USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES
Title: Vegetation Water Content Mapping in a Diverse Agricultural Landscape: The National Airborne Field Experiment 2006
Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 19, 2010
Publication Date: June 19, 2010
Citation: Cosh, M.H., Tao, J., Jackson, T.J., McKee, L.G., O'Neill, P. 2010. Vegetation water content mapping in a diverse agricultural landscape: The National Airborne Field Experiment 2006. Journal of Applied Remote Sensing (JARS). 4(043532):1-12.
Interpretive Summary: Land cover and vegetation characteristics are important parameters for understanding the land surface atmosphere interface. Soil moisture, land surface temperature and evapotranspiration are heavily influenced by the characteristics of the vegetation at the land surface and a key property of the vegetation is the vegetation water content. Critical to soil moisture remote sensing in particular is the remote sensing of vegetation water content as it is a confounding factor in the soil moisture retrieval algorithms. A set of studies has been initiated to determine how vegetation water content can be estimated for a variety of agricultural crops to aid in the retrieval process of soil moisture. One such study was the National Airborne Field Experiment 2006 (NAFE06) in the Murrumbidgee Catchment in Australia in late 2006. Aircraft and field samplers were deployed to measure soil moisture on a broad range of scales in the critical agricultural region of Australia. Crop types included wheat, alfalfa, and canola. Error estimates were within 0.33 kg/m2.
Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE’06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE’06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/m2. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy.