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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 #340505
Title: Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

Author
item XIE, DONGHUI - Capital Normal University
item Gao, Feng
item SUN, L. - US Department Of Agriculture (USDA)
item Anderson, Martha

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/11/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different spatial, temporal and spectral characteristics have been launched, resulting in a dramatic improvement in the ability to acquire images of the Earth’s surface, these instruments typically represent a trade-off between spatial and temporal resolution. Data fusion methods have been developed to blend images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate high temporal and spatial resolution data, thereby enhancing the capability of remote sensing for monitoring crop conditions. The data fusion approach relies on using Landsat and MODIS image pairs that were acquired on the same day to estimate Landsat-scale reflectances on other MODIS dates. This study assesses the impacts of Landsat-MODIS image pair selection using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. The objectives include: 1) to evaluate the impact of pair images from different sensor combinations; 2) to assess the accuracy of data fusion results when different dates of pair images are used; 3) to assess data fusion accuracy for different land cover types; and 4) to recommend a strategy in data pair selection for achieving better data fusion results. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) for the evaluation. Our results show that the MODIS pair image with smaller view zenith angles produced better predictions. As expected, the image pair closer to the prediction date produced better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crop was overestimated during the non-growing season when using and input image pair from the growing season, while NDVI was slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies were evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produced more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved for crop condition monitoring if appropriate image pairs are used.