Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons |
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| Institution: | 1. Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA;2. Institute of Biodiversity Sciences, Fudan University, Shanghai, 200433, China;3. Atmospheric Science Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;4. Atmospheric Turbulence and Diffusion Division, NOAA/ARL, Oak Ridge, TN 37831, USA;5. School of Natural Resource, University of Nebraska, Lincoln, Lincoln, NE 68583, USA;6. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;7. International Center for Agricultural Research in Dry Areas, the Consultative Group on International Agricultural Research (CGIAR), Amman, 11195, Jordan;8. College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA;1. Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0973, USA;2. Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0817, USA;3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation. |
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| Keywords: | LAI HJ-1CCD image Remote sensing feature Winter wheat Partial least squares regression |
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