Prediction Of Clay Content Research Articles

Using AVHRR images for spatial prediction of clay content in the lower Namoi Valley of eastern Australia

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=40), using the root mean square error (RMSE) of prediction. The MLR-ETS performed very poorly (RMSE=35.0%), due to some degree of contrariety of the external training data with the data from the prediction area. The RK is superior to all the methods in predicting the topsoil % clay with RMSE of 10.2%. This performance by RK, compared with MLR-ITS (RMSE=13.3%) and IRF-1 (RMSE=12.6%), is quite remarkable at the regional scale of consideration. The correlation coefficient ( ρ) between the actual and predicted % clay confirms the order of prediction performance. Isarithmic maps of the topsoil % clay, as predicted by the best two methods, indicate that IRF-1 over-smoothed the predicted clay values as it removed both the low and high spikes in the spatial distribution of the % clay. However, the map of predicted topsoil clay by RK, which incorporated the AVHRR bands and indices in the prediction model reflects most of the local variability. Thus this study has demonstrated that a few soil-sampled sites, combined with the AVHRR data, could be adequate for regional soil inventory of good quality and known precision using RK. The basic tenet of this study can be extended to any situation where ancillary attributes have relatively high correlation with soil variables.