Paper Info
Title
Winter Wheat Leaf Area Index (LAI) Inversion Combining with HJ-1/CCD1 and GF-1/WFV1 Data.
Abstract
The LAI is the key factor which has an important influence on crop growth. LAI inversion from remote sensing is an important work in crop management. While, the accuracy of LAI inversion from remote sensing data is restricted by the limited number of observation. Multiple-sensor method has been proposed by the researchers. In this study, two sensor remote sensing data (HJ-1A/CCD1 and GF-1/WFV1) were collected in the study area. The random forest regression (RFR) was adopted in LAI inversion. The MODIS LAI product and the measured wheat LAI were used to calibrate and validate the LAI inversion model. The four spectral indices (DVI, SR, EVI, and SAVI) based on remote sensing data were calculated to develop the LAI inversion model. The accuracy of inversion of wheat LAI by remote sensing image can be improved by adding observations of angle data. Our data analysis resulted in an accuracy of R2 = 0.36, MAE = 0.467, and RMSE = 0.613 for the measured LAI. And in the validation by MODIS LAI product, an accuracy of R2 = 0.48, MAE = 1.05, and RMSE = 2.72 was found, which was a little greater than the average accuracy of mono-angle data for inversion of LAI. The result indicates that the reasonable combination of multi-sensor data can improve the accuracy of LAI estimation.
Year
Venue
Field
2016
GRMSE
Leaf area index,Agronomy,Crop growth,Crop management,Inversion (meteorology),Remote sensing,Mean squared error,Random forest,Mathematics,Calibration
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Dan Li152.14
Jie Lv200.68
Chongyang Wang300.68
Wei Liu437392.81
Jiang Hao553.16
Shuisen Chen6219.18