Remote Sensing
and Agriculture

We are developing management decision aids that derive much of their power from satellite data, made available quickly after collection. By assisting agricultural managers in some daily decisions, pesticide usage is reduced, energy is saved, and potential water quality impacts of crop production are minimized.

Real-time Surface Flux Estimates
This Atmospheric Land EXchange - Inverse (ALEXI) model is applied to surface flux evaluation over large geographical region (e.g, the central United States). Daily input data sources, and output data products are routinely produced. These products are made available via the internet for the USA Region, as well as for the IHOP Domain. Ongoing validation efforts of these satellite derived land-surface flux estmates include comparison against Ameriflux tower measurements near Park Falls, Wisconsin, and using the flux mesurements being collected by the Oklahoma Mesonet. This collaborative effort between CIMSS and the University of Oklahoma is mutually beneficial for both parties as they attempt to confirm their flux data.

In additon to polar orbiting and geostationary satellite information, ALEXI model input data fields of temperature, pressure and atmospheric moisture are provided by the CIMSS Regional Assimilation System (CRAS) forecast model. Fraction vegetation cover is provided by the USGS EROS AVHRR 1 km composite, bi-weekly NDVI product. All other radiometric data are from the GOES Sounder instrument. See Mecikalski et al. (1999; J. Appl. Meteor., V. 38, 1352-1369) for more complete details on the analysis technique. Graphics are created with NCAR Graphics.

Decision Support Products in Agriculture
Information products will be made available to decision-makers by way of the World Wide Web, and/or satellite-based information transmission systems. A number of products (and almost products) have found their way to the Wi-Mn Coop. Extension Agric. Weather page.

Three decision aids are in various stages of development:

Estimated Evapotranspiration --Assisting potato farmers determine how much and when to irrigate, to minimize the amount of water and fertilizer reaching the groundwater beneath their crops.

Potato Foliar Disease Prediction --Predicting development of leaf blight disease on potatoes, allowing the growers to apply fungicide sprays only when necessary.

Cranberry Frost Prediction --Advising cranberry growers of the chance of night time frost in their marshes, both early in the day and as the evening develops.

The tools used to develop these decision aids are data sources and research models. The primary data sources are estimates of solar energy at the Earth's surface based on GOES-8 Satellite images and surface weather observations.

Two computer simulation models are being used in our work: Mesoscale model of the atmosphere above the United States to provide one and two day forecasts, and a detailed Plant-environment model to assist the development of simple surface models that are interfaced with the mesoscale model to provide site-specific information.

TiSDat is part of a NASA-sponsored program to facilitate access to satellite data for all potential users. TiSDat is a suite of decision support information products for agriculture that merge satellite and meteorological data and forecast models of theatmosphere and land surface. Much of the technology is transferable to other crops and applications, and additional products are in development. For technical and administrative background on the program, visit the Public Use of Remote Sensing Data (RSD) page.