FVC estimation using the proposed method can be up to 82.2%. The results suggest that this method may be generally useful for FVC estimation in urban and periurban areas.

Land use conversions or changes of land management practices are primary drivers of global environmental change. 'Natural experiment' situations, where some conditions vary, but other potential land use determinants remain relatively constant, offer unique opportunities to study land use change, its drivers and feedbacks on human-environment systems. The Chalkidiki peninsula in Northern Greece is an ideal test case to study recent land use transformations and socio-economic changes (e.g. resulting from accession to the EU) against a stable reference area. Of the three peninsular legs of Cassandra, Sithonia and Athos, the latter harbours the 'Autonomous Monastic State of the Holy Mountain', a sovereign and isolated monastic state. Apart from subsistence agriculture around the monasteries, it represents a Mediterranean ecosystem in a state virtually unaffected by modern human use. We have used a time series of 22 fully corrected Landsat-TM and ETM+ data to study land use/land cover change on the peninsula, and related the results to a similar study in the adjacent County of Lagadas. A diachronic land use change analysis based on SVM classification was conducted using two three image-pairs. Where natural and semi-natural vegetation formations remained stable, trends were calculated using a pixel-wise linear trend analysis of SMA-derived vegetation cover estimates. Results were interpreted using auxiliary data and in relation to the Athos area. Changes were found to result from discontinuation of extensive land use in Cassandra and Sithonia in favour of intensified agricultural use and the expansion of tourist activities, complemented by land abandonment in less attractive areas.

An increase in average air temperature across the island of Ireland has resulted in a change in the seasonality of vegetation. Current ground-based methods of monitoring seasonality are species-specific and limited to a few point locations across the country. Medium resolution satellite data, e.g. MERIS, provide a means of acquiring multi-year time series of imagery that can be used to capture the spatio-temporal dynamics in vegetation seasonality over the whole island. For this study, a geophysical measure of vegetation growth, the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), derived from MERIS Global Vegetation Index (MGVI) data is being used to determine seasonality. Tiles, extracted from a rectangular global grid, covering the island of Ireland have been processed through the European Space Agency's (ESA) Grid Processing on Demand (GPOD) service. Initial analysis of the imagery has consisted of defining an optimal time composite period in order to minimise cloud effects for daily MGVI values using ancillary cloud data from a meteorological observatory. Methods of in-situ observation of seasonality in mixed woodland have also been explored. Initial findings suggest that a 10-day composite period should be optimal for Ireland given the high occurrence of cloud cover.

Grassland ecosystem degradation and desertification has been highly concerned in China for years because such degradation is perceived to directly relate with the occurrence of sandstorms invading into north China. In this study we intend to map the spatial-temporal variation of vegetation cover density from remote sensing data in Hulun Buir, a typical grassland ecosystem with the highest biomass productivity in Inner Mongolia of China. Since NDVI is a good indicator of vegetation, a practical approach had been developed in the study to map the spatial-temporal variation of the vegetation cover. The MODIS satellite data were used for the mapping. Results from our study indicated that the vegetation cover rate had been steadily decreasing in recent years, with relatively high spatial and temporal variation. Our study reveals that the rate on average has a trend of steadily decreasing in recent years. In 2000 the rate was above 80.6% on average, while it decreased to below 76.5% in 2006. Generally the west part of the region had much lower vegetation cover rate than the east part, probably due to the fact that the east part was dominated with forest ecosystem while the west part with fragile grassland. The counties of Xinbaerhuyou Banner and Manzhouli in the west part had the lowest vegetation cover rate among the 13 counties. As to the grassland types, lowland meadow had the highest vegetation cover rate while the temperate meadow and steppe had the lowest, indicating that ecosystem degradation was very serious in the temperate meadow and steppe, which were mainly distributed in the west part of the region. Though many factors might contribute to the decrease of vegetation cover, annual precipitation vibration had very good correspondence with the up-and-down change of vegetation cover in the region. In addition, overgrazing also played an important role in accelerating the degradation under the drought year. Therefore, we were able to conclude that the grassland ecosystem in Hulun Buir was under a very serious situation of degradation and desertification. Our study suggested that the change of vegetation cover rate could be an applicable indicator for grassland ecosystem monitoring required urgently to combat grassland degradation and desertification in arid and semiarid region.

In the context of agricultural applications, the knowledge of soil moisture availability is an essential aspect for irrigation management. The microwave waveband region (SAR) has been primarily used to estimate soil moisture from Earth Observation (E.O.) data. However, the optical domain (0.4 - 2.5 μm) may as well offer the possibility to get information about soil moisture since an overall decrease of soil reflectance corresponds to increasing surface soil water content. Data from two different experiments (ESA SPARC and AgriSAR) have been exploited aiming at estimating soil moisture from optical E.O. data by using the radiative transfer model PROSAILH. A soil scale factor (α) was introduced into the model and estimated using a LUT inversion technique. Relatively high negative relationships between the α-factor and the measured soil water content (up to R² = 0.73) could be found for several crop types with low vegetation cover. The results of this study indicate the potential to retrieve surface soil moisture information from optical E.O. data for similar soil types. The method gives the advantage of retrieving simultaneously soil and canopy characteristics from the same E.O. data sources by using a physical method of parameter estimation.

In Alpine regions, snow is a predominant environmental factor. High accurate snow monitoring in the Alpine Region is of great importance as temporal and spatial variations in snow coverage. It is required for various purposes such as meteorological modelling, climate studies, snow mapping estimation of stored water equivalent or snowmelt runoff prediction. In contrast to conventional in situ snow observations, remote sensing data regularly provide spatial snow cover information which can be used for climate induced studies on snow cover variability. The main objectives of this study are to assess the accuracy of chronological sequences derived from fractional snow cover maps as well as to detect and analyze temporal and spatial variability patterns within the Alpine Region based on different statistical applications. Time series of more than 20 years (1985 - 2007) are used to derive spatial and temporal snow cover dynamics.

Plumes are a mixture of fresh water and river sediment load, with some dilution caused by currents. Spatial and temporal variation of the river plumes can be studied by remote sensing techniques. The main objectives of this work were modeling the Douro River Plume (DRP) dimension based on image segmentation of MERIS data and to establish a relationship between the DRP dimension and different input parameters. Two different segmentation techniques were applied (watershed and region-based) in order to estimate the DRP dimension of twenty-five MERIS scenes (from 2003 to 2005). Firstly, we considered a simple linear regression model of the DRP dimension on the water volume, considering seasonal effects (summer period and the rest of the year), where a significant correlation of 0.664 was found (watershed segmentation) ignoring summer period. The second proposed model consisted in the incorporation of several parameters (last available plume, water volume, tide height and wind speed), presumed to be related to the DRP dimension. A determination coefficient of 62.2% was found for watershed segmentation excluding the summer period, regarding the multiple linear regression branch of the second proposed model.

Airborne remote sensing has proven to be a cost-effective tool for monitoring natural and agricultural resources. As digital technology has evolved, airborne systems built around digital cameras have been developed, leading to numerous applications. This paper will describe the evolution of low-cost airborne remote sensing systems and describe applications in hydrology and water resources including evapotranspiration estimates of natural and agricultural vegetation, wetland and riparian corridor monitoring and other water related applications.

Thermal and spectral remotely sensed data make the monitoring from flux energy variables in the land atmosphere interface possible. Therefore, remotely sensed data can be used as an alternative to estimate actual evapotranspiration (ET) by applying the energy balance equation. In order to test the applicability of this approach in Mexico, MODIS (Moderate Resolution Imaging Spectroradiometer) estimations from land surface variables are used at 16-day intervals of composite data. Ancillary information is collected from 2000 ground stations. The methodology includes the Simplified Surface Energy Balance model (SSEB) and its intercomparison with a combined model from the Surface Energy Balance Algorithm (SEBAL) and the Two Source Energy Balance (TSEB) procedures. Preliminary results applied to one 16-day interval during winter, 2002, showed that ET is spatially structured at a landscape level. The most significant discrepancies between estimations are found due to the general assumptions applied to each model. Secondly, the use of interpolated ancilliary data from local observations, along with remote sensing data, provides a better representation of spatial variations of ET with SEBAL-TSEB model for the study period. There is not enough evidence to asses objectively the performance of both applied procedures. Further testing is required to evaluate at a local scale the reliability from estimations.

The monitoring of agricultural areas in Cyprus provides important data for efficient water supply plans and for avoiding unnecessary water lost due to inefficient irrigation. In this context, satellite remote sensing techniques may be useful as an efficient tool for monitoring agricultural areas. The objective of this study is to present the overall methodology for monitoring agricultural areas and estimating the irrigation demand in Cyprus using satellite remote sensing, irrigation models and other auxiliary data. Field spectro-radiometric measurements using SVC-HR 1024 and GER 1500 were undertaken to determine the spectral signature of different types of crops so as to assist our classification techniques. Final crop maps using Landsat TM and ETM+ can be produced and the optimal amount of irrigation demand required for certain types of crops can be determined in order to avoid any non-effective water management. This paper presents the overall methodology of the proposed research study designed to enable the implementation of an integrated approach by combining satellite remote sensing, irrigation models, micro-sensor technology and in-situ spectroradiometric measurements to determine the irrigation demand and finally to validate our results.

Daily high spatial resolution assessment of actual evapotranspiration is essential for water management and crop water requirement estimation under stress conditions. The application of energy balance models usually requires satellite observations of radiometric surface temperature with high geometrical and temporal resolutions. By now, however, high spatial resolution (~ 100 m) is available with low time frequency (approximately every two weeks); at the opposite daily acquisition are characterised by poor spatial resolution. The analysis of vegetation index (VI) and land surface temperature (LST) spatial relationship, shows in substance a scale invariant behaviour; this consideration allows the application of spatial sharpening algorithms of thermal data, by means of a combination of high spatial resolution data in VIS/NIR range with high temporal acquisition on TIR. In this paper, a sharpening algorithm was applied using the thermal bands of MODIS (MOderate resolution Imaging Spectroradiometer) and vegetation indices derived by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor; the choice of this sensors is justified by the simultaneous acquisition time. The results of this sharpening process was firstly compared against LST estimation (at the same spatial resolution) by means of the ASTER simultaneous data; then the derived high spatial resolution LST distribution was used in order to investigate the effect of the disaggregation on the outputs of surface energy balance models. The above described application was performed on a Sicilian study area.

The LAI is a key parameter in hydrological processes, especially in the physically based distribution models. It is a critical ecosystem attribute since physiological processes such as photosynthesis, transpiration and evaporation depend on it. The diffusion of water vapor, momentum, heat and light through the canopy is regulated by the distribution and density of the leaves, branches, twigs and stems. The LAI influences the sensible heat flux H in the surface energy balance single source models through the calculation of the roughness length and of the displacement height. The aerodynamic resistance between the soil and within-canopy source height is a function of the LAI through the roughness length. This research carried out a sensitivity analysis of some of the most important parameters of surface energy balance models to the LAI time variation, in order to take into account the effects of the LAI variation with the phenological period. Finally empirical retrieved relationships between field spectroradiometric data and the field LAI measured via a light-sensitive instrument are presented for a cereal field.

The use of remote sensing technology to estimate regional evapotranspiration has been carried out for many years. Recently, with the advancements in quantification of remote sensing and the access of MODIS data, more scientists have been using MODIS data to monitoring regional evapotranspiration (ET) instead of the NOAA/AVHRR data. The surface energy balance algorithm for land (SEBAL) model combined with NOAA/AVHRR and MODIS data separately is applied to estimate the 24-hour regional evapotranspiration in a semi-arid agricultural area of northern China. And the SEBAL regional evapotranspiration model calculated results from MODIS and NOAA/AVHRR data are compared with the in-situ measured ground surface evaporation. The analysis shows that in estimating regional evapotranspiration of the satellite based application, MODIS data is more appropriate than NOAA/AVHRR data.

This paper is a contribution to develop crown shape parameters-based individual tree species identification in spaceborne high resolution imagery. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify single tree species. The proposed feature analysis by using shape parameters and the selected texture parameters-the mean, variance and angular second moment(ASM) were tested and compared for single tree species delineation and identification. As expected, initial studies have shown that the crown shape parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees from QuickBird imagery.

North China Plain was the most important cropping region in China with severe challenges of water shortage. Cropping in the plain required large amount of irrigation water to support harvest. However, water resource was very limited due to high evaporation and unbalanced precipitation. Both surface and underground water resources in the region had been over-extracted. Since agriculture consists of the largest component of water uses, mapping irrigation area for estimation of agricultural water demand was urgently required to improve the administration of water resource for effective utilization in the region. We presented our systematic investigation of mapping the irrigation area in the plain using MODIS remote sensing data. Winter wheat had been identified as the main cropping systems requiring intensive irrigation during the growing season from March to early June. The normalized difference of vegetation index (NDVI) had been used to identify winter wheat and forest, which could then be used as the input for irrigation mapping. Then Vegetation supply water index (VSWI) had been used for identifying irrigated area in winter wheat field, which combined the information of temperature and growing condition of vegetation together. According to our study in North China plain, irrigation area could be properly mapped for estimation of agricultural water demand using the MODIS data. Total irrigation area of the region was about 5.9 million ha in 2006. The results indicated that the spatial variation of irrigation area was very obvious in the region. More intensive irrigation could be observed in southern Hebei and northeast Henan of the region. Irrigation percentage in these areas might reach up to 70% for winter wheat in 2006. Therefore, our study demonstrated that MODIS data could be useful for irrigation mapping in regional scale.

African agriculture is expected to be hard-hit by ongoing climate change. Effects are heterogeneous within the continent, but in some regions resulting production declines have already impacted food security. Time series of remote sensing data allow us to examine where persistent changes occur. In this study, we propose to examine recent trends in agricultural production using 26 years of NDVI data. We use the 8-km resolution AVHRR NDVI 15-day composites of the GIMMS group (1981-2006). Temporal data-filtering is applied using an iterative Savitzky-Golay algorithm to remove noise in the time series. Except for some regions with persistent cloud cover, this filter produced smooth profiles. Subsequently two methods were used to extract phenology indicators from the profiles for each raster cell. These indicators include start of season, length of season, time of maximum NDVI, maximum NDVI, and cumulated NDVI over the season. Having extracted the indicators for every year, we aggregate them for agricultural areas at sub-national level using a crop mask. The aggregation was done to focus the analysis on agriculture, and allow future comparison with yield statistics. Trend analysis was performed for yearly aggregated indicators to assess where persistent change occurred during the 26-year period. Results show that the phenology extraction method chosen has an important influence on trend outcomes. Consistent trends suggest a rising yield trend for 500-1100 mm rainfall zones ranging from Senegal to Sudan. Negative yield trends are expected for the southern Atlantic coast of West Africa, and for western Tanzania.

The aim of this study was to evaluate the potentials and limits of remote sensing time series regarding change analysis of drylands. We focussed on the assessment and monitoring of land degradation using different scales of remote sensing data. Special interest was paid on how the spatial resolutions of different sensors influence the derivation of vegetation related variables, such as trends in time and the shift of phenological cycles. Hence, a comparison was performed using high and medium resolution sensors and their suitability for monitoring land degradation will be evaluated. Long time series of Landsat TM and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a test area in the Mediterranean. At local scale additional information was delivered by a multi-seasonal land use/cover change detection (LUCC) analysis. The test site which is located in Central Macedonia (Greece) is mainly characterized by long-term, gradual processes mainly driven by grazing and the extension of irrigated arable land.

Multi-temporal imagery has been used for landuse and land cover change detection since the very early stage of remote sensing technology. As large amount of remotely sensed data have been collected, historical land cover changes and change patterns can be reconstructed by a time series recorded by images. This paper reports a study on the methodology for quantifying spatial pattern of land cover changes in an arid zone during a 13-year period and the attempts to identify the key factors for these changes. The approach is based on the post-classification method. Multi-temporal images were independently classified to establish change trajectories for the farmland land cover type. A set of class-level metrics is then calculated on the trajectory classes, including Percentage of Landscape (PLAND), Normalized Landscape Shape Index (NLSI), Interspersion and Juxtaposition Index (IJI) and Area Weighted Fractal Dimension Index (FRAC_AM). These metrics and their relationship were shown as good indicators on the environmental impact in the fragile ecosystem due to the rapid expansion of farmland accompanied with the limited water resources. The results show that spatial pattern metrics of land cover change trajectories provide an effective measurement on landscape changes, which can further be interpreted for agriculture planning and management.

Imaging spectroscopy can provide real-time high throughput information on growing crops. The spectroscopic data can be obtained from space-borne, air-borne and handheld sensors. Such data have been used for assessing the nutritional status of some field crops (maize, rice, barely, potato etc.). In this study a handheld FieldSpec 3 spectroradiometer in the 350 - 2500 nm range of the electromagnetic spectrum was evaluated for its use to estimate sugarcane leaf nitrogen concentrations. Sugarcane leaf samples from one variety viz., N19 of two age groups (4-5 and 6-7 months) were subjected to spectral and chemical measurements. Leaf reflectance data were collected under controlled conditions and leaf nitrogen concentration was obtained using an automated combustion technique (Leco TruSpec N). The potential of spectroscopic data for estimating sugarcane leaf nitrogen status was evaluated using univariate correlation and regression analyses methods with the first-order reflectance across the spectral range from 400 to 2500 nm. The variables that presented high correlation with nitrogen concentration were used to develop simple indices combining reflectances of 2-wavelengths. Simple linear regression was then used to select a model that yielded the highest R². These were the R₇₄₄ / R₂₁₄₂ index for the 4-5 months old cane crop and the (R₂₂₀₀ - R₂₀₂₅) / (R₂₂₀₀ + R₂₀₂₅) index for the 6-7 months old cane crop, with R² of 0.74 and 0.87, respectively.

Spatial variability of crop growth often needs to be evaluated due to different soil conditions, weather patterns and crop information in a region. To simulate crop growth and productivity at a regional scale, a RS- and GIS-based crop growth model named RS-CGM was developed. The model calculates crop distribution, leaf area index, soil water content using remote sensing data that were integrated in crop growth module by inputting direct forcing variables, re-calibrating specific parameters, and correcting yield prediction using simulation-observation difference of a variable. The main RS-CGM components were intensively calibrated and verified against comprehensive field measurements of soil conditions, irrigation, evapotranspiration (ET), crop leaf area index (LAI) and yields. .The RS-CGM was applied to a county in the North China Plain to simulate winter-wheat yields in spatial and temporal dimensions. The model divides the simulating area into a number of crop growth elements and calculates each element with a set of parameters, then achieves the spatial crop yields and other concerned results aggregating to administrative regions. The simulated results show that the model can effectively express the spatial variety of yields in a region. And suggest that it was feasible to develop a spatial crop growth model combined with GIS, RS, and physiological process-oriented.

The Advanced technology in space-borne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R²=0.7302. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by ASTER image at anthesis stage. The RMSE was 0.893 % for ASTER image model, and the R² was 0.7194. It is thus feasible to forecast grain quality by NRI derived from ASTER image.

The aim of the paper is mountain forest landuse/cover changes analysis due to anthropogenic and climatic stressors. Our analysis indicates a potentially application of threshold techniques to land-cover classification and changes analysis due to climatic effects for Romanian Carpathian mountain forest ecosystem. The climate of the Carpathians is moderately cool and humid, with both temperature and precipitation strongly correlated with elevation. Extreme climatic events and anthropogenic effects have a strong impact on forest ecosystem. Specific aim of this paper is to assess, forecast, and mitigate the risks of air pollution and climatic changes and extreme events on mountain forest ecosystem in Prahova Valley, Romanian Carpathians test area and to provide early warning strategies on the basis of spectral information derived from Landsat TM, ETM and MODIS satellite data over 1987- 2007 period. The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Rice crop is one of the most important agricultural products in Asia. It is necessary to monitor rice in wide area for the food control and the adjustment of food. This study focuses on the validation for monitoring of rice crop growth and extraction of rice-planted area using the German TerraSAR-X (X-band), ENVISAT-1/ASAR (C-band) and ALOS/PALSAR (L-band). TerraSAR-X is an advanced satellite which is able to observe 1m resolution with single polarization (HH or VV) or 2m resolution with dual polarization (HH/VV) in SpotLight mode. Also ASAR and PALSAR have single and dual polarization mode. Multi-temporal SAR data of each satellite are processed and analyzed to investigate temporal change of SAR backscattering coefficient of rice-planted area during the rice growing cycle with different wavelength, polarization and resolution in the test site of Hiroshima, Japan. Ground truth data are measured simultaneously with satellite observation such as height of plant, vegetation cover and Leaf Area Index (LAI) corresponding to SAR observation, and also the correlation between SAR backscattering coefficient and those parameters of rice crop growing were analyzed. SAR backscatter shows the significant change in early stage of rice growing cycle. Therefore, rice-planted area extraction is conducted with multi-temporal SAR data based on a classification technique using maximum likelihood method (MLC). In conclusion, rice crop growth and rice-planted area extraction can be successfully monitored using multi-temporal and multi-wavelength SAR data.

Surface emissivity in the thermal infrared (TIR) region is an important parameter for determining the land surface temperature from remote sensing measurements. This work compares the emissivities measured by different field methods (the Box method and the Temperature and Emissivity Separation, TES, algorithm) as well as emissivity data from ASTER scenes and the spectra obtained from the ASTER Spectral Library. The study was performed with a field radiometer having TIR bands with central wavelengths at 11.3 μm, 10.6 μm, 9.1 μm, 8.7 μm and 8.4 μm, similar to the ASTER TIR bands. The measurements were made at two sites in southern New Mexico. The first was in the White Sands National Monument, and the second was an open shrub land in the Jornada Experimental Range, in the northern Chihuahuan Desert, New Mexico, USA. The measurements show that, in general, emissivities derived with the Box method agree within 3% with those derived with the TES method for the spectral bands centered at 10.6 μm and 11.3 μm. However, the emissivities for the shorter wavelength bands are higher when derived with the Box method than those with the TES algorithm (differences range from 2% to 7%). The field emissivities agree within 2% with the laboratory spectrum for the 8-13 μm, 11.3 μm and 10.6 μm bands. However, the field and laboratory measurements in general differ from 3% to 16% for the shorter wavelength bands, i.e., 9.1 μm, 8.6 μm and 8.4 μm. A good agreement between the experimental measurements and the ASTER TIR emissivity data is observed for White Sands, especially over the 9 - 12 μm range (agreement within 4%). The study showed an emissivity increase up to 17% in the 8 to 9 μm range and an increase of 8% in emissivity ratio of average channels (8.4 μm, 8.6 μm, 9.1 μm):(10.6 μm, 11.3 μm) for two gypsum samples with different water content.

A paddy rice ecosystem is a farming system composed of paddy, animals, microbes and other environmental factors in specific time and space, with particular temporal and spatial dynamics. Since paddy rice is a main grain crop to feed above half of population in China, the performance of paddy rice ecosystem is highly concerned to yield level of paddy and food supply safety in China. Therefore, monitoring the performance of paddy rice ecosystem is very important to obtain the required information for evaluation of ecosystem health. In the study we intend to develop an approach to monitor the ecosystem performance spatially and dynamically in a regional scale using MODIS remote sensing data and GIS spatial mapping. On the basis of key factors governing the paddy rice ecosystem, we accordingly develop the following three indicators for the evaluation: Crop growing index (CGI), environmental Index (EI), and pests-diseases index (PDI). Then, we integrated the three indicators into a model with different weight coefficients to calculate Comprehensive ecosystem health index (CEHI) to evaluate the performance and functioning of paddy rice ecosystem in a regional scale. CGI indicates the health status of paddy rice calculated from the normalizing enhanced vegetation Index (EVI) retrieved from MODIS data. EI is estimated from temperature Index (TI) and precipitation Index (PI) indicating heat and water stress on the rice field. PDI reflects the damage brought by pests and diseases, which can be estimated using the information obtained from governmental websites. Applying the approach to Lower Yangtze River Plain, we monitor and evaluate the performance of paddy rice ecosystem in various stages of rice growing period in 2006. The results indicated that the performance of the ecosystem was generally very encouraging. During booting stage and heading and blooming stage, the health level was the highest in Anhui province, which is the main paddy rice producer in the region. During stage of yellow ripeness, Jiangsu province had the lowest level of performance. Yield level of paddy rice in 2006 confirms that the applicability of the proposed approach for a rapid evaluation and monitoring of agricultural ecosystem performance in Lower Yangtze River Plain. As a result, the new approach could supply scientific basis for relevant departments taking policies and measures to make sure stable development of paddy yield.

In the field of hydrological modelling, there is mostly a lack of spatially distributed data that may allow a detailed analysis of simulation results. This study was to demonstrate that remote sensing can partly fill this gap, as combining reflective and thermal data allows the retrieval of estimates for evapotranspiration (ET). Two Landsat-5 TM scenes were analysed, and the results were afterwards compared to the daily output of the Precipitation Runoff Modeling System, a conceptual model based on Hydrologic Response Units and designed for meso- to macroscale applications. For the study site, the mesoscale Ruwer basin located in the low mountain range of Rhineland-Palatinate (Germany), an overall good agreement of ET estimates retrieved by both approaches was found. At one date, some mismatches indicated clear inconsistencies in the model structure and parameterisation scheme. Based on these findings, a modified soil module was implemented to allow for a more realistic specification of land use dependant parameters. After this, PRMS provided ET estimates now very similar to those from Landsat TM, and the RMSE was reduced from 1.30 to 0.86 mm. These results indicate, that the representation of the hydrological cycle by hydrological modelling may be improved by the integration of appropriate remote sensing data.

Snow is the most important freshwater resource in northern Xinjiang, which is a typical inland arid ecosystem in western China. Snow mapping can provide useful information for water resource management in this arid ecosystem. An applicable approach for snow mapping in Northern Xinjiang Basin using MODIS data was proposed in this paper. The approach of linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions within a pixel, which was used to establish a regression function with NDSI at a 250-meter grid resolution. Field campaigns were conducted to examine whether NDSI can be used to extend the utility of the snow mapping approach to obtain sub-pixel estimates of snow cover. In addition, snow depths at 80 sampling sites were collected in the study region. The correlation between image reflectivity and snow depth as well as the comparison between measured snow spectra and image spectra were analyzed. An algorithm was developed on the basis of the correlation for snow depth mapping in the region. Validation for another dataset with 50 sampling sites showed an RMSE of 1.63, indicating that the algorithm was able to provide an estimation of snow depth at an accuracy of 1.63cm. The results indicated that snow cover area can reach 81% and average snow depth was 13.8 cm in north Xinjiang in January 2005. Generally speaking, the snow cover and depth had a trend of gradually decreasing from north to south and from the surroundings to the center. Temporally, the cover reached a maximum in early January, and the depth reached a maximum was ten days later. Snow duration was so different in different regions with the Aletai region having the longest and the Bole having the shortest. In the period of snow melting, snow depth decreased earlier, afterward snow cover dwindled. Our study showed that the spatial and temporal variation of snow cover was very critical for water resource management in the arid inland region and MODIS satellite data provide an alternative for snow mapping through dedicated development of mapping algorithms suitable for local application.

Retrieving land-surface temperature with split-window algorithm was firstly applied to NOAA-AVHRR data. With the application of MODIS sensor, its data has been used more and more widely. Since MODIS sensor is able to observe vapor in the air, it can provide the parameters including vapor content and atmospheric transmissivity for split-window algorithm which can thus be applied more conveniently. The article, adopting the split-window algorithms of Becker-Li (1990), Sobrino (1991) and Qin Zhihao (2005), retrieves the surface temperature at daytime and nighttime with MODIS1B data and compares with the surface temperature products of NASA. Finally, the algorithm of Qin Zhihao is demonstrated to be the one with higher accuracy at daytime and nighttime and the algorithm for surface temperature at nighttime is simple with acceptable accuracy.

In the present paper, the spectrum response of rice leaf to the stress of heavy metal Zinc pollution was studied in three spectral ranges of the red edge position (REP) (680-740 nm), the visible spectrum (460-680 nm) and the near infrared spectrum (750-1000 nm). The results indicate that the chlorophyll level reduces with the increase of Zinc concentration in soil. With increase of the Zinc content of rice leaves, the leaf spectral reflectivity in visible light and the range of red edge shift ascends, the leaf spectral reflectivity in the near infrared light decreases. The visible light, the near infrared light and the range of red edge shift are fitted much linearly with the logarithm of Zinc content in rice leaves with the high squared regression coefficients of 0.9182, 0.9477 and 0.9445 respectively. The regression models are reliable to estimate the Zinc content in rice leaves.

Remote sensing (RS) data can be used to estimate the chlorophyll-a concentration of water body, which has become a key issue of water quality monitoring. In this paper the field water reflectance spectra have been measured and used for monitoring the water quality of Taihu Lake according to reflectance spectra of water varying with concentrations. After analyzing the spectra characters, the optimal band range (670-710nm) were selected and the chlorophyll-a concentration by linear spectral unmixing model were retrieved using known water and chlorophyll-a endmember spectra of in situ samples. TM and MODIS image was processed and the distribution of chlorophyll-a concentration was retrieved by linear spectral unmixing method. The result showed that a fine linear correlation existed between the abundance of chlorophyll-a from the spectral unmixing model and the concentration of chlorophyll-a from the analytical result in the laboratory, and also shows that spectral unmxing model is a feasible model in the practical application for water quality monitoring.