INTEGRATING MODIS AND LANDSAT DATA FOR ECOLOGICAL AND CROP CONDITION....FOR THE SERVIR PROJECT IN HINDU-KUSH HIMALAYA (HKH)REGION
Hydrology and Remote Sensing Laboratory
2013 Annual Report
1a.Objectives (from AD-416):
To build an operational data fusion approach to integrate existing Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products and Landsat data for ecological and crop condition monitoring in HKH region.
1b.Approach (from AD-416):
Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. A synthesized approach fusing multiple remote sensing inputs provides a feasible and economic solution for many application areas. In recent years, we have developed a Spatial 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, 250-500m). The fused reflectance products can provide synthesized images with MODIS revisit frequency and Landsat spatial details. Here, we will build an operational STARFM approach to integrate existing MODIS reflectance products and freely available Landsat data for the SERVIR (Spanish “to serve”) project. The operational algorithm will maintain a cloud-free historical Landsat and MODIS image database for forward predictions as new MODIS acquisitions become available. The Landsat and MODIS image pairs will be updated once the latest clear Landsat and MODIS image pair becomes available. We will evaluate and test the STARFM algorithm for crop and ecological condition monitoring in the HKH region.
Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. However, it is difficult to acquire remotely sensed data with both high spatial resolution and frequent coverage. In this project, we have improved and built an operational data fusion framework to integrate existing MODIS data products and freely available Landsat data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) for the NASA SERVIR (Spanish “to serve”) project. The data fusion framework has been evaluated and tested for crop and ecological condition monitoring in the Hindu Kush-Himalayan (HKH) region. The software package and testing dataset have been delivered to the NASA SERVIR program.