Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing

Gumma, M K and Thenkabail, P S and Teluguntla, P and Whitbread, A M (2018) Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing. In: Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment . Springer, pp. 425-448. ISBN 978-3-319-78710-7

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Abstract

Cereals and grain legumes are the most important part of human diet and nutrition. The expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice fallows are best niche areas with residual moisture to grow short-duration legumes, thereby achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season ( kharif ) rice cultivation or post-rainy ( rabi ) fallows in rice-growing environments between 2005 and 2015 using temporal moderate-resolution imaging spectroradiometer (MODIS) data applying spectral matching techniques. This study was conducted in South Asia where different rice ecosystems exist. MODIS 16 day normalized difference vegetation index (NDVI) at 250 m spatial resolution and season-wise-intensive ground survey data were used to map rice systems and the fallows thereafter ( rabi fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available subnational-level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79%, respectively, with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the subnational statistics with R ^ 2 values of 94% at the district level for the years 2005–2006 and 2015–2016. Results clearly show that rice fallow areas increased from 2005 to 2015. The results show spatial distribution of rice fallows in South Asia, which are identified as target domains for sustainable intensification of short-duration grain legumes, fixing the soil nitrogen and increasing incomes of small-holder farmers.

Item Type:	Book Section
Divisions:	Research Program : Innovation Systems for the Drylands (ISD)
CRP:	CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC) CGIAR Research Program on Water, Land and Ecosystems (WLE)
Series Name:	Geotechnologies and the Environment
Uncontrolled Keywords:	Grain legumes, Ground survey data, MODIS 250, NDVI, Potential areas, Seasonal rice mapping, Rice fallows, Spectral matching techniques, Moderate-resolution imaging spectroradiometer, South Asia, Spatial Distribution of Croplands
Subjects:	Others > Agriculture Others > GIS Techniques/Remote Sensing Others > Rice Others > South Asia
Depositing User:	Mr Ramesh K
Date Deposited:	20 Sep 2018 06:08
Last Modified:	20 Sep 2018 06:08
URI:	http://oar.icrisat.org/id/eprint/10867
Acknowledgement:	This research was supported by two CGIAR Research Programs: Grain Legumes and Dryland Cereals (GLDC) and (Water Land and Ecosystems (WLE)). The research was also supported by the global food security support analysis data at 30 m project (GFSAD30; http://geography.wr.usgs.gov/science/croplands/; https://croplands.org/) funded by the NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments) funding obtained through NASA ROSES solicitation as well as by the Land Change Science (LCS), Land Remote Sensing (LRS), and Climate Land Use Change Mission Area Programs of the US Geological Survey (USGS). The authors would like to thank to the International Rice Research Institute (IRRI) for providing ground survey data and district-wise national statistics; Dr. Dheeravath Venkateshwarlu, Dr. Andrew Nelson, and Dr. Mitch Scull for supporting ground surveys in India; Dr. Saidul Islam for Bangladesh ground survey data and Dr. Nimal Desanayake for Sri Lanka ground survey data. Also thanks to Ms. Deepika Uppala for supporting data analysis.
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