Overview

The US agricultural sector suffers from a lack of accurate and detailed soil temperature and moisture forecasts. Crude agriculture–specific models have been developed, often in academic environments, to address this need. For example, phenological pest models have been developed based only on average daily air temperatures. RAL with funding from NASA conducted researched on this problem by combining advanced weather and soil forecast systems (Dynamic, Integrated Forecast system (DICast®) and the High–Resolution Land Data Assimilation System (HRLDAS) with the goal of producing a more accurate soil temperature and moisture forecast at high temporal and spatial resolutions. The output of the land–surface model was evaluated by Telvent/DTN/Meteorlogix (RAL's commercial partner in this project). NASA's interest in funding the project was to determine whether incorporation of their Moderate Resolution Imaging Spectroradiometer (MODIS) products into the land–surface model could improve soil temperature and moisture forecasts.

Leaf Area Index (LAI) from HRLDAS on 22 April 2006.

The vegetation state and land use are key factors in the land–surface model. Prior to this project, HRLDAS has used static land use and climatological vegetation data sets developed in the 1970s by the USGS. This project evaluated the use of MODIS satellite data sets to improve the initial conditions provided to HRLDAS. The MODIS land use data sets are static, but are of higher spatial resolution and are much newer than the USGS data sets. Rather than monthly climatological averages, the MODIS Leaf Area Index (LAI) and other products are updated weekly and better represent the current vegetation state. As anticipated, the use of these products improved the soil temperature and moisture forecasts, but great care must be taken when utilizing these data types for this purpose.

Leaf Area Index (LAI) from MODIS on 22 April 2006 showing more detail.

Model runs covering 2005–2006 were compared to soil temperature and moisture observations. The soil temperature errors at 5 cm were reduced by roughly 10% and the 10 cm soil temperature errors were reduced by approximately 50%. These are critical depths for agriculture.

MODIS data, as well as enhancements made by the HRLDAS developers during this project, were incorporated into the prototype soil temperature prediction system. Continued land–surface model development will attempt to further improve the modeled heat transfer at the surface. The results from retrospective studies and the 2009 growing season using these model improvements were incorporated into the end–of–project report to NASA.