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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #213368

Title: Watershed-Scale Crop Type Classification using Seasonal Trends in Remote Sensing-Derived Vegetation Indices

item Sudduth, Kenneth - Ken
item Sadler, Edward
item Lerch, Robert

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/2/2009
Publication Date: 10/15/2009
Citation: Jang, G.S., Sudduth, K.A., Sadler, E.J., Lerch, R.N. 2009. Watershed-Scale Crop Type Classification using Seasonal Trends in Remote Sensing-Derived Vegetation Indices. Transactions of the ASABE. 52(2):1535-1544.

Interpretive Summary: Soils of the Salt River Basin in Northeast Missouri are predominantly claypan soils that are poorly drained and have high runoff potential. The claypan soils are especially vulnerable to soil erosion, which has degraded soil and water quality throughout the basin, and to surface transport of herbicides. Because of this, the Salt River Basin was selected for the study of the effects of best management practices on water quality. Much of this research relies on computer modeling, which in turn requires information on spatial distribution of different crops. In this research, we investigated two different ways of creating crop type maps for this purpose, using remote sensing data from NASA’s Landsat satellite. One method was based on traditional image classification approaches and relied heavily on interaction of a remote sensing analyst with the image data. The second method used MODIS satellite data as an auxiliary data source in addition to the Landsat data. Because the MODIS data were available every 16 days throughout the year, it was possible to distinguish among crops on the basis of their annual growing patterns. Both methods were able to distinguish among corn, soybean, grass, and wheat with a high degree of accuracy, with the MODIS method being slightly more accurate. Because this method was also more efficient, we recommend that it be considered for projects where differentiation of crop type is needed. This research will benefit environmental scientists wishing to investigate watershed- or basin-scale processes, as it provides them with a good method to obtain a portion of the input data needed for their environmental analysis models.

Technical Abstract: Analysis and simulation of watershed-scale processes requires spatial characterization of land use, including differentiation among crop types. If this crop type information could be obtained accurately from remote sensing data, the effort required would be significantly reduced, especially for large watersheds. The objective of this study was to compare two methods using multiple satellite remote sensing datasets to differentiate land cover, including crop type, for the Salt River/Mark Twain Lake basin in northeast Missouri. Method 1 involved unsupervised classification of Landsat visible and near-infrared satellite images obtained at multiple dates in the growing season, followed by traditional, manual class identification. Method 2 employed the same unsupervised classification, but also used normalized difference vegetation index (NDVI) maps obtained on a 16-day cycle from MODIS satellite images as ancillary data to derive seasonal NDVI trends for each class in the classification map. Tree analysis was applied to the NDVI trend data to group similar classes into clusters, and land cover for each cluster was determined from ground truth data. Additional ground truth data was used to assess the accuracy of the procedure, and crop acreage estimates were compared to county-level statistics. Overall, Method 2 provided somewhat higher levels of classification accuracy. It also was more efficient in terms of analyst time and ground truth data requirements. Therefore, this method employing variations in seasonal NDVI trends is suggested for differentiation of crop type. Maps developed using this process will be useful as input data to environmental analysis models.