Paper Info
Title
Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data
Abstract
Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) to estimate LAI and biomass with combined OSVIs and RPPs (the product of OSVIs and RPPs (COSVI-RPPs)), (3) to use multiple stepwise regression (MSR) and partial least squares regression (PLSR) to test and compare the estimations of LAI and biomass in winter wheat, respectively. The results showed that LAI and biomass were highly correlated with several OSVIs (the enhanced vegetation index (EVI) and modified triangular vegetation index 2 (MTVI2)) and RPPs (the radar vegetation index (RVI) and double-bounce eigenvalue relative difference (DERD)). The product of MTVI2 and DERD (R-2 = 0.67 and RMSE = 0.68, p < 0.01) and that of MTVI2 and RVI (R-2 = 0. 68 and RMSE = 0.65, p < 0.01) were strongly related to LAI, and the product of the optimized soil adjusted vegetation index (OSAVI) and DERD (R-2 = 0.79 and RMSE = 148.65 g/m(2), p < 0.01) and that of EVI and RVI (R-2 = 0. 80 and RMSE = 146.33 g/m(2), p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.
Year
DOI
Venue
2015
10.3390/rs71013251
REMOTE SENSING
Keywords
Field
DocType
optical spectral vegetation indices,radar polarimetric parameters,LAI,biomass,winter wheat
Biomass,Leaf area index,Vegetation,Stepwise regression,Regression analysis,Remote sensing,Partial least squares regression,Mean squared error,Enhanced vegetation index,Geology
Journal
Volume
Issue
Citations 
7
10
9
PageRank 
References 
Authors
0.65
12
9
Name
Order
Citations
PageRank
xiuliang jin1317.50
Gui-Jun Yang214833.61
Xingang Xu3319.89
Hao Yang4287.63
Haikuan Feng56910.14
zhenhai64514.06
jiaxiao shen790.65
yubin lan83112.79
J.-C. Zhao913552.42