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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 #292654
Title: Integrating remote sensing data from multiple optical sensors for ecological and crop condition monitoring

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

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/15/2013
Publication Date: 9/24/2013
Citation: Gao, F.N., Wang, P., Masek, J. 2013. Integrating remote sensing data from multiple optical sensors for ecological and crop condition monitoring [abstract]. SPIE Optical Engineering and Applications. DOI: 10.1117/12.2023417.

Interpretive Summary:

Technical Abstract: Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. Due to technical limitations and budget constraints, remote sensing instruments trade spatial resolution for swath width. As a result, it is difficult to acquire remotely sensed data with both high spatial resolution and frequent coverage. A synthesized approach fusing multiple types of remote sensing imagery provides a feasible and economical solution for many application areas. In recent years, we have developed a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) that allows fusing high spatial resolution data from Landsat (16-day, 30m) with high temporal resolution data from MODIS (daily, 500m). The fused reflectance products can provide synthesized images with MODIS revisit frequency and Landsat spatial details. In this presentation, we will demonstrate an operational data fusion framework based on STARFM for integrating existing MODIS data products and freely available Landsat data for ecological and crop condition monitoring. Improvements of data consistency between Landsat and MODIS will be discussed and demonstrated. Tests focus on a cloudy naturally vegetated area and a crop agricultural region. Our initial results show that the detailed spatial and temporal variability of the landscapes can be identified from the fused remote sensing data. Different spectral and temporal patterns for natural vegetation and crops can be found at the field scales in the study areas. The operational data fusion framework provides an alternative solution to build dense time-series images at high temporal and spatial resolution for ecological and crop condition monitoring.