Record Details

Inversion of Radiative Transfer Model for Retrieval of Wheat Biophysical Parameters from Broadband Reflectance Measurements

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Inversion of Radiative Transfer Model for Retrieval of Wheat Biophysical Parameters from Broadband Reflectance Measurements
Not Available
 
Creator Vinay Kumar Sehgal, Debasish Chakraborty, Rabi Narayan Sahoo
 
Subject Remote sensing
Wheat
 
Description Not Available
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 model. Reflectance 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 function. Three inversion techniques were used and their performance was compared using different statistical parameters and target diagram. The 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 parameters. Target 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 parameters. In all the three inversion techniques, the general order of retrieval accuracy was LAI > Cab > CCC > Cw. The range of Cw was very narrow and none of the techniques could estimate variations in it. In most of the cases, the parameters were underestimated by model inversion. The best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur district. The comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56, similar to that observed in ground experimentation. The findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors.
Indian Agricultural Research Institute
 
Date 2017-01-11T08:32:02Z
2017-01-11T08:32:02Z
2016-06-01
 
Type Research Paper
 
Identifier Not Available
2214-3173
http://dx.doi.org/10.1016/j.inpa.2016.04.001
http://krishi.icar.gov.in/jspui/handle/123456789/1330
 
Language English
 
Relation Not Available;
 
Publisher Elsevier B.V.