Measuring the Effects of Soil Parameters on Bidirectional Reflectance Distribution Function

Remote sensing data acquisition often requires a revisit to the same target. Therefore, it is not always possible to have the same illumination and viewing conditions. Bidirectional Reflectance Distribution Function (BRDF) is an attempt to predict the reflectance of an object for any given viewing and illumination geometry by explaining the interaction of the incident energy with the target object, the medium lying between the source and the target, and the interaction of the reflected energy with the medium between the target and the sensor. In this study various factors affecting BRDF were explored. Various factors contribute to this characteristic of the surface to reflect unequally in different directions like its structure, shape, degree of absorption and transmittance. Bidirectional Reflectance Factor, Anisotropic Factor, and Anisotropic Index were used in the research. Radiances were recorded using the Sandmeier Field Goniometer of target areas at the agricultural farms of Mississippi State University

Estimating leaf area index in savanna vegetation using remote sensing and inverse modelling

Leaf area index (LAI), defined as the one sided green leaf area per unit ground area, is a key parameter in ecosystem process models. Owing to the large area of the earth's surface that they occupy, savanna ecosystems represent the third largest terrestrial carbon sink. There is considerable uncertainty however, as to the functioning of these ecosystems, particularly as they respond to land cover changes. Consequently, ecosystem process models constitute one of the best methods available for investigating the effect this may have on terrestrial carbon cycling. If these models are to be used over large areas however, they need to be parameterised.This thesis develops a methodology to estimate LAI in savanna ecosystems, using remotely sensed earth observation (EO) data, laboratory bidirectional reflectance measurements (BRDF), physically based canopy reflectance models (CRMs), and artificial neural networks (ANN). First, the scattering behaviour of Kalahari soils was characterised, by making laboratory BRDF measurements. Soils were shown to be highly non-Lambertian. These measurements were then used to parameterise three different CRMs. Modelled reflectances were assessed with respect to Landsat ETM+ and Terra-MODIS reflectances. Results showed that a 1-D turbid medium provided the closest fit to the measurements. A series of model sensitivity analyses (SA) were performed, and it was shown that reflectance in the red and shortwave infrared displayed greatest sensitivity to LAI, sensitivity in the near-infrared was negligible. Model inversions were performed with ANN and different waveband combinations, and LAI was estimated. The results showed that LAI could be estimated with high accuracy, an RMSE of 0.3 1, and 0.18, from ETM+ and MODIS measurements, respectively. These results were promising, and with further improvements to models, coupled with more accurate input data, will see the use of EO data play an increasingly important role in understanding the functioning of these savanna ecosystems