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Large scale maps of cropping intensity from MODIS
Josh
Gray, Boston University, joshgray@bu.edu
(Presenter)
Mark
Friedl, Boston University, friedl@bu.edu
Steve
Frolking, University of New Hampshire, steve.frolking@unh.edu
Agricultural systems are geographically extensive, have profound significance to society, and also affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, growing pressure exists to increase yields on existing agricultural lands. In tropical and sub-tropical regions, multi-cropping is widely used to increase food production, but regional-to-global information related to multi-cropping practices is poor. High temporal resolution sensors such as MODIS 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 multi-cropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multi-temporal remote sensing to map multi-cropping systems in Asia. Our analysis focused on study regions in Northern India and China that are heavily farmed and include large areas of multi-crop agriculture. 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 EVI 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 improved methods to segment, smooth, and gap-fill 8-day EVI time series calculated from MODIS BRDF corrected surface reflectances using weighted Loess smoothing. Segmentation of cropping cycles was based on changes in slope for linear regressions estimated for local windows, and were constrained by EVI amplitude and minimum crop cycle length requirements. The procedure can be used to map seasonal or long-term average cropping strategies, and to characterize changes in cropping intensity over longer time periods. We implemented a parallel processing strategy which overcame data volume and computational challenges that have prevented previous investigations of this type. Using time series of MODIS EVI images over India and China from 2000-2012, we demonstrate the utility of multi-temporal remote sensing for characterizing multi-cropping practices over two of the most important and intensely agricultural regions in the worlds. We also discuss the challenges, future improvements, and broader impacts of this work. The datasets produced using this method provide information related to global cropping systems, and more broadly, provide important information that is required to ensure sustainable management of Earth's resources and ensure food security.
Presentation Type: Poster
Session: Poster Session 1-A
(Tue 11:00 AM)
Associated Project(s):
Poster Location ID: 40
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