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Remote Sens. 2017, 9(12), 1232;

High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data

Guangdong Research Center of Smart Homeland Engineering, South China Normal University, Guangzhou 510631, China
State Key Laboratory of Earth Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Authors to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial distribution of cropping intensities that allows for monitoring of the multiple cropping activities over large areas. Although efforts have been made to map cropping cycles at 500 m or coarser resolution, producing cropping cycle maps at high resolution remain challenging because data from single satellite sensor do not provide sufficient spatiotemporal observations. In this paper, we generate dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derive the cropping cycles from the fused time series data. The method achieves overall accuracies of 92.5% and 89.2%, respectively, for two typical regions of multiple cropping in China using samples identified based on satellite time series data, and an overall accuracy of 81.2% for four subregions using all samples identified based on multi-temporal high resolution images. The mapped crop cycles show to be reasonable geographically and agree with the national census data. The fusion approach provides a feasible way to map cropping cycles at 30 m resolution and enables improved depiction of the spatial distribution of multiple cropping. View Full-Text
Keywords: time series; data fusion; land cover mapping; multiple cropping; cropping intensity time series; data fusion; land cover mapping; multiple cropping; cropping intensity

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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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Li, L.; Zhao, Y.; Fu, Y.; Pan, Y.; Yu, L.; Xin, Q. High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data. Remote Sens. 2017, 9, 1232.

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