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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 #346141

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Vegetation water content of crops and woodlands for improving soil moisture retrievals from Coriolis WindSat

Author
item Hunt, Earle - Ray
item Li, L. - Naval Research Laboratory
item Hawley, Jennifer
item Gaiser, P. - Naval Research Laboratory
item Twarog, E. - Naval Research Laboratory
item Cosh, Michael

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2018
Publication Date: 2/10/2018
Citation: Hunt Jr, E.R., Li, L., Friedman, J.M., Gaiser, P., Twarog, E., Cosh, M.H. 2018. Vegetation water content of crops and woodlands for improving soil moisture retrievals from Coriolis WindSat. Remote Sensing. 10:273. https://doi.org/10.3390/rs10020273.
DOI: https://doi.org/10.3390/rs10020273

Interpretive Summary: Monitoring soil moisture content using microwave emissions from the soil is important for assessing drought and agricultural production. There are several satellites in orbit which measure microwave emissions from the soil surface, one of which is Coriolis WindSat developed by the US Department of Defense. Errors from estimating soil moisture content from satellites increase exponentially with increasing water contained in the vegetation above the soil, particularly in the woody stems of trees. Stem water content is proportional to tree height, so our objective was to improve methods for remote sensing stem water content by comparing satellite data with tree height in a mixed agriculture-woodland area. However, there was no relationship between tree height and vegetation indices calculated from either Landsat 8 Operational Line Imager or WorldView-3 satellite data. This research will be used by other scientists to develop better algorithms to correct microwave satellite data for vegetation water content.

Technical Abstract: Estimation of vegetation water content (VWC) by shortwave infrared remote sensing improves soil moisture retrievals. The largest unknown for predicting VWC is stem water content, which is assumed to be allometrically related to canopy water content. From forest science, stem volume is linearly related to tree height, and stem water content is proportional to stem volume. We hypothesized that tree height is positively correlated with canopy water content, and thus with the normalized difference infrared index (NDII). Airborne color-infrared imagery were acquired at two study areas on Maryland’s Eastern Shore, and photogrammetric structure-from-motion point clouds were constructed to estimate tree heights. The estimated tree heights were only weakly correlated with measured data. NDII was calculated from Landsat 8 Operational Line Imager (30-m pixel) and WorldView-3 (7.5 m pixel); but contrary to the hypothesis, NDII was not correlated with woodland stand height. Therefore stem water content was calculated from average tree height and canopy water content was calculated from L8-OLI to obtain VWC. For maize and soybean, measured stem water contents were highly correlated to canopy water contents, so VWC was calculated directly from NDII. VWC was estimated for 2 days, one in the early summer and one in the late summer; there was an average increase of 0.5 kg m-2 between the two dates, primarily from the growth of maize. VWC for the surrounding region varied from 2.0 to 3.0 kg m-2, so retrievals from Coriolis Windsat could be included with other microwave data for soil moisture contents at higher temporal resolution.