arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years, combining satellite altimetry and in‑situ gauge data to enable large‑scale spatiotemporal graph imputation. The authors highlight the extreme sparsity of observations—less than 1% of sections per day—and the directed acyclic topology of river networks, which challenge existing imputation methods. They propose a bidirectional selective state‑space model that samples connected subgraphs and uses topology‑aware positional encodings, achieving 18–39% lower RMSE than the current state‑of‑the‑art SWOT‑based approach while providing predictions for all river sections.
Spectral-temporal unmixing of MODIS time series is essential for applications such as abundance mapping and spectral library construction, yet it remains challenging due to (i) spectral-temporal infor...
The paper introduces Multi-task Disentangling Fusion Mamba (MDF-Mamba), a framework for spectral-temporal unmixing of MODIS time series. It uses a disentangling architecture that pairs per-channel spectral experts with a bidirectional Mamba temporal branch, and integrates their outputs via a manifold-constrained hyper-connection (mHCFusion). The method also models endmember variability with pixel-wise learnable weights and employs a multi-task training objective combining supervised abundance estimation, self-supervised spectral reconstruction, and endmember regularization, achieving top performance on most evaluation metrics for Alberta and Saskatchewan datasets.
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years (2016‑2026) that integrates satellite altimetry, including SWOT, to enable large‑scale spatiotemporal graph imputation. The dataset is extremely sparse—fewer than 1% of sections are observed daily—and features a directed acyclic river topology that is larger and structurally distinct from existing benchmarks. The authors demonstrate that conventional imputation methods struggle with this topology, scale, and sparsity, and propose a bidirectional selective state‑space model that outperforms prior approaches, reducing RMSE against in‑situ gauges by 18‑39% and providing predictions for every river section. whyItMatters":"AmazonSWE offers a novel, real‑world use case that could improve flood forecasting and water resource management by enabling more accurate and comprehensive water surface elevation estimates across a vast, sparsely monitored river network."
arXiv:2606. 08484v1 Announce Type: cross Abstract: Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning.
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) from Sentinel‑1 and Sentinel‑2 with embeddings from the foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores for pure and mixed stands. The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access product.
arXiv:2606. 23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle.
GeoPrior-Mamba is a new multi-directional Mamba framework that reconstructs fine‑resolution XCO2 fields from sparse satellite data by incorporating structured process priors derived from a language model. These priors encode biospheric uptake, ecosystem respiration, and anthropogenic emissions into deterministic tables, which are spatially instantiated with geographic, ecological, emission, and seasonal information and injected into the model via a lightweight knowledge adapter. On OCO‑2 data from 2018‑2020, the method achieves an RMSE of 0.81 ppm and an R² of 0.93, outperforming CAMS background interpolation and Trans‑XCO2, and shows faster convergence and consistency with ground‑based TCCON measurements.
The study evaluates recurrent neural networks (RNNs) for classifying tree species from Sentinel‑2 time‑series data, focusing on distinguishing pure from mixed Norway spruce pixels. RNNs outperform Random Forest and XGBoost, achieving a Macro‑F1 of 0.851. Integrated Gradients analysis identifies key spectral‑temporal patterns—especially after day of year 125 and in band B11—that drive class separation, offering insights into phenological periods useful for mapping mixed forest structures.
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) with embeddings from foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores (0.843 for pure stands, 0.653 for mixed stands). The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access resource.
arXiv:2608. 06612v1 Announce Type: cross Abstract: The plethora of readily available geospatial data offers exciting opportunities to learn high quality representations of the planet, but the sheer size of the Earth Observations (EO), differing modalities, and different sensor types pose significant challenges in doing so.