Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield (ΦF) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and ΦFto drought need further study. This study normalized SIF as SIFnto account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFnanomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and ΦFduring a 2019 drought in the North China Plain (NCP). The results show SIFnprovides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and ΦF. In the first two weeks of drought, SIFndecreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFnoutperformed other indicators, proving sensitive to early drought detection. SIFnwas also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFnanomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and ΦFanomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll (ΦF) are impacted by drought and influence SIF at different stages.

Maize is a key global food crop, with China being a major producer vital for global maize supply and food security. Accurately analysing the relationships between grain yields of maize and influencing factors is crucial for enhancing crop production, evaluating arable land quality, and optimizing planting structure. However, when modelling those relationships, the coefficients of each influencing factor vary spatially and have different spatial scales, suggesting that the importance of the influencing factors is multiscale. Traditional global and local modelling methods such as Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models cannot accurately explain those multiscale spatial relationships. The Multiscale GWR (MGWR) model, an extension of GWR, addresses these limitations by allowing each explanatory variable to have a unique spatial scale. By aligning the neighbourhood structure of each variable with its corresponding spatial scale, MGWR improves the accuracy of local regression coefficient estimations, providing a more refined analysis of spatial heterogeneity. In this paper, the multiscale importance of influencing factors on maize grain yield of the Chinese mainland was apportioned via MGWR model. Our findings verified that the relationships between maize grain yield and influencing factors differ at multiple spatial scales. MGWR model can comprehensively apportion the importance of influencing factors at multiple scale, while global and local modelling methods provide biased estimations, with OLS method leaving large residues and GWR model attributing part contribution to spatially varying intercept terms. With the MGWR model, organic fertilizer and terrain aspect are globally important and their relationships with yields keep stationary; the relationships between yields and soil pH value, GDP, DEM and slope vary on a medium-scale, presenting obvious regional differences; cultivation convenience and hydrothermal conditions affect yields at small scales. The comprehensive apportionment of multiscale relationships is an important guideline for the scientific management of agriculture and arable land resources.

Accurately predicting spatially continuous daily air temperature (Ta) is critical for agriculture, environmental management, and ecology. While meteorological stations provide precise Ta data, their spatial coverage is limited. Remotely sensed land surface temperature (LST), often fused with meteorological data, offers broader spatial coverage but struggles due to complex relationships between Ta and LST, influenced by factors like topography and human activities. Traditional supervised learning methods often fail to capture the spatial autocorrelation and heterogeneity inherent in the relationships, indicating the need for a more robust approach that integrates geographic knowledge. This study proposes the spatially varying coefficients random forest (SVCRF) model, to integrate exploratory spatial data analysis (ESDAs) into random forest (RF) to capture spatially nonstationary relationships. It first stratifies the study area based on bivariate Local Indicators of Spatial Association and geographical detector, then builds several spatial RFs with specific spatial positions and extent. In each spatial RF, the distance from observation/prediction sites to its position is added as a key predictor variable to model the local spatial variations of the relationships within the spatial extent. Applied to daily Ta mapping at 1 km resolution across China using data from 5425 meteorological stations, the SVCRF model demonstrated superior accuracy, achieving root-mean-squared error (RMSE) of$1.315~^{\circ }$C and mean absolute error (MAE) of$1.014~^{\circ }$C. Compared to RF, regression kriging (RK), and geographically weighted regression (GWR), it reduced MAE by$0.351~^{\circ }$C,$0.786~^{\circ }$C, and$0.831~^{\circ }$C, respectively. The model also offers high interpretability, with uncertainty estimates aligning with actual errors and spatially resolved variable importance highlighting spatial patterns.

Change detection, a critical and flourishing Earth observation technology, aims to identify changes through cross-temporal remote sensing images acquired over the same geographical area. With the widespread use in various change scenarios, it becomes essential to utilize heterogeneous images due to the high challenge of accessing the ideal homogeneous images. Nevertheless, domain shift, generated by different imaging factors (e.g., sensors, seasons, atmosphere, illumination), makes it unable to compare the heterogeneous images directly. To address this problem, we propose a deep domain adaptation and disentangled representation network for unsupervised heterogeneous change detection (DADR-HCD), which bridges the domain gap from the perspective of causal mechanisms and compares the differences in the content feature space. In the training stage, the deep features of the input bitemporal images are further disentangled into the domain-invariant (content) features and domain-specific (style) features through an explicit image translation network. Furthermore, unlike comparing the differences at the image level or deep feature space, the change probability maps are directly calculated based on the content feature similarity in the prediction stage, which minimizes the style noise and avoids the asymmetry of image-level translation. Finally, the binary change maps are obtained using threshold segmentation and morphological post-processing strategies. The comprehensive experimental results and detailed analysis on five typical datasets demonstrate the effectiveness and superiority of the proposed DADR-HCD network in the unsupervised heterogeneous change detection task.

Accurate information on the spatial and temporal distribution of abandoned cropland (AC) is crucial for protecting arable land, and maintaining regional food security and ecological stability. Nevertheless, the unavailability of dedicated monitoring for AC, along with the extended time frame required for remote sensing surveillance and the intricate transformation of land cover types following abandonment, poses considerable challenges in producing accurate AC maps for scientific purposes. To address these challenges, a new framework for AC identification was proposed. Specifically, the change detection was performed using the phenology-enhanced change vector analysis (PECVA) method, followed by a co-training semi-supervised classification method based on historical samples and sparse samples (HS-SSC) to classify the land cover type in change areas and obtain long-term land cover mapping results. Next, a land cover type change detector method was applied to identify the location and time of AC occurrences. Sufficient comparative experiments and accuracy validation were conducted to confirm the effectiveness of the PECVA and HS-SSC. The proposed method was verified in areas with single and double cropping per year, with average precision rates of 85.40% and 83.57% respectively for multi-year AC identification. This study offers a promising tool for identifying AC, which can aid in AC recultivation and serve for arable land conservation and land resource management. Our code is available at https://github.com/zhangtingting114/ACI.

When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries.

An efficient monitoring network is very important in accessing the marine environmental quality and its protection and management. In an estuary, there are fronts that separate distinctly different water masses and affect material transport, nutrient distribution, pollutant aggregation, and diffusion. This stratified heterogeneous surface neither satisfies the stationary requirements of kriging, nor can be handled adequately by removing a spatially continuous trend. This article presents a stratified optimization method for a multivariate monitoring network. In this method, principal component analysis (PCA) was used to reduce the dimensionality of the correlated targets, and the mean of surface with nonhomogeneity (MSN) method was adopted to produce the best linear unbiased estimator for a spatially stratified heterogeneous surface that failed to satisfy the requirements for a kriging estimate. The existing monitoring network in the Yangtze River estuary and its adjacent sea, which was designed by purposive sampling year ago was optimized as an illustration. The optimization consisted of two steps: reduce the redundant monitoring sites and then optimally add new sites to the remaining sites. After optimization, the inclusion of 51 sites in the monitoring network was found to produce a smaller total estimated error than that of the current network, which has 70 sites; moreover, the use of 55 sites can produce a higher precision of estimation for all three principal components (PCs) than that of the current 70 sites. The results demonstrated that the proposed method is suitable for optimizing environmental monitoring sites that have dominant stratified nonhomogeneity and that involve multiple factors.