首页|Improving leaf area index estimation accuracy of wheat by involving leaf chlorophyll content information
Improving leaf area index estimation accuracy of wheat by involving leaf chlorophyll content information
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NSTL
Elsevier
? 2022 Elsevier B.V.Red-edge band is widely used for LAI estimation as it is highly correlated to vegetation growth conditions. Canopy reflectance is affected by both vegetation biophysical and biochemical characteristics. However, estimating LAI using satellite reflectance data as input rarely considers the influence of leaf chlorophyll content (LCC). This study tested the hypothesis whether LAI estimation accuracy can be improved by involving LCC information. Firstly, the sensitivities of seven PROSAIL simulated Sentinel-2 bands to LAI and LCC were investigated, and related vegetation indices (VIs) were constructed using these sensitive bands (including LAI-sensitive VIs and LCC-sensitive VIs). Then, the LAI estimation model taking sensitive VIs as input and LCC estimation model taking sensitive VIs as input were generated by random forest regression algorithm. Finally, the improved LAI estimation model involving LCC information was proposed using three different methods: (1) PROSAIL simulated LCC, (2) simulated LCC with noise, and (3) functional equation of LCC. The results indicated that the three LCC information introducing methods all improved the LAI estimation accuracy, while using the functional equation of LCC (growth equation) performed best with RMSE of 0.736, which is 11.54% higher when compared to the basic LAI estimation model.
Leaf area indexLeaf chlorophyll contentRed-edge bandSensitivity analysisVegetation index
Jia K.、Xia M.、Yao Y.、Zhang X.、Wei X.、Liu Y.、Zhan Y.、Chen Z.
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State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University
Aerospace Information Research Institute Chinese Academy of Sciences
NSTL
Elsevier
