ReviewNational science review2023
Challenges and opportunities in remote sensing-based crop monitoring: a review.
Review in National science review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Multi-scale spatial-temporal remote sensing fusion for phenology identification in rice germplasm resources.Plant phenomics (Washington, D.C.) · 2026Article
- Potential and challenges of Positron Emission Tomography beyond conventional preclinical and clinical imaging.Zeitschrift fur medizinische Physik · 2026Review
- Article
- Multi-scale remote sensing monitoring of aboveground vegetation carbon storage in long-distance expressways.Carbon balance and management · 2026Article
- A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (2013-2024) Based on Harmonized Landsat-Sentinel-2 Data.Scientific data · 2026Article
- Wheat yield prediction using integrated optical and radar remote sensing with machine learning across key phenological stages.Scientific reports · 2026Article
- Evaluating Multi-Temporal Sentinel-1 and Sentinel-2 Imagery for Crop Classification: A Case Study in a Paddy Rice Growing Region of China.Sensors (Basel, Switzerland) · 2026Article
- Predicting Multiple Traits of Rice and Cotton Across Varieties and Regions Using Multi-Source Data and a Meta-Hybrid Regression Ensemble.Sensors (Basel, Switzerland) · 2026Article
- Global determinants of yield variability under sustainable farming approaches across climate, soil, and topography. A meta-analysis.Agronomy for sustainable development · 2026Article
- NSII: a novel soybean identification index based on Sentinel-2 imagery for accurate and efficient soybean mapping.Frontiers in plant science · 2026Article
- Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery.Scientific reports · 2025Article
- Predicting human mobility flows in cities using deep learning on satellite imagery.Nature communications · 2025Article
- Spatial distribution dataset of 30-m resolution cropland in agricultural regions, Qinghai-Tibet Plateau.Scientific data · 2025Article
- Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges.Plants (Basel, Switzerland) · 2025Review
- Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback.Plants (Basel, Switzerland) · 2025Article
- Maize yield estimation in Northeast China's black soil region using a deep learning model with attention mechanism and remote sensing.Scientific reports · 2025Article
- Time Series Remote Sensing Image Classification with a Data-Driven Active Deep Learning Approach.Sensors (Basel, Switzerland) · 2025Article
- Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.Scientific data · 2025Article
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.