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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #323255

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery

Author
item Gao, Feng
item Anderson, Martha
item ZHANG, XIAOYANG - South Dakota State University
item YANG, ZHENGWEI - National Agricultural Statistical Service (NASS, USDA)
item Alfieri, Joseph
item Kustas, William - Bill
item MUELLER, RICK - National Agricultural Statistical Service (NASS, USDA)
item JOHNSON, D. - National Agricultural Statistical Service (NASS, USDA)
item Prueger, John

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/3/2016
Publication Date: 11/10/2016
Publication URL: http://handle.nal.usda.gov/10113/5583854
Citation: Gao, F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D., Prueger, J.H. 2016. Mapping crop progress at field scales using Landsat and MODIS. Remote Sensing of Environment. 188:9-25.

Interpretive Summary: Crop progress information can benefit farmers in scheduling irrigation, fertilization and harvest operation. The USDA National Agricultural Statistics Service (NASS) reports crop progress and condition supplied by local farmers weekly at the state and district levels. However, the ground data collection is time consuming, coarse and subjective. This paper presents a remote sensing approach to map crop phenology at field scales. Compared to ground observations as well as crop progress reports from NASS, crop phenology detected from remote sensing imagery captures spatial variability at field scales and are highly correlated to the crop growth stages reported by NASS. The remote sensing approach provides an effective method to map crop progress and condition which is required by the National Agricultural Statistics Service and Foreign Agricultural Service for crop yield estimation.

Technical Abstract: Crop progress and condition are required for crop management and yield estimation. In the United States, they are reported weekly at state or district level by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using the field observations provided by local farmers. The ground data collection is time consuming and subjective. Remote sensing provides a data source for observing crop progress and conditions. This paper assesses remote sensing approaches for mapping crop phenology using the fused Landsat-MODIS imagery. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our results show that the detailed spatial and temporal variability of the landscapes can be identified from the fused Landsat-MODIS data. The mean absolute difference of NDVI between actual Landsat observations and the fused Landsat-MODIS data are less than 0.05 for every year. The derived phenological metrics show distinct features for different crops and natural vegetation at field scales. Strong correlations between the remote sensing detected phenology and the reported crop growth stages from the NASS crop progress report (CPR) are observed. The greenup dates detected from remote sensing data were around V3 stages when 2-4 leaves were developed or in about 1-3 weeks after the reported emerged dates. The differences of greenup dates between corn and soybean were 8-10 days similar to the differences of the reported emerged dates between two crops at district level. The harvest dates were found in about 2-3 weeks after the dormancy stage was detected for corn and in about 1-2 weeks for soybeans. Our results suggest that crop phenology and certain growth stages at field scales (30m spatial resolution) can be mapped by integrating multiple remote sensing data. The crop growth information at field scales will benefit managers and farmers in scheduling fertilization, irrigation and harvesting operation for achieving higher yields.