Change detection (CD) with hyperspectral images (HSIs) can be effectively performed using deep learning networks (DLNs) by taking advantage of HSIs for their abundant spectral and spatial information and the excellent performance of DLN in machine learning. By modeling the temporal dependence of multiscale representative features, more discriminative information reflecting land use and land cover (LULC) changes can be obtained by suppressing less correlated information while improving the robustness of pseudo-changes caused by imaging noises. However, preserving time-dependent multiscale representative features while extracting spatial–spectral features based on conventional DLN is difficult, mainly due to the structural limitation of conventional DLN. A multipath convolutional long short-term memory (LSTM) multipath convolutional long short-term memory neural network (MP-ConvLSTM) taking advantage of LSTM and convolutional neural network (CNN) through the designed parallel architecture to learn multilevel temporal dependencies of bitemporal HSIs, therefore, was proposed for extracting multiscale temporal–spatial–spectral features by combining hidden states from different paths of ConvLSTM in the present study. In the proposed MP-ConvLSTM, the efficient channel attention (ECA) module was introduced to refine features of different paths, and Siamese CNN was adopted to reduce HSIs’ dimensionality and extract preliminary features to build up an end-to-end trainable model for CD with HSIs. The validity of the MP-ConvLSTM was evaluated using the binary and multiclass CD datasets. The CD accuracy of the proposed MP-ConvLSTM was visually and statistically evaluated by different criteria and compared with those derived from several state-of-the-art (SOTA) CD algorithms. The experiments demonstrated that our proposed model not only outperformed those SOTA CD models but also exhibited better tradeoff between complexity and accuracy in general.

High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive sensors. However, the ESA climate change initiative (CCI) SM product, which combines data from multiple sensors, sacrifices spatial-temporal resolution and coverage due to satellite orbit constraints and retrieval algorithms. To address this issue, an SM reconstruction approach based on a conditional variational autoencoder model was developed, leveraging the high spatial resolution of SMAP L4 data and the accuracy of CCI fused products across different land cover types. This method resulted in the creation of a global three-day SM product at 0.0625 $^{\circ }$ spanning from 2015 to 2021. The reconstructed SM product underwent rigorous validation against global core SM sites and sparse observation networks. The evaluation employed multiple metrics, including the global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The validation yielded results, with ubRMSE values of approximately 0.029 and 0.071 m $^{3}$ /m $^{3}$ , and CC values of around 0.863 and 0.743 for core SM sites and sparse observation networks. This reconstructed product offers global coverage and enhanced accuracy compared to existing benchmarks.

The NASA–ISRO Synthetic Aperture Radar (NISAR) mission provides L-band soil moisture at 200 m, but validating its early products depends on retrieval flags, overpass collocation and sensor depth. We evaluated the beta Level-3 product against seven U.S. in situ networks (October 2025–January 2026) and the provisional product at 21 National Ecological Observatory Network sites (June–July 2026). Beta retrievals with flag 0, which averages all three candidate algorithms, were matched to in situ data within 60 min, merged by orbit and averaged over 0–10 cm sensors, yielding 261 site overpasses from 117 sites. Beta NISAR had a preliminary bias of 0.003 m³ m⁻³, a root mean square error (RMSE) of 0.081 m³ m⁻³ (95% interval 0.066–0.098) and $R=0.578$. Accepting all flag classes increased coverage ninefold and RMSE to 0.096 m³ m⁻³. On common site overpasses, deeper sensors did not have significantly higher RMSE than 5 cm sensors. The exploratory provisional assessment found a wet bias of 0.110 m³ m⁻³, concentrated at very dry reference sites, with a pooled R of −0.038 but an anomaly R within sites of 0.259. These estimates rest on few sites and short records; validation of early NISAR products should report flag classes, overpass construction and matched depths and separate pooled from temporal agreement.