Marshall J. McFarland

Land surface temperature derived from the SSM/I passive microwave brightness temperatures

Passive microwave brightness temperatures from the Defense Meteorological Space Program Special Sensor Microwave/Imager (SSM/I) were used to determine surface temperature over land areas in the central plains of the United States. A regression analysis comparing all of the SSM/I channels and minimum screen air temperatures (representing the surface temperature) showed good correlations, with root-mean-square errors of 2-3 degC. Pixels containing large amounts of water, snow, and falling rain, as classified with SSM/I brightness temperatures, were excluded from the analysis. The use of independent ground truth data such as soil moisture or land surface type was not required to obtain the correlations between brightness temperatures and surface temperatures. >

Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager

The use of empirical parameter retrieval algorithms over land requires the prior classification of surface types according to their microwave emission properties. A land-surface-type classification scheme was developed to be used with the Special Sensor Microwave/Imager (SSM/I) algorithm package. The classification rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent classification rules was derived which should result in increased confidence of parameter retrievals. >

Correlations between Nimbus-7 Scanning Multichannel Microwave Radiometer Data and an Antecedent Precipitation Index

Abstract Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25–50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.

Nimbus 7 SMMR Investigation of Snowpack Properties in the Northern Great Plains for the Winter of 1978-1979

An investigation of the capabilities of remote sensing of snowpack properties was conducted with brightness temperatures from the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and climatological data for the northern Great Plains for the winter of 1978-1979. The radiometer data included horizontally and vertically polarized brightness temperatures at the 0.81-, 1.66-, and 2.80-, and 4.54-cm wavelengths for both day and night overpasses, with a repeat coverage on the average of every two to three days. The brightness temperatures in each channel and the daily surface climatological elements of maximum and minimum air temperature, precipitation, snowfall, and snow depth were objectively analyzed to a 20-km grid with 35 rows and 42 columns. The analysis concentrated on temporal analyses of selected grid cells. Characteristic signatures were observed for initial snow accumulation, snow depth to about 20 cm, beginning of snow melting in the surface layers, and snow melt. The process of snow ripening was evident in the thawing and refreezing cycles of the snow surface layers. Discrimination of dry soil, wet soil, snow amount to 15 cm, and liquid water at the soil surface before runoff occurred was present with the use of both polarizations at the 0.81- and 1.66-cm wavelengths, although the longer wavelengths contained additional information on the state of the surface underlying the snow pack.

Energy analysis of lateral move irrigation machines

Abstract The energy required to operate linear-move irrigation machines fed by flexible hoses is analyzed in this paper. The total energy requirement was divided into five components: three related to distributing the water, and two related to moving the machine and dragging the hose. The energy components are represented by mathematical equations and a numerical example is presented using available data for a typical short (72 m) and long (360 m) lateral move irrigation machine. The major findings are: (a) 60–70% of the energy is required to distribute the water; (b) the energy required to move the machine and to drag the flexible hose is less than 3%; and (c) the short machine consumes 30% less energy than the long machine, mainly due to the low-pressure control system for water distribution on the soil surface.

Agricultural climatology: a review of the science and the potential

Agricultural climatology has evolved into a science capable of playing a major role in the provision of sufficient food to support the worlds increasing population. Climatic resource assessment and crop‐weather model development for yield assessment are the primary applications.