Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

作     者：Vinay Kumar Sehgal Debasish Chakraborty Rabi Narayan Sahoo 

作者机构：Division of Agricultural PhysicsIndian Agricultural Research InstituteNew Delhi 110012India 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2016年第3卷第2期

页      面：107-118页

核心收录：

学科分类：09[农学] 0901[农学-作物学] 

基　　金：This study was financially supported by In-house project grant code IARI:PHY:09:04(3)of Indian Agricultural Research Institute India.Second author acknowledges the fellowship provided by the Indian Council of Agricultural Research(ICAR)to undertake Master’s degree programme.Authors acknowledge the research facilities extended by Head Division of Agricultural Physics Indian Agricultural Research Institute New Delhi 

主　　题：PROSAIL Look up table Neural network Leaf area index Chlorophyll content Target diagram IRS LISS-3 

摘      要：This study describes the retrieval of wheat biophysical variables of leaf chlorophyll(Cab),leaf area index(LAI),canopy chlorophyll(CCC),and leaf wetness(Cw)from broadband reflectance data corresponding to IRS LISS-3(Linear Imaging Self Scanner)sensor by inversion of PROSAIL5B canopy radiative transfer *** data of wheat crop,grown under different treatments,were measured by hand-held spectroradiometer and later integrated to LISS-3 reflectance using its band-wise relative spectral response *** inversion techniques were used and their performance was compared using different statistical parameters and target *** inversion techniques tried were:a look up table with best solution(LUT-I),a look up table with mean of best 10%solutions(LUT-II)and an artificial neural network(ANN).All the techniques could estimate the biophysical variables by capturing variability in their observed values,though accuracy of estimation varied among the three *** diagram clearly depicted the superiority of LUT-II over the other two approaches indicating that a mean of best 10%solutions is a better strategy while ANN was worst performer showing highest bias for all the *** all the three inversion techniques,the general order of retrieval accuracy was LAICabCCC*** range of Cw was very narrow and none of the techniques could estimate variations in *** most of the cases,the parameters were underestimated by model *** best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur *** comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56,similar to that observed in ground *** findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors.