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- Author or Editor: Robert H. Woodward x
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Abstract
Five days of clear sky observations over Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. The approach relies on numerical model results to identify important variables other than soil moisture which have a significant effect on the surface temperature, and to define linear relationships between these variables and surface temperature. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudo-independent tests in which the test day is held out of the regression is 0.71. When advection is neglected in these tests the average value of r 2 drops to 0.57.
It is shown that a depiction coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as interred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92% of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85% of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.
Abstract
Five days of clear sky observations over Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. The approach relies on numerical model results to identify important variables other than soil moisture which have a significant effect on the surface temperature, and to define linear relationships between these variables and surface temperature. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudo-independent tests in which the test day is held out of the regression is 0.71. When advection is neglected in these tests the average value of r 2 drops to 0.57.
It is shown that a depiction coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as interred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92% of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85% of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.
Abstract
In the absence of a current capability for global routine daily soil moisture observation, an infrared technique using existing instrumentation is sought. Numerical modeling results are reported from a pilot study, the purpose of which was to develop such a technique and to determine the quality and reliability of soil moisture information which it can produce.
In order to determine which physical parameters observable from GOES are most sensitive to soil moisture and which are less prone to interference by seasonal changes, atmospheric effects, vegetation cover, etc., a detailed one-dimensional boundary layer-surface-soil model was employed. The model is described briefly. Results of sensitivity tests are presented which show that the mid-morning differential of surface temperature with respect to absorbed solar radiation is optimally sensitive to soil moisture. A case study comparing model results with GOES infrared data confirms the sensitivity of this parameter to soil moisture and also establishes the applicability of the model to predicting area-averaged surface temperature changes.
A series of model runs were then used to develop a simulated surface temperature dataset from which a soil moisture algorithm was developed. This algorithm uses only GOES observations to separate the soil moisture signal from the interfering effects on the surface temperature. It is shown that soil moisture can be most accurately estimated by this method in dry or marginal agricultural areas where drought is a frequent threat. Sources of error, including the effects of advection and clouds, are examined and methods of minimizing errors are discussed.
Abstract
In the absence of a current capability for global routine daily soil moisture observation, an infrared technique using existing instrumentation is sought. Numerical modeling results are reported from a pilot study, the purpose of which was to develop such a technique and to determine the quality and reliability of soil moisture information which it can produce.
In order to determine which physical parameters observable from GOES are most sensitive to soil moisture and which are less prone to interference by seasonal changes, atmospheric effects, vegetation cover, etc., a detailed one-dimensional boundary layer-surface-soil model was employed. The model is described briefly. Results of sensitivity tests are presented which show that the mid-morning differential of surface temperature with respect to absorbed solar radiation is optimally sensitive to soil moisture. A case study comparing model results with GOES infrared data confirms the sensitivity of this parameter to soil moisture and also establishes the applicability of the model to predicting area-averaged surface temperature changes.
A series of model runs were then used to develop a simulated surface temperature dataset from which a soil moisture algorithm was developed. This algorithm uses only GOES observations to separate the soil moisture signal from the interfering effects on the surface temperature. It is shown that soil moisture can be most accurately estimated by this method in dry or marginal agricultural areas where drought is a frequent threat. Sources of error, including the effects of advection and clouds, are examined and methods of minimizing errors are discussed.
