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Journal of Applied Remote Sensing

Rice yield estimation using Landsat ETM+ Data
Author(s): Altaf Ali Siyal; Jan Dempewolf; Inbal Becker-Reshef
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Paper Abstract

Paddy rice areas in Larkana district in Sindh province, Pakistan, were mapped over eight years. Landsat 7 ETM+ satellite imagery was classified for rice areas using training data collected through visual interpretation and using a bagged decision tree approach. Within the rice areas, we estimated yield for the 2013 season using regression models based on Landsat-derived normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) values against historic, reported yield values. The annual cropped rice area estimated from satellite imagery was between 19% and 24% lower than the area reported by the Crop Reporting Service, Sindh. A positive and strong relationship with coefficient of determination (R2) of 0.94 was observed between the reported rice crop yield and NDVI at the peak of the growing season for the years 2006 to 2013. A fair relation (R2=0.875) between rice crop yield and RVI was observed for the same years. A strong relationship between observed and predicted rice production with model efficiency=0.925, mean bias error=−85,016t, and RMSE=80,726t was obtained. Thus, Landsat ETM+ has a high potential for estimating rice yield and production at the district level in Pakistan and elsewhere.

Paper Details

Date Published: 18 November 2015
PDF: 16 pages
J. Appl. Rem. Sens. 9(1) 095986 doi: 10.1117/1.JRS.9.095986
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Altaf Ali Siyal, Sindh Agriculture Univ. (Pakistan)
Mehran Univ. of Engineering & Technology (Pakistan)
Jan Dempewolf, Univ. of Maryland, College Park (United States)
Inbal Becker-Reshef, Univ. of Maryland, College Park (United States)

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