Early detection of rice paddies is crucial in East Asian countries that regulate cultivation areas for food security, yet frequent cloud cover and biased acreage estimates in smallholder regions call for a timely, spatially detailed method. Here we developed a framework that combines early-season rice classification with parcel delineation in smallholder landscapes. We targeted the transplanting period, when irrigation water and sparse canopies maximize the difference between the Normalized Difference Vegetation Index (NDVI) and the Land Surface Water Index (LSWI). To overcome clouds, we used deep learning regression that reconstructed cloud-masked vegetation indices, restoring coverage during the cloud-prone season instead of using full growing-season observations. We then classified rice from this single gap-filled feature and refined the 10-m result onto parcels delineated by a foundation segmentation model. Gap-filling reconstructed pixels accurately (R2 = 0.95 for NDVI and 0.90 for LSWI), and the maps reached an Overall Accuracy (OA) of 88.7 %, comparable to full growing-season benchmarks. Parcel integration improved smallholder classification and reduced area-estimation bias, supporting early-season classification with finer parcel boundaries.
Accurate estimation of leaf area index (LAI) is essential for evaluating forest canopy structure and ecosystem productivity. However, field measurement is difficult in areas with complex terrain and dense forest cover, resulting in limited training samples for machine-learning-based LAI inversion. In this study, a forest LAI inversion method is proposed by coupling PROSAIL and LESS radiative transfer models with a sample augmentation strategy. Firstly, field-measured LAI and multispectral reflectance were used to constrain leaf optical properties through PROSAIL-based LUT screening and Bayesian optimization. These parameters were incorporated into the LESS model to simulate forest scenes including point cloud, multispectral, and LAI data. Secondly, spectral and point cloud features were extracted, followed by correlation analysis for preliminary feature screening. Different machine learning models were evaluated for LAI inversion, and SHAP analysis was applied to select influential features. Finally, the simulated and measured datasets were integrated to construct an augmented sample set, which was used with the optimal machine learning model to retrieve the forest LAI at plot scale. The experimental results showed that: (1) relative to the model trained only on field-measured samples, the augmentation strategy based on LESS-generated virtual samples improved LAI inversion performance, with R2 increasing from 0.48 to 0.56; (2) compared with the LESS-based augmentation strategy, the PROSAIL–LESS coupling strategy further improved model performance, with R2 further increasing from 0.56 to 0.59. The proposed approach provides a flexible and cost-effective solution to the problem of sample scarcity in LAI modeling.
Terrestrial laser scanning (TLS) enables non-destructive estimation of aboveground biomass (AGB) through direct volumetric modelling and allometric model calibration. However, manual individual tree segmentation of TLS plot point clouds creates a processing bottleneck that limits scalability. We evaluated whether automated tree segmentation using RayExtract can replace manual segmentation to provide plot-level volume estimates aimed at satellite calibration/validation data. We evaluated direct volume estimates and allometric models calibrated using RayExtract tree predictions across seven TLS plots spanning various forest types, ranging from mountainous conifer to tropical rainforests. For direct volume modelling at 0.25 ha resolution, automated segmentation introduced a systematic positive bias of 20.5% (±5.5) relative to manual segmentation. Filtering using outlier detection reduced this to 6.9% (±4.3), while a filtering approach based on visual verification reduced bias to 4.2% (±2.7) on average. After visual verification, which requires only 0.7% of the active processing time compared to manual segmentation, RayExtract produced direct volume estimates with uncertainties comparable to other well-established AGB error sources across all forest types. For allometric model fitting, RayExtract-derived models substantially increased uncertainty and bias. Visual verification generally improved metrics, resulting in uncertainty and bias increases of 9 and 15 percentage points (pp) on V ∼ D models respectively, and 5 and 19 pp on V ∼ HCD models relative to manual segmentation-based models. Bias and uncertainty of automated segmentation-based models were largest in tropical forests, and targeted manual segmentation or correction of a set of representative trees likely remains necessary for local allometric model calibration.
Accurate estimation of maize residue cover (MRC) at field to regional scales is essential for evaluating conservation tillage practices and quantifying soil carbon sequestration potential. However, optical remote sensing-based MRC retrieval is strongly affected by surface moisture variability, which alters spectral responses and exacerbates the spectral confusion between maize residues and bare soil, while single-source data lack sufficient discriminatory power to resolve this complexity. To address these limitations, we propose an integrated MRC estimation framework that combines moisture-stratified correction, multi-source feature fusion, and explainable ensemble learning. First, surface moisture heterogeneity is characterized using fuzzy C-means (FCM) clustering, enabling the partitioning of the landscape into moisture-consistent zones. Within each zone, the Kubelka-Munk (KM) radiative transfer model is employed to explicitly correct moisture-induced absorption effects in Sentinel-2 reflectance, thereby restoring the intrinsic residue spectral properties. Second, Sentinel-2 reflectance, Sentinel-1 SAR backscatter, and texture features are jointly integrated to enhance residue-soil separability. Finally, a Stacking ensemble model is constructed to fuse multi-source features, and SHAP analysis is used to interpret variable contributions and reveal the mechanisms underpinning model performance. Results demonstrate that the proposed FCM + KM framework substantially improves MRC estimation accuracy across heterogeneous moisture conditions, increasing R2 from 0.46 to 0.83 and reducing RMSE from 0.14 to 0.08. The proposed framework provides a physics-informed and interpretable pathway for improving large-scale residue monitoring, particularly in regions where moisture variability constrains optical-based retrievals.
A comprehensive study has been conducted with satellite ocean color, sea surface temperature (SST), and sea surface salinity (SSS) observations, as well as ocean current and ocean nutrient data with the model simulations from 2018 to 2024, to characterize and quantify physical, optical, biological, and biogeochemical ocean properties, assess the ocean processes and understand the mechanism that drives the ocean ecosystem features in the Georges Bank (GB) region. The impact of GB on SST is less significant except in summer and SSS is not influenced by the GB seafloor topography. The enhanced chlorophyll-a (Chl-a) in the GB region is found to be significantly higher than those nearby open ocean regions and the Gulf of Maine with notable spring and autumn phytoplankton blooms. Of the major nutrients, the elevated Chl-a matches well with the higher iron (Fe) level both spatially and temporally, while the other nutrients, i.e., nitrate, phosphate, and silicate are negatively correlated. The linkage between the enhanced Chl-a and increased Fe in the GB region shows that Fe is the key nutrient driver that fuels the high biological activity over the GB region, and depletion of nitrate and phosphate hampers further increase of Chl-a during the spring bloom. Unlike most climate-driven biological activities, e.g., Biological Indian Ocean Dipole (BIOD) driven by nutrients from deeper ocean, the nutrient model shows the enhanced iron at the sea surface is caused by the mixing and re-suspension of iron-rich sediment from the GB sea floor. As a case study, this study sheds the light to address the high productivity and marine biodiversity of the ocean ecosystems in these ocean banks and helps to understand the physical, nutrient, and biological processes that drive these ocean bank ecosystems.
