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Remote Sens. 2015, 7(1), 512-529; doi:10.3390/rs70100512

MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis

1
Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA
2
Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Alfredo R. Huete and Prasad S. Thenkabail
Received: 17 September 2014 / Accepted: 23 December 2014 / Published: 6 January 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
View Full-Text   |   Download PDF [30844 KB, uploaded 6 January 2015]   |  

Abstract

Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest. View Full-Text
Keywords: bioenergy land use; MODIS; phenological mixture analysis; spatial constraint; endmember variability; the U.S. Midwest bioenergy land use; MODIS; phenological mixture analysis; spatial constraint; endmember variability; the U.S. Midwest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhong, C.; Wang, C.; Wu, C. MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis. Remote Sens. 2015, 7, 512-529.

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