Record Details

Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

OAR@ICRISAT

View Archive Info
 
 
Field Value
 
Relation http://oar.icrisat.org/11765/
https://doi.org/10.1007/s12524-021-01341-6
doi:10.1007/s12524-021-01341-6
 
Title Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India
 
Creator Gumma, M K
Kadiyala, M D M
Panjala, P
Ray, S S
Akuraju, V R
Dubey, S
Smith, A P
Das, R
Whitbread, A M
 
Subject GIS Techniques/Remote Sensing
 
Description Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing,
storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic
stresses. In this study, we used a suite of methods to assess yields at the village level (\5 km2) using remote sensing
technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were
generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We
derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in
yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques
(SMTs) and crop management information collected from field surveys along with ground data. The locations of crop
cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted
vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop
simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation
approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of
remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R2 is
greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models,
crop yields at harvest could be successfully predicted.
 
Publisher Springer
 
Date 2021-03
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11765/1/Gumma2021_Article_AssimilationOfRemoteSensingDat.pdf
Gumma, M K and Kadiyala, M D M and Panjala, P and Ray, S S and Akuraju, V R and Dubey, S and Smith, A P and Das, R and Whitbread, A M (2021) Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India. Journal of the Indian Society of Remote Sensing (TSI). ISSN 0255-660X