Mapping Asian Cropping Intensity With MODIS

Gray, J and Friedl, M and Frolking, S and Ramankutty, N and Nelson, A and Gumma, M K (2014) Mapping Asian Cropping Intensity With MODIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 07 (08). pp. 3373-3379. ISSN 1939-1404

PDF - Published Version Restricted to ICRISAT users only Download (1MB) | Request a copy

Abstract

Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.

Item Type:	Article
Divisions:	RP-Resilient Dryland Systems
CRP:	CGIAR Research Program on Dryland Systems
Uncontrolled Keywords:	Cropping Intensity, Remote Sensing, Agricultural systems, Time series, Mapping, Agriculture
Subjects:	Others > Agriculture-Farming, Production, Technology, Economics
Depositing User:	Mr Ramesh K
Date Deposited:	14 Dec 2015 09:18
Last Modified:	14 Dec 2015 09:18
URI:	http://oar.icrisat.org/id/eprint/9185
Official URL:	http://dx.doi.org/10.1109/JSTARS.2014.2344630
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	This work was supported in part by NASA under Grant NNX11AE75 G and in part by NSF under Grant EAR-1038907 and Grant EF-1064614.
Links:	Google Scholar

View Statistics

Actions (login required)

View Item