Soil Moisture and Remote Sensing

Últimos artículos

• Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference

  Yi Yu, Jian Peng, Yucheng Lin, Trevor F. Keenan, Thomas F. A. Bishop · 18 de agosto de 2026

  Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process modelling within ecohydrology, which integrate EO, meteorological forcing and process models to characterise coupled water, energy and carbon dynamics in v…

• An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series

  Shubham Kumar Singh, Peilei Fan, Suraj A. Yadav, Rajendra Prasad, Prashant K Srivastava · 25 de junio de 2026

  Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem. Concurrent variations in soil moisture and canopy density generate substantial ambiguities in radar backscatter and s…

• Deep Learning for Soil Moisture Estimation: Fusing Satellite Data with Optimally-Lagged Meteorological Features

  Adrian Canovas-Rodriguez, Aurora Gonz\'alez Vidal, Antonio F. Skarmeta · 23 de junio de 2026

  Accurate soil moisture estimation in semi-arid agricultural regions requires integrating remote sensing and meteorological information while accounting for the delayed response of soil moisture to atmospheric forcing. This study introduces a Cross-Correlation Function (CCF) methodology to determine …

• A Survey on Data-Driven Models for Soil Moisture Regression and Classification

  Ilektra Tsimpidi, George Georgoulas, Vidya Sumathy, George Nikolakopoulos · 18 de junio de 2026

  Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations. Physics-based approaches, such as water balance models, rely on explicit hydrological equations and h…

• SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning

  Kang Yang, Yuanlin Yang, Yuning Chen, Sikai Yang, Xinyu Zhang, Wan Du · 19 de marzo de 2026

  Precision agriculture demands continuous and accurate monitoring of soil moisture (M) and key macronutrients, including nitrogen (N), phosphorus (P), and potassium (K), to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, curre…

• RaUF: Learning the Spatial Uncertainty Field of Radar

  Shengpeng Wang, Kuangyu Wang, Wei Wang · 3 de marzo de 2026

  Millimeter-wave radar offers unique advantages in adverse weather but suffers from low spatial fidelity, severe azimuth ambiguity, and clutter-induced spurious returns. Existing methods mainly focus on improving spatial perception effectiveness via coarse-to-fine cross-modal supervision, yet often o…

• Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation

  Ioannis Kontogiorgakis, Athanasios Askitopoulos, Iason Tsardanidis, Dimitrios Bormpoudakis, Ilias Tsoumas, Fotios Balampanis, Charalampos Kontoes · 23 de febrero de 2026

  Accurate soil moisture (SM) estimation is critical for precision agriculture, water resources management and climate monitoring. Yet, existing satellite SM products are too coarse (>1km) for farm-level applications. We present a high-resolution (10m) SM estimation framework for vegetated areas acros…

• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification

  Zhihan Zeng, Yang Zhao, Kaihe Wang, Dusit Niyato, Hongyuan Shu, Junchu Zhao, Yanjun Huang, Yue Xiu, Zhongpei Zhang, Ning Wei · 20 de enero de 2026

  As the electromagnetic environment becomes increasingly complex, Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference. Although Deep Learning (DL) effectively identifies basic interference, classifying compound interference remains difficult due to …

• SoilX: Calibration-Free Comprehensive Soil Sensing Through Contrastive Cross-Component Learning

  Kang Yang, Yuanlin Yang, Yuning Chen, Sikai Yang, Xinyu Zhang, Wan Du · 10 de noviembre de 2025

  Precision agriculture demands continuous and accurate monitoring of soil moisture (M) and key macronutrients, including nitrogen (N), phosphorus (P), and potassium (K), to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, curre…