Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection.
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
By August Posch, Jitendra Kumar, Forrest M. Hoffman, Auroop R. Ganguly
arXiv:2607. 04449v1 Announce Type: cross Abstract: Field-boundary maps support crop monitoring, irrigation planning, and yield estimation, but many smallholder parcels span only a few 10 m Sentinel-2 pixels.
By Isaac Corley, Caleb Robinson, Jennifer Marcus, Hannah Kerner
arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
By Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor
arXiv:2607. 14509v1 Announce Type: cross Abstract: This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants.
By Alper Erten, Murilo Gustineli, Adrian Cheung
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
By Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli
arXiv:2606. 25174v1 Announce Type: new Abstract: 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.
By Shubham Kumar Singh, Peilei Fan, Suraj A. Yadav, Rajendra Prasad, Prashant K Srivastava
arXiv:2607. 03663v1 Announce Type: cross Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery.
By Luiz Felipe Parente Santiago (Institute of Computing, Brazilian Army Research Institute in the Amazon), Rosiane Rodrigues de Freitas (Institute of Computing), Daniel Rodrigues dos Santos (Military Institute of Engineering), Felipe Ferrari (Military Institute of Engineering)
arXiv:2606. 29664v1 Announce Type: cross Abstract: Geospatial foundation models pretrained on satellite imagery promise broad generalization across remote sensing tasks and regions, but their geographic transferability has not been systematically tested, especially in agriculture applications.
By Zhuocheng Shang, Sanmay Das, Ahmed Eldawy
arXiv:2607. 12748v1 Announce Type: cross Abstract: Farm site discovery from satellite imagery is a spatiotemporal candidate ranking problem because farm evidence is distributed across pasture, field boundaries, roads, buildings, and seasonal vegetation patterns.
By Usman Haider, Fatima Khalid, Karl Mason
arXiv:2605. 05627v2 Announce Type: replace-cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained.
By Gabriel Jeanson, David-Alexandre Duclos, William Larriv\'ee-Hardy, No\'e Cochet, Mat\v{e}j Boxan, Anthony Desch\^enes, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere