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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 #292141

Title: Monitoring crop and vegetation condition using the fused dense time-series landsat-like imagery

item WANG, PEIJUAN - Collaborator
item Gao, Feng
item MASEK, JEFFREY - National Aeronautics And Space Administration (NASA)

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/29/2013
Publication Date: 4/18/2013
Citation: Wang, P., Gao, F.N., Masek, J. 2013. Monitoring crop and vegetation condition using the fused dense time-series landsat-like imagery [abstract]. BARC Poster Day.

Interpretive Summary:

Technical Abstract: Since the launch of the first Landsat satellite in the early 1970s, Landsat has been widely used in many applications such as land cover and land use change monitoring, crop yield estimation, forest fire detection, and global ecosystem carbon cycle studies. Medium resolution sensors like Landsat have an ideal spatial resolution for the applications at the field scales, but the numbers of available observations are limited due to a long revisiting cycle (16 days for Landsat). Cloud contaminations further exacerbate the situation. As a result, it is difficult to acquire cloud-free remote sensing imagery at both high spatial and temporal resolutions. A possible solution for the applications that require dense time-series remote sensing data at a fine spatial resolution is to fuse the imagery from multiple remote sensing sensors. We have built an operational data fusion framework based on the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) developed by Gao et al. to generate dense, time-series Landsat-like images for a cloudy region by fusing the high temporal resolution Moderate Resolution Imaging Spectroradiometer (MODIS, daily, 500m) data product and high spatial resolution Landsat imagery (16-day, 30m). Case studies are focused on monitoring natural vegetation and crop growth conditions in the cloudy areas within the Hindu Kush-Himalayan (HKH) region. Our results show that different temporal patterns at the field scales can be captured for different vegetation types in the study areas. Six areas covered by evergreen broadleaf forest (EBF), evergreen needle forest (ENF), deciduous broadleaf forest (DBF), mixed forest (MF), grasses, and crop are selected and analyzed in terms of spatial patterns and temporal characteristics. Using the fused surface reflectance, the Enhanced Vegetation Index (EVI) is calculated and used to monitor vegetation condition. A clear crop growth pattern from onset of greenness to dominant peak and then to the end of senescence is captured. In terms of forest, a general decreasing trend is shown from the end of September to March. The EVI of grasses are lower compared to forests. The operational data fusion framework provides a feasible and cost effective way to build dense time-series images at a Landsat spatial resolution for crop and natural vegetation condition monitoring in cloudy regions that will greatly benefit the USDA National Agricultural Statistics Service (NASS), Forest Service (FS) and Foreign Agricultural Service (FAS) in many of their operational applications.