文摘
Soil information is needed at the regional scale to enable planning of land utilization in accordance with its capacity. Because existing soil maps are inadequate in Australia to meet this demand, there is the need to develop models that could be used to improve soil maps at this scale for aggregation up to the national or continental scale. The most efficient and cheapest means of achieving this is by using remotely sensed data in multivariate spatial prediction models. This study therefore examines the soil spectral properties as depicted by the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) data, with the main aim of developing prediction models for improved mapping of, for example, topsoil % clay in the Lower Namoi Valley of New South Wales (NSW). The paper compares several prediction models: multiple linear regression (MLR) using an external training set (MLR-ETS), interpolation by MLR — MLR-INT, kriging based on a generalized covariance function of order 1 (IRF-1), and a mixed model of MLR and ordinary kriging, termed as regression/kriging (RK). Comparison was based on an independent validation set (N
