Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground”
Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spe...
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2025-02-01
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| author | Wu Nile Su Rina Na Mula Cha Ersi Yulong Bao Jiquan Zhang Zhijun Tong Xingpeng Liu Chunli Zhao |
| author_facet | Wu Nile Su Rina Na Mula Cha Ersi Yulong Bao Jiquan Zhang Zhijun Tong Xingpeng Liu Chunli Zhao |
| author_sort | Wu Nile |
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| description | Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R<sup>2</sup> = 0.962 and an RMSE = 5.590 mg/m<sup>2</sup> in the training set, and an R<sup>2</sup> = 0.582 and an RMSE = 6.019 mg/m<sup>2</sup> in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R<sup>2</sup> = 0.923 and an RMSE = 8.097 mg/m<sup>2</sup>, while the test set showed an R<sup>2</sup> = 0.837 and an RMSE = 3.250 mg/m<sup>2</sup>, which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R<sup>2</sup> = 0.76, test R<sup>2</sup> = 0.88, RMSE = 18.83 mg/m<sup>2</sup>). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture. |
| format | Article |
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| spelling | doaj-art-9d31a155234d4143a29ccf535cf1e52e2025-08-20T02:01:24ZengMDPI AGRemote Sensing2072-42922025-02-0117457210.3390/rs17040572Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground”Wu Nile0Su Rina1Na Mula2Cha Ersi3Yulong Bao4Jiquan Zhang5Zhijun Tong6Xingpeng Liu7Chunli Zhao8School of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaCollege of Geographical Sciences, Inner Mongolia Normal University, Hohhot 010022, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaCollege of Forestry and Grassland, Jilin Agricultural University, Changchun 130024, ChinaLeaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R<sup>2</sup> = 0.962 and an RMSE = 5.590 mg/m<sup>2</sup> in the training set, and an R<sup>2</sup> = 0.582 and an RMSE = 6.019 mg/m<sup>2</sup> in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R<sup>2</sup> = 0.923 and an RMSE = 8.097 mg/m<sup>2</sup>, while the test set showed an R<sup>2</sup> = 0.837 and an RMSE = 3.250 mg/m<sup>2</sup>, which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R<sup>2</sup> = 0.76, test R<sup>2</sup> = 0.88, RMSE = 18.83 mg/m<sup>2</sup>). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture.https://www.mdpi.com/2072-4292/17/4/572UAV hyperspectral remote sensingsimulated Sentinel-2A spectramachine learningleaf chlorophyll contentdifferent growth periods |
| spellingShingle | Wu Nile Su Rina Na Mula Cha Ersi Yulong Bao Jiquan Zhang Zhijun Tong Xingpeng Liu Chunli Zhao Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” Remote Sensing UAV hyperspectral remote sensing simulated Sentinel-2A spectra machine learning leaf chlorophyll content different growth periods |
| title | Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” |
| title_full | Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” |
| title_fullStr | Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” |
| title_full_unstemmed | Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” |
| title_short | Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” |
| title_sort | inversion of leaf chlorophyll content in different growth periods of maize based on multi source data from sky space ground |
| topic | UAV hyperspectral remote sensing simulated Sentinel-2A spectra machine learning leaf chlorophyll content different growth periods |
| url | https://www.mdpi.com/2072-4292/17/4/572 |
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