Estimating leaf area index of wheat at the booting stage using GF-2 data:A case study of Langfang City,Hebei Province

Leaf area index(LAI) is an important agricultural parameter to assess crop growing status for production estimation. Due to its very high spatial resolution,GF-2 can be used as a new source of remote sensing data for crop monitoring. It is particularly valuable to develop approaches for LAI estimation using GF -2 data. In this paper, the study of LAI estimation for wheat at the booting stage in Wanzhuang Town, Langfang City of Hebei Province in North China Plain is presented. Canopy LAI of wheat at the booting stage was measured over an experimental field in the town. Regression analysis method was performed with LAI and four different vegetation indexes,and the neural network method combined with the PROSAIL model was also considered. The results show that the best LAI retrieval regression model is the binomial model of normalized difference vegetation index (NDVI). The correlation coefficient (R2) and root mean square error (RMSE) are 0. 719 3 and 0. 393 6 respectively. Compared with the regression analysis method, the accuracy is greatly improved by using neural network method,and the R2and RMSE reached 0.900 8 and 0.273 2 respectively. Neural network method has feasibility and applicability based GF-2 data at wheat booting stage,which would provide a reference scheme for LAI inversion from high spatial resolution satellite image.