A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives
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...
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2025-04-01
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| author | Xuan Li Bingxue Zhu Sijia Li Lushi Liu Kaishan Song Jiping Liu |
| author_facet | Xuan Li Bingxue Zhu Sijia Li Lushi Liu Kaishan Song Jiping Liu |
| author_sort | Xuan Li |
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| description | 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<sup>2</sup>) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R<sup>2</sup> = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities. |
| format | Article |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-5fa050ba6b6e4b1199bed38ee72d9d862025-08-20T02:25:02ZengMDPI AGSensors1424-82202025-04-01258234510.3390/s25082345A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future PerspectivesXuan Li0Bingxue Zhu1Sijia Li2Lushi Liu3Kaishan Song4Jiping Liu5State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Tourism and Geography, Jilin Normal University, Siping 136000, ChinaChlorophyll 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<sup>2</sup>) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R<sup>2</sup> = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities.https://www.mdpi.com/1424-8220/25/8/2345chlorophyll contenthyperspectral imagingmachine learningradiative transfer modelsprecision agriculture |
| spellingShingle | Xuan Li Bingxue Zhu Sijia Li Lushi Liu Kaishan Song Jiping Liu A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives Sensors chlorophyll content hyperspectral imaging machine learning radiative transfer models precision agriculture |
| title | A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives |
| title_full | A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives |
| title_fullStr | A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives |
| title_full_unstemmed | A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives |
| title_short | A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives |
| title_sort | comprehensive review of crop chlorophyll mapping using remote sensing approaches achievements limitations and future perspectives |
| topic | chlorophyll content hyperspectral imaging machine learning radiative transfer models precision agriculture |
| url | https://www.mdpi.com/1424-8220/25/8/2345 |
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