Abstract: A method for improving estimation of leaf area index in an early growth stage of wheat based on red-edge bands of Sentinel-2 satellite images includes the following steps: acquiring field background spectrum and wheat canopy spectrum information by means of Sentinel-2; respectively acquiring a “straw-soil” spectrum and a “wheat-straw-soil” spectrum, and calculating slopes of red-edge areas of the “straw-soil” spectrum and the “wheat-straw-soil” spectrum; calculating a tangent function SATF corrected by a background adjustment coefficient ?; constructing a wheat LAI estimation model based on a spectral variable SATFNIR-RE2; performing preliminary screening on the LAI estimation model using cross validation; and then testing the screened model with independent data.

Abstract: A method for estimating the aboveground biomass of rice based on multi-spectral images of an unmanned aerial vehicle (UAV), including: normatively collecting UAV multi-spectral image data of rice canopy and ground measured biomass data; after collection, preprocessing images, extracting reflectivity and texture feature parameters, calculating a vegetation index, and constructing a new texture index; and by stepwise multiple regression analysis, integrating the vegetation index and the texture index to estimate rice biomass, and establishing a multivariate linear model for estimating biomass. A new estimation model is verified for accuracy by a cross-validation method. The method has high estimation accuracy and less requirements on input data, and is suitable for the whole growth period of rice. Estimating rice biomass by integrating UAV spectrum and texture information is proposed for the first time, and can be widely used for monitoring crop growth by UAV remote sensing.

Abstract: A method for estimating the aboveground biomass of rice based on multi-spectral images of an unmanned aerial vehicle (UAV), including: normatively collecting UAV multi-spectral image data of rice canopy and ground measured biomass data; after collection, preprocessing images, extracting reflectivity and texture feature parameters, calculating a vegetation index, and constructing a new texture index; and by stepwise multiple regression analysis, integrating the vegetation index and the texture index to estimate rice biomass, and establishing a multivariate linear model for estimating biomass. A new estimation model is verified for accuracy by a cross-validation method. The method has high estimation accuracy and less requirements on input data, and is suitable for the whole growth period of rice. Estimating rice biomass by integrating UAV spectrum and texture information is proposed for the first time, and can be widely used for monitoring crop growth by UAV remote sensing.