Develop the procedures, algorithms and models necessary to use remote sensing for estimating landscape properties and simulating processes affecting hydrological, energy and biogeochemical fluxes over a range of spatial and temporal scales.

Objectives

• Develop algorithms for deriving local, continental, and global surface temperature, soil moisture, vegetation and snow cover, surface water quality and soil erosion distributions by integrating ground and satellite observations with modeling.
• Integrate remote sensing measurements proposed for the NASA's Earth Observing System and GIS technologies into land surface models.
• Study the potential impact of global climate change on hydrologic processes and the potential of remote sensing for monitoring such changes at various scales.

Expected Results

• New remote sensing technologies and algorithms will be developed to measure landscape properties, agroecosystem health. Multi-frequency remote sensing data will be combined with GIS technologies to improve land surface models.
• Cost-effective solutions to social, economics, and environmental problems caused by water scarcity, water stress, and extreme climate events will be developed.

• An operational microwave remote sensing algorithm for evaluating vegetation, soil roughness and soil texture effects on soil moisture retrieval.
• A reliable thermal-IR emission model for evaluating emissivity variations from satellite data.
• Using new sensor technology for evaluating landscape roughness and vegetation cover variations.
• A methodology for defining subpixel heterogeneity and its effects on derived remote sensing products.

Integrating Remote Sensing with Models

• A methodology for combining multi-wavelength and multi-temporal remote sensing measurements with land surface models.
• A framework for evaluating how surface parameters derived from remote sensing scale with resolution and degree of heterogeneity.
• Understanding the role of surface heterogeneity feedbacks on atmospheric processes from local to regional scales.

A sequence of surface soil moisture maps generated from the ESTAR observations collected during SGP97.

Developing Soil Moisture Algorithm Using Satellite Data TRIMM Microwave Imager (TMI)

A composite of multiple TMI observations for 10.65 GHz frequency and H polarization The data is for July 8, 1999 over the continental US. There are 7 channels in TMI (10.65 H and V, 19.35 H and V, 21V, 37 H and V). The spatial resolution at 10.65 GHz frequency is nominally 40 km.

Example of Emissivity Separation Using Multi- spectral Thermal Data SGP97 Experiment, El Reno Study Area

Thermal Infrared Multispectral Scanner images of El Reno fields, 2 July 1997 (pixel size 12 m). Three different land surfaces in the El Reno study area are outlined: a pasture (ER09), a harvested winter wheat field (ER10) and a plowed winter wheat field (ER13). Maximum range emissivity, shown on the left (A) is scaled for contrasts between 0.00 to 0.06. NDVI, shown on the right (B), ranges from -0.1 to +0.7. Fields ER10 and ER13 are indistinguishable in the NDVI scene, but are easily distinguished in the emissivity scene. The ability to discriminate between bare soil and heavily thatched cover (wheat stubble) is important for energy balance modeling purposes.

Mapping Topography and Vegetation Cover Using Lidar

A topographic profile measured using the USDA-ARS airborne lidar altimeter. Both the topography and height and density of the sparse shrubland vegetation are observed by the sensor. This information can be used for estimating landscape roughness and vegetation height, type and cover.

Southern Great Plains Experiment 1997

From the sequence of surface soil moisture maps generated from the ESTAR observations in combination with remotely sensed land use and fractional vegetation cover modeled derived spatially distributed latent heat flux (evapotranspiration) maps for the SGP97 experimental domain.

Comparison of 12 m spatially-distributed heat flux output from a land-atmosphere remote sensing model area weighted and compared to flux aircraft measurements for evaluating up-scaling algorithms and model performance. Remote sensing and aircraft flux observations were collected over the El Reno study site during SGP97.

Evaluating a disaggregation procedure (DisALEXI) for taking a regional land-atmosphere model (ALEXI) output and with high resolution remote sensing imagery computing local fluxes. Data from the SGP97 Experiment, El Reno Study area.

Remotely Sensed Data from Monsoon �

Remotely Sensed Inputs Surface temperature Surface soil moisture Vegetation Index (NDVI)
LES-Remote Sensing Model Output Net radiation Sensible Heat flux Latent Heat flux

Results from an LES-Remote Sensing Model Evaluating the effects of surface temperature heterogeneity on simulated near-surface air temperature
The top panel is the computed correlation function between radiometric surface temperature (Tr) and the time-averaged air temperature (q) plotted as color against the vertical position at which the air temperature is taken from and the longitudinal separation between the air and surface values. The strength of the correlation coefficients is depicted by the color bar. The bottom panel shows longitudinal series from the top panel for three heights in the atmospheric boundary layer, included for further illustration. Remote Sensing data are from the Monsson � Experiment.

Research Cooperators:
NASA, NOAA, USGS, NRCS, ARS Grazing Land Research Lab, ARS National Soil Tilth Lab, ARS Jornada Experimental Range, ARS Southwest Watershed Research Center, ARS Northwest Watershed Research Center, University of Wisconsin, University of Massachusetts, Princeton, University of Maryland, University of Virginia, Utah State University, Penn State University, Oklahoma State University.