Modeling yield and backscatter using satellite derived biophysical variables of rice crop based on Artificial Neural Networks

Authors

  • MAHESH PALAKURU School of Civil Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu
  • SIRISHA ADAMALA Natural Resources Management Division, Central Island Agricultural Research Institute (CIARI), Port Blair, Andaman & Nicobar Islands
  • HARISH BABU BACHINA Department of Applied Engineering, Vignan’s Foundation for Science, Technology and Research (VFSTR) University, Vadlamudi

DOI:

https://doi.org/10.54386/jam.v22i1.120

Keywords:

Rice yield, backscatter, artificial neural network, model, MLR

Abstract

In this study, ‘observed rice yield (ton acre-1)’ and ‘remotely sensed backscatter’are modelled using artificial neural network (ANN) and multiple linear regression (MLR) methods for East and West Godavari districts of Andhra Pradesh in India. The biophysical variables viz. backscatter (bs), normalized difference vegetation index (NDVI), Chlorophyll (chfl), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), canopy water content (CWC), and fraction of vegetation cover (Fcover) were derived from Scatterometer Satellite-1 (SCATSAT-1), Moderate Imaging Spectrometer (MODIS) and Sentinel-2 satellite data.Inputs selected are bs, NDVI, chfl, FAPAR, LAI, CWC, and Fcover for rice yield model, whereas NDVI, chfl, FAPAR, LAI, CWC, and Fcover are inputs for backscatter models. The performance of ANN and MLR models was evaluated using three indices such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results concluded that the ANN models achieved R2 of 0.908 and 0.884 which are 42.73% and 28.85% higher than that of the MLR method for rice yield and backscatter, respectively.

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Published

01-03-2020

How to Cite

MAHESH PALAKURU, SIRISHA ADAMALA, & HARISH BABU BACHINA. (2020). Modeling yield and backscatter using satellite derived biophysical variables of rice crop based on Artificial Neural Networks. Journal of Agrometeorology, 22(1), 41–47. https://doi.org/10.54386/jam.v22i1.120

Issue

Vol. 22 No. 1 (2020): March

Section

Research Paper

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