It is argued that for many regions of the earth, ground based remote sensing techniques for rainfall measurement are impractical due to their cost and limited range of coverage. Global rainfall estimates must therefore be provided largely by satellites, particularly those exploiting atmospheric windows in the visible/IR and microwave regimes. / A 2-D pattern matching technique using visible and IR data is proposed and its accuracy investigated. Because these wavelengths respond primarily to cloud and not rain sized drops, significant accuracy may be achieved in rain areas, but not directly in amounts. However, when long term average rain rates for raining areas are used, errors of (TURN) (+OR-) 49% for 10('5) km('2) are achieved. This compares favorably with other techniques based on cloud life histories. / Microwave radiometer-based techniques are also investigated and are found to be fundamentally limited in accuracy ((TURN) (+OR-) 70%) by the large variations in the (unknown) effective rain layer height. However, useful accuracy ((TURN) (+OR-) 20%) may be obtained over large areas ((TURN) 10('5) km('2)), provided that the long term average effective rain layer height is known. It is argued that a once or twice daily satellite rain estimate is insufficient for either accurate estimates of daily accumulation or for samples of climatological rain rates. A hybrid microwave satellite-vis/IR technique is therefore proposed in order to solve the temporal resolution problem without the necessity of orbiting many microwave radiometers. This technique would take advantage of the high ( 1/2 hour) temporal resolution of the vis/IR data and could achieve accuracies of (TURN) (+OR-) 20% for 12 hour accumulations over 10('5) km('2) provided that the mean effective rain layer height is known. The practical implementation of such a technique will ultimately depend on the relative difficulty of measuring the mean effective rain layer height, and the mean rain rate for raining areas as well as the availability of appropriate sensor platforms.

No description available.

Thesis (Ph. D.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2003. / Includes bibliographical references (p. 231-237).

The sertao of northeast Brazil is a semiarid region characterized by recurring droughts. The vastness of the area (650,000 km$ sp2)$ poses a challenge to the effective monitoring of the impacts of drought at a scale that would be useful to the inhabitants of the sertao. Remote sensing data provide a viable way of assessing the extent and nature of drought across the landscape. / The work present a more effective algorithm to estimate rainfall from both the cold and warm cloud types present. Using a decision-tree methodology, the analysis yields rainfall estimates over the 0-21 mm range. Because seasonal variations in rainfall produce differences in vegetation, soils and hydrologic responses, Principal Components Analysis was used to examine these land surface responses. Individual components and component pairings were useful in identifying variations in vegetation density, geobotanical differences and drainage characteristics. The presence of cloud cover was found to dampen the land surface information that could be extracted. Landsat Thematic Mapper (TM) imagery was then used to produce a moisture index which characterizes surface wetness in relation to other features present in a scene. The multispectral combination of TM bands 1, 4 and 6 allowed for the separation of the surface types present, in locational space. This space was defined by an open-ended triange made up of a vertical "water line", a horizontal line of equal vegetation density; and a negatively-slopping iso-moisture line. The stability of the moisture index was influenced by varying scale and seasonal conditions. / In the drought conditions that prevailed in 1991-1992, these methods provide important additions to existing drought monitoring approaches in the Brazilian northeast. Further calibration is required in order to extend their applicability to other geographical regions and time frames.

No description available.