Remote Sensing in Agriculture

Latest papers

• AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations

  Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar, Dharmendra Saraswat · 24 September 2026

  Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel level supervision. This mismatch has limited the development of large-scale benchmarks for crop yield learning using mul…

• Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

  Jordi Cerd\`a-Bautista, Vasileios Sitokonstantinou, Homer Durand, Gherardo Varando, Michele Ronco, Gustau Camps-Valls · 22 September 2026

  Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Ni\~no, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by …

• A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products

  \'Etienne Clabaut, Samuel Foucher, Yacine Bouroubi · 17 September 2026

  High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. In Quebec, these limitations…

• From Foundation Embeddings to Cropland Maps: Label Efficiency, Temporal Transferability and Independent Human Validation

  Mohammad Ammar Mughees, Giovanni Montefoschi, Zhongxin Chen, Maria Antonia Brovelli · 16 September 2026

  Geospatial foundation models provide reusable representations of satellite imagery that support downstream mapping with limited task-specific modelling. We evaluate whether annual AlphaEarth embeddings support binary cultivated-versus-non-cultivated mapping in Maine, USA, using 192 spatially separat…

• SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning

  Rajiv Ranjan, Udaiveer Singh, Shashank Tamaskar, Dharmendra Saraswat · 16 September 2026

  Earth observation is inherently dynamic, yet temporal information in many foundation models is learned through reconstruction, invariance, or retrospective sequence summarization. SPEAR NeXT is introduced as a compact pixel-wise multimodal spectral temporal foundation model in which temporal self su…

• Forward-Facing Near-Infrared Adds Little to Colour for Farm-Machinery Traversability: A Site-Disjoint Evaluation of Sensor-Dependent Spatial Leakage

  Sungwoo Kang · 15 September 2026

  Near-infrared (NIR) imaging does not consistently outperform standard color cameras for daytime agricultural traversability once spatial data leakage is eliminated. Prior benchmarks suggesting an NIR advantage used sequence-level splits that permitted spatially autocorrelated imagery into test sets,…

• Monitoring Pasture Restoration from Satellite Image Time Series: Caveats and Opportunities

  Linnea Sartorius, Isak Randahl, Delia Fano Yela, Georg Andersson, Sadegh Jamali, Aleksis Pirinen · 19 August 2026

  Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (SITS) have been widely used for land surface monitoring. In semi-natural grasslands - the habitat type in focus in this work - restoration outcomes d…

• Turning spectra into images improves plant trait retrieval with 2D-CNNs

  Javier Lopatin, Teja Kattenborn, Eya Cherif, Sebastián Moreno · 18 August 2026

  Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies. We asked whether transforming 1D spectra into 2D …

• Learning to Forecast Crop Growth from Earth Observation Data

  Dominik Senti, Mehmet Ozgur Turkoglu, Michele Volpi, Helge Aasen · 17 August 2026

  Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we investigate whether Earth observation time series and meteorological drivers can be used to predict future canopy developmen…

• A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissau

  Miguel, Sofia, Maria, Patr\'icia, Luke, Jo\~ao · 13 August 2026

  Cashew production is a widespread economic activity in Guinea-Bissau, as well as other countries in West Africa. However, unregulated cashew production can be directly associated with increasing regionwide deforestation rates, biodiversity losses, and a fragile economic structure. There is no nation…

• Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh

  Muhammad Masud Tarek, Md. Alamgir Hossain, Md. Samiul Islam, Muntasir Hasan Kanchan · 13 August 2026

  Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability. This study employs remote sensing data and machine learning techniques to an…

• SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

  Moti Rattan Gupta, Anupam Sobti · 12 August 2026

  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 moni…

• SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping

  Thomas Lauber, Mehmet Ozgur Turkoglu, Sélène Ledain, Helge Aasen · 11 August 2026

  Operational crop mapping requires models that generalise across years, resolve fine-grained crop taxonomies, and distinguish cropland from surrounding landscapes. However, existing crop mapping datasets enable evaluation of these requirements only in isolation. We therefore introduce SwissCrop25, a …

• Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

  Vaishali Swaminathan, Nithya Rajan, J Alex Thomasson, Amrit Shrestha, Karem Meza Capcha, Robert Hardin, Pramod Pokhrel · 11 August 2026

  Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield. This study developed remote sensing and machine learning-based me…

• Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality

  Syed Roshaan Ali Shah, Kasper Bonte, David Bekaert, Kristof Van Tricht, Dieter Wens · 7 August 2026

  Machine learning, and deep networks in particular, are increasingly used to derive higher-level Earth observation (EO) products such as annual land-cover and crop-type maps. Many are generated operationally: each year a new acquisition is processed, typically with the same model, extending a multi-y…

• Above-ground Biomass Estimation with Geospatial Foundation Models

  Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler · 6 August 2026

  Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale. Geospatial Foundation Models (GFMs) have recently emerged as a promising machine learning paradigm to d…

• TRNet: Topography-Guided Frequency Rectification and Structure-Aware Decoding for Multimodal Paddy Rice Segmentation

  Kaiwen Xiao, Chunlong Fu, Liping Zheng, Yanfeng Su · 6 August 2026

  Mapping paddy rice from very-high-resolution imagery in mountainous and hilly regions is difficult because terrain alters optical appearance and increases confusion with visually similar vegetation. We present TRNet for 0.5-m GaoJing-1 red--green--blue (RGB) imagery, a 5-m TanDEM-X digital elevation…

• Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction

  Ang Li, Menghui Jiang, Xiaobin Guan, Dong Chu, Huanfeng Shen · 4 August 2026

  Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaini…

• A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States

  Zhixing Ruan, Lixin Lu · 4 August 2026

  Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides …

• PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series

  Xuechen Li · 4 August 2026

  Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series. Existing approaches typically rely on dense parcel-level annotations and task-specific model training, which limits their applicability to new re…

• Space2Ground 2.0: A Multi-Source Dataset and Framework for Agricultural Monitoring through Fusion of Street-Level and Satellite Imagery

  Iason Tsardanidis, Alkiviadis Koukos, George Choumos, Vasileios Sitokonstantinou, Charalampos Kontoes · 31 July 2026

  Accurate and scalable parcel-level agricultural monitoring remains challenging because satellite Earth Observation alone provides only an overhead perspective of agricultural parcels, while optical observations are further affected by cloud-induced temporal gaps. This paper presents Space2Ground 2.0…

• Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification

  Dristi Datta, Md Khalid Hasan Sakib, Manoranjan Paul · 28 July 2026

  Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes. Existing studies often rely on standalone tree ensembles or generic neural networks, although fine-grained ecological clas…

• Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

  Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor · 27 July 2026

  Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis. This study presents a reproducible workflow for mapping farmland extent and visible boundaries from 1 m NAIP RGB im…

• Delineate Anything v2: A Global Foundation Model for Field Delineation

  Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Yevhenii Salii, Volodymyr Kuzin, Charlotte Julia Li-Xing Wang, Zoltan Szantoi · 22 July 2026

  Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological co…

• STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series

  Pradeep Dalal, Rajiv Ranjan, Sushil Ghildiyal, Shashank Tamaskar, Neeraj Goel · 22 July 2026

  Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), bui…