Estimating the Biomass of Maize with Hyperspectral and LiDAR Data
AbstractThe accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (
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Wang, C.; Nie, S.; Xi, X.; Luo, S.; Sun, X. Estimating the Biomass of Maize with Hyperspectral and LiDAR Data. Remote Sens. 2017, 9, 11.
Wang C, Nie S, Xi X, Luo S, Sun X. Estimating the Biomass of Maize with Hyperspectral and LiDAR Data. Remote Sensing. 2017; 9(1):11.Chicago/Turabian Style
Wang, Cheng; Nie, Sheng; Xi, Xiaohuan; Luo, Shezhou; Sun, Xiaofeng. 2017. "Estimating the Biomass of Maize with Hyperspectral and LiDAR Data." Remote Sens. 9, no. 1: 11.
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