ReviewSensors (Basel, Switzerland)2025
A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.BMC plant biology · 2026Article
- Hyperspectral Imaging-Based Evaluation of Seasonal Growth Characteristics in Turfgrass.Plants (Basel, Switzerland) · 2026Article
- Rapid and Non-Destructive Assessment of Eight Essential Amino Acids in Foxtail Millet: Development of an Efficient and Accurate Detection Model Based on Near-Infrared Hyperspectral.Foods (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R
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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.