Chlorophyll-a (Chl-a) concentration estimation by remote sensing is an important means for monitoring offshore water quality and eutrophication. In-situ hyperspectral data can achieve accurate analyses of Chl-a, but it is not suitable for regional inversion. Satellite remote sensing provides the possibility for regional inversion, but the precision is lower limited to atmospheric correction result. Therefore, this work uses machine learning to fuse in-situ hyperspectral data and Sentinel-2 multispectral instrument images to combine their complementary advantages, so as to improve the precision of regional Chl-a concentration inversion. First, the in-situ spectra were resampled based on the satellite spectral response function to obtain equivalent reflectance. Second, the spectral feature bands of Chl-a were determined by correlation analysis. Then three machine learning models, support vector regression, random forest, and back propagation neural network, were used to establish mapping relationships of feature bands between equivalent reflectance and satellite image reflectance so as to correct the satellite feature bands. Finally, Chl-a inversion models were constructed based on the satellite feature bands before and after correction. The results demonstrate that the corrected inversion model shows an increase in R2 by 0.25 and a decrease in mean relative error by 7.6%. This fusion method effectively improves the accuracy of large-scale Chl-a concentration estimation.

Understanding the impact of climate change on Earth presents a significant scientific challenge. Monitoring changes in terrestrial ecosystems, including leaf water content, is essential for assessing plant transpiration, water use efficiency, and physiological processes. Optical remote sensing, utilizing multi-angular reflectance measurements in the near infrared and shortwave infrared wavelengths, offers a precise method for estimating leaf water content. We propose and evaluate a new index based on multi-angular reflection, using 256 leaf samples from 10 plant species for calibration and 683 samples for validation. Hyperspectral indices derived from multi-angular spectra were assessed, facilitating efficient leaf water content analysis with minimal time and specific bands required. We investigate the relationship of leaf water content using spectral indices and apply linear and nonlinear regression models to calibration data, resulting in two indices for each indicator. The newly proposed indices, (R1−R2)/(R1−R3) for linear and (R1905−R1840)/(R1905−R1875) for nonlinear, demonstrate high coefficients of determination for leaf water content (>0.94) using multi-angular reflectance measurements. Published spectral indices exhibit weak relationships with our calibration dataset. The proposed leaf water content indices perform well, with an overall root mean square error of 0.0024 (g/cm2) and 0.0026 (g/cm2) for linear and nonlinear indices, respectively, validated by Leaf Optical Properties Experiment, ANGERS, and multi-angular datasets. The (R1−R2)/(R1−R3) bands show promise for leaf water content estimation. Future studies should encompass more plant species and field data.

Classification of complex and large hyperspectral images (HSIs) with machine learning (ML) algorithms is an important research area. Recently, explainable artificial intelligence (XAI), which helps to explain and interpret black-box ML algorithms, has become popular. Our study aims to present extensive research on the use of XAI methods in explaining the band effect in HSI classification and the impact of reducing the high band number of HSIs by feature selection on the performance of the classifiers. The importance levels of the spectral bands that are effective in the decisions of the different ML classifiers were examined with the deep reinforcement learning and XAI methods, such as Shapley additive explanations (SHAP) and permutation feature importance (PFI). Our work selects representative bands using SHAP and PFI as XAI analysis techniques. We evaluated the XAI-based band selection performance on three publicly available HSI datasets using random forest, light gradient-boosting machine, and extreme gradient boosting classifier algorithms. The results obtained by applying XAI and deep learning methods were used to select spectral bands. Additionally, principal component analysis, a common dimension reduction technique, was performed on the dataset used in our study. Comparable performance evaluation shows that XAI-based methods choose informative bands and outperform other methods in the subsequent tasks. Thus the global and class-based effects of spectral bands can be explained, and the performance of classifiers can be improved by eliminating features that have a negative impact on classification. In HSI classification, studies examining the decisions of ML classifiers using XAI techniques are limited. Our study is one of the pioneer studies in the usage of XAI in HSI classification.

The spatiotemporal distribution information of crops is essential. However, remote sensing mapping still faces challenges, such as fragmented land plots, cloud cover, and changes in crop morphology across the phenological stages. To address these issues, we constructed a time series multi-feature dataset using the flexible spatio-temporal data fusion method, which fused Sentinel-2 and MODIS data, with the addition of Sentinel-1 SAR data. We employed the time-weighted dynamic time warping (TWDTW) algorithm and compared its performance with the random forest (RF) algorithm. By comparing the crop classification results of different lengths of time-series multi-feature datasets, we investigate the earliest identifiable time for crops. The results demonstrate that the incorporation of multi-source time-series remote sensing data in the construction of crop phenological features effectively improves crop classification accuracy. The TWDTW algorithm attained an overall accuracy of 92.28% and a Kappa coefficient of 0.895, outperforming the RF method, which had an accuracy of 90.44% and a Kappa coefficient of 0.869. Early-stage time-series data was enough to identify wheat, whereas complete time-series data for the entire growing season was required for oilseed rape and fallow fields. In the early stages of identification, using spatial-temporal fusion can improve crop recognition accuracy. This study found that the spatio-temporal fusion of multi-source remote sensing data and TWDTW offers the potential for accurate crop classification, especially in areas with complex fragmented cropping systems.

We investigate the potential of Canavalia ensiformis green manure in enhancing soil fertility, rice growth, and environmental sustainability in Thai rice cultivation. The research integrates field measurements with high-resolution PlanetScope satellite data to monitor the environmental changes induced by green manure application. Fourteen vegetation indices (VIs) were derived from satellite data to assess the impact of green manure on rice growth and development. The results demonstrate short-term improvements in soil organic matter, nitrogen, phosphorus, and potassium levels in the green manure-amended plots, with strong correlations observed between VIs and field-measured parameters. The visible atmospherically resistant index showed the strongest correlations with soil moisture across various depths (r=0.621 to 0.935, p<0.01), and the yellow normalized difference vegetation index demonstrated the highest correlation with available phosphorus (r=0.812, p<0.05). For rice growth parameters, the carotenoid reflectance indices (CRI Green550) exhibited the strongest correlation with plant height (r=0.878), and the chlorophyll content estimation indices (Chl Green550) with chlorophyll content (r=0.642). The normalized difference vegetation index showed the highest correlation with rice yield (r=0.885). The study highlights the effectiveness of these indices in monitoring soil and plant parameters in response to green manure application. However, the benefits of green manure diminished over time, emphasizing the need for regular application and complementary soil conservation practices. The findings contribute to developing sustainable rice cultivation practices and underscore the potential of integrating green manure with remote sensing technologies to enhance soil health, crop productivity, and environmental sustainability in Thai rice farming systems.

The coffee agro-ecosystems are increasingly being transformed into small-scale coffee-growing agricultural systems. In this context, the challenge of accurately classifying coffee cropping systems (CSs) becomes more significant, particularly in regions such as Uganda where dense vegetation and diverse topography complicate traditional land surveys. We harness the capabilities of remote sensing to provide hyperspectral data crucial for distinguishing between various coffee CSs and other land covers. Specifically, we focus on the spectral analysis of three types of Robusta coffee CSs—those integrating agroforestry, those combined with banana cultivation, and those in full sun exposure. Using in situ hyperspectral measurements captured by the FieldSpec 2™ spectroradiometer across the 325 to 1075 nm range of the electromagnetic spectrum, we aimed to (1) analyze the unique spectral properties and behaviors of these Robusta coffee CSs and (2) effectively discriminate among them using advanced hyperspectral datasets alongside the machine learning (ML) classification algorithms. The key to this process was the use of narrow spectral bands (NSBs) and various narrow-band vegetation indices (VIs), serving as predictor variables. A selection of critical variables (NSB = 9 and VIs = 8) was identified through the guided regularized random forest (RF) technique and then applied to four ML algorithms—RF, stochastic gradient boosting (GB), linear discriminant analysis, and support vector machine for classification experiments. The findings indicated high discrimination accuracy, with the RF and GB algorithms achieving overall accuracies of 93% and 90.5%, respectively, when using the selected VIs, and 87.3% (RF) and 83% (GB) when applying the chosen NBSs. These results underline the efficacy of integrating hyperspectral datasets and ML algorithms in reliably categorizing Robusta coffee CSs, a crucial step toward enhancing sustainable coffee cultivation practices.

Simultaneous achievement of the detection of small targets in synthetic aperture radar (SAR) images and the visual interpretation of the entire scene remains challenging due to the SAR imaging properties. To address these challenges, a two-branch network, which includes a proposed target detection network of TYSAR-YOLOv5 and a SAR image colorization network of CycleGAN, is developed. TYSAR-YOLOv5 is established on the basis of the YOLOv5 network, which incorporates an additional detection head specifically designed for capturing small targets from shallow features. The BoTNet structure is also deployed in the backbone network to capture global information and locate targets in highly dense scenes accurately with an acceptable number of parameters. To enhance the interpretation of the surrounding environment of detected targets, the CycleGAN network known for style transfer capabilities is employed to colorize SAR images and fuse with detected targets to achieve the enhanced target detection result of the SAR image. We expanded and updated the large-scale multi-class SAR image target detection dataset-1.0 (MSAR-1.0) by adding small aircraft targets using data augmentation techniques. The reference color images corresponding to the SAR images of each target type are also added using Google Earth Engine and public vision data for SAR image colorization. Experimental results demonstrate that the developed network dominantly outperformed state-of-the-art target detection networks in small aircraft target detection and multi-class target detection tasks. Of more importance, the final visualizations of target detection exhibit excellent interpretability, which provides rich semantic information benefiting decision-making.

Mapping native grasses and alien species on river dikes is crucial for vegetation management, which influences native species conservation and river dike maintenance. We aimed to improve alien species distribution mapping of Solidago altissima and Sorghum halepense on river dikes along Tone River, by fusing satellite and drone images. The methodology includes fusing pan-sharpened WorldView-3 satellites with drone images for one sample dike. The object-based approach segmented images into objects and extracted statistical data to form datasets. Regression models were constructed from the sample dike to predict fused satellite–drone datasets for other dikes. The random forest classification model was then applied to map the distribution of alien species. Our findings highlight the enhanced mapping accuracy achieved using a fused satellite–drone dataset approach. For Solidago altissima, the fused dataset reaches the highest overall accuracy (OA) of 98.39% and a kappa coefficient of 0.976. Likewise, for Sorghum halepense, the fused dataset achieved the highest OA of 97.78% and a kappa coefficient of 0.936, compared with that of pan-sharpened and original satellite datasets. These outcomes underscore the contribution of the fused satellite–drone imagery for alien species distribution mapping in the river dike environment.

Agricultural greenhouses (AGHs) are one of the main technological methods for regulating crop growth environments globally. They play a crucial role in food production, resource conservation, and rural economies, yet they also present environmental security threats. The current research on AGH extraction primarily focuses on small scales. However, the problem of confusing greenhouses with other features has not been completely resolved, and accurately obtaining the distribution of AGHs on large spatial scales is a challenge. We extract multi-temporal spectral feature data from Sentinel-2 images using Google Earth Engine (GEE) and build a hierarchical extraction decision tree model to extract AGHs. The model achieves an overall accuracy of 95.65%, with a Kappa coefficient of 0.89. Utilizing this model, a spatial distribution map of AGHs in Jilin Province for 2021 was generated, offering an initial insight into the overall distribution of AGHs. This method also demonstrates the significant potential and extensive application prospects of utilizing Sentinel-2 data and the GEE platform for identifying AGHs. It offers a scientific foundation for the sustainable development of agricultural regions and holds significant importance in advancing the development of facility agriculture by using Sentinel-2 data embedded in GEE at a regional or global scale.

Accurate monitoring of aboveground biomass (AGB) is crucial in preventing grassland degradation and achieving carbon neutrality. Remote sensing data and machine learning-based methods have been widely used to estimate the grassland AGB from national to regional scales due to their unique advantages of low cost and high efficiency. However, in the context of significant spatial heterogeneity, the estimation process for AGB in this category usually has inherent uncertainty. Existing statistical validation methods are unable to characterize the spatial distribution of uncertainty and generally lack consideration of potential uncertainty in grassland AGB. To address this issue, we developed a framework to map the spatial distribution of uncertainty based on the quantile regression forest model. Furthermore, the framework explored the driving factors of uncertainty using the geographical detector model. The research results show that the quantile regression forests model in the framework well-estimated the grassland AGB and characterized its spatial distribution. Also, the spatial pattern of uncertainty was closely related to the AGB and affected the amount of sampling points. Among the multiple factors, the soil-adjusted vegetation indices were the primary driving force of the uncertainty. This research presents an approach for mapping uncertainty in grassland AGB estimation and spatializing estimation error, which could be an effective complement to existing AGB estimation methods and thus facilitate the accurate management of grassland resources.

Saliency detection is a conventional computer vision technique used to identify salient regions in difference images for change detection (CD) in multi-temporal/bi-temporal remotely sensed images. However, most existing saliency-based CD methods have primarily focused on analyzing the colors and brightness of different images, often neglecting the rich spectral-spatial and context-aware features of remotely sensed multi-spectral images. Furthermore, these methods independently leverage the visual saliency features and spectral-spatial features of remote sensing images, without seamlessly integrating the two components. In response to this limitation, this paper proposes a novel CD method based on context-aware saliency-spectral-spatial features, introducing a weighted coarse-to-fine fusion strategy. The proposed method fully leverages context-aware saliency features, as well as spectral and spatial features from remotely sensed images. It summarizes the combination of these features into three principles: local low-level feature, global low-level feature, and high-level feature. Then, with the assistance of segmentation objects, the proposed method generates a single-scale salience map through global-local comparisons between an object and its K-level neighborhood objects, employing the three principles of multi-level features. Finally, in accordance with principles 2 and 3, a weighted coarse-to-fine fusion strategy at the pixel level is designed to incorporate multi-scale saliency maps. Fusion weights for pixels, from coarse-to-fine scale, are adaptively obtained by considering the heterogeneity of the object they belong to and the intensity difference between the pixel and the object. To verify the effectiveness of the proposed method, ten comparative experiments were conducted against other state-of-the-art methods The experimental results show that the proposed method better employs context-aware saliency-spectral-spatial features to preserve the details of CD map changes and reduce noise. Furthermore, experiments conducted on three datasets, featuring various region types, region sizes, and spatial resolutions of remotely sensed images, highlight the feasibility and transferability of the proposed method for change detection.

Deep learning-based dehazing of remote sensing images faces two major problems: distortion of remote sensing images and lack of large-scale real paired datasets. To solve these problems, we propose a remote sensing image dehazing method based on data mixing and Laplace network. The Laplace pyramid can divide remote sensing images into different frequency domain layers (the low-frequency layer retains global color information, and the high-frequency layer retains texture details from coarse to fine), and these features are fed into a lightweight multilayer perceptron to learn long-range dependencies. A backbone network consisting of a spatial weighted residual channel attention module can help the residual haze removal module to learn the distribution of haze in remote sensing images for effective haze removal. To address the problem of lack of large-scale real datasets, we cross-mix and restructure the synthetic dataset with the small-sample real dataset, and use the restructured mixed dataset for training, and the trained model can effectively recover the color information of real remote sensing images. After validating the effectiveness and superiority of our model on synthetic datasets, hybrid datasets, and synthetic hyperspectral datasets, we conduct generalizability experiments, and the results show the potential application of our method in advanced vision tasks.

Synthetic aperture radar (SAR) inherently encounters speckle noise as a characteristic challenge, which degrades image quality significantly. In recent years, learning-based methods have achieved remarkable success in SAR despeckling. However, these methods are often limited by complex speckle-noise patterns and the size of SAR images. To further improve despeckling quality and adapt arbitrary-size SAR images, we propose a novel method based on a conditional diffusion model coupled with a multi-scale and dual attention mechanism. During despeckling, the strong modeling capability of the diffusion model enables more accurate original clear scene restoration. Multi-scale features encompassing low-frequency large structure information and high-frequency details in SAR images are aggregated and input to position and channel dual attention mechanism, which effectively extracts global context information and establishes long-range dependencies, ensuring a broader perspective and more accurate distinction between structure textures and speckle noise. Using smooth noise estimation across overlapping patches to guide the sampling process, the proposed method is capable of handling arbitrary-size SAR images. Extensive experiment demonstrates that the proposed method has outstanding despeckling performance and flexible adaptability for arbitrary-size SAR images.

Clouds are one of the major factors leading to the failure of optical remote sensing observations on Earth, and reconstructing the information loss caused by clouds in optical images has attracted significant attention. Existing methods primarily utilize multi-temporal optical images as references, which makes it challenging to reflect real-time information. Due to the cloud-penetrating capability of synthetic aperture radar (SAR) imaging, SAR images can serve as reference images to capture real-time information during the cloud removal process. We propose a cloud removal method, DS2-SO-CR, that leverages multi-source data fusion using a conditional generative adversarial network (CGAN) combined with a residual network to effectively remove clouds from multi-cloud images. We used a CGAN model to obtain SAR-to-optical images, which are then input into a fusion residual network along with SAR images and cloudy optical images. This significantly enhances the richness and accuracy of the texture in the cloud-removed images. To ensure the preservation of cloud-free regions in cloudy images during the fusion process, we added a cloud detection component to the fusion network. The proposed method was validated on the SEN12MS-CR and HN-TS datasets, achieving the following accuracy metrics: PSNR of 30.48, SSIM of 0.892, MSE of 0.038, LPIPS of 0.146, and FID of 37.53. This study provides a reference for cloud removal in remote sensing imagery of cloudy regions, as well as support for monitoring geological disasters, vegetation, and land cover changes.