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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 #328370
Title: Compositing MODIS Terra and Aqua 250m daily surface reflectance data sets for vegetation monitoring

Author
item YANG, ZHENGWEI - National Agricultural Statistical Service (NASS, USDA)
item Gao, Feng
item HU, LEI - George Mason University
item YU, GENONG - George Mason University
item DI, LIPING - George Mason University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/14/2016
Publication Date: 7/18/2016
Citation: Yang, Z., Gao, F.N., Hu, L., Yu, G., Di, L. 2016. Compositing MODIS Terra and Aqua 250m daily surface reflectance data sets for vegetation monitoring. Meeting Abstract. 2016 CDROM.

Interpretive Summary:

Technical Abstract: Remote sensing based vegetation Indices have been proven valuable in providing a spatially complete view of crop’s vegetation condition, which also manifests the impact of the disastrous events such as massive flood and drought. VegScape, a web GIS application for crop vegetation condition monitoring system based on NASA MODIS (or Moderate Resolution Imaging Spectroradiometer) 250m resolution, daily surface reflectance data from Terra sensor, has been successfully providing vegetation/crop condition monitoring service to US government and agricultural industrial community for their decision making support, and to researchers, professors and students for their research and education use since its inception in 2013. The VegScape service relies on the data availability of MODIS Terra sensor. Unfortunately, its operation has often been interrupted by incidence of MODIS Terra data unavailable. The occasional unavailability of MODIS Terra data has affected business operation of those rely on VegScape data. In addition, cloud contamination could limit the capability on monitoring vegetation condition continuously especially during quick growth season. MODIS is aboard NASA's Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning at ~10:30am, while Aqua passes south to north over the equator in the afternoon at ~1:30pm. This paper proposes to derive vegetation indices by compositing MODIS data from both MODIS Terra and Aqua sensors. This composite aims to significantly increase the availability of MODIS data. It is assumed that the crop vegetation should not change significantly within a few hours and so does the remotely sensed imagery from MODIS Terra and Aqua. In this paper, the daily nadir BRDF (Bidirectional Reflectance Distribution Function) adjusted reflectance (NBAR) is first computed for both Terra and Aqua MODIS at 250m resolution. This correction reduces view and illumination angle effects for better image normalization and mosaicking. The BRDF-corrected daily MODIS surface reflectance images from both Terra and Aqua are then composited using maximum value composite (MVC) method to produce a daily composite image. The weekly composite data are then produced from the daily composite results. The preliminary results show that NBAR data effectively eliminates the radiometric differences caused by different view zenith angles from different swaths and different sensors. The quality of the MODIS composite from Terra-Aqua surface reflectance images have been significantly improved as compared with the Terra only composite results. The mosaicked surface reflectance image becomes seamless and radiometric measurement becomes more consistent. The cloud cover of the Terra-Aqua composite image has been significantly reduced due to the addition of extra data from Aqua. The resulting vegetation indices derived from the Terra-Aqua composited data is smoother than those not BRDF-corrected. Finally, compositing Terra-Aqua will significantly reduce the VegScape operation down time since the VegScape can continue operation even when one of sensors stops working.