Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources. This is because of the advantages of these modern technologies in that they provide a high sampling density enabling the exploration of the within-field spatial and temporal variability of soil characteristics, and being fast and cost-effective, compared to the traditional laboratory analysis methods. However, soils are complex in nature, and measurements of key soil properties or processes in soils might not be achievable by the use of a single sensor. This necessitates innovative solutions beyond the single-sensor approach, which should be implementable not only under laboratory conditions but in situ in either stationary or on-line measurement modes. In the past few years, several studies on the implementation of multi-sensor (remote and proximal) and data fusion approaches in soil science have been reported in the literature, although this research area is still at its early stages of development. The aim of such integration is twofold: (1) improves the detection accuracy of a soil property or a list of soil properties, and (2) improves the quality of soil maps, management zone maps and the resulting decision support for variable rate applications in precision agriculture. The integration of different data layers has greatly benefited many other applications in agriculture and environmental soil modeling that require more extensive temporal and spatial information than that contained in any individual dataset provided by a single sensor. At the same time, the major progress that has been made in different aspects of digital soil mapping (DSM) make DSM more mature and operable than ever before. The integration of multi-sensor source data fusion with the DSM technique is foreseen to provide a better understanding of soil processes and enable a more accurate estimation of soil properties at various spatial and temporal scales. It will also provide new insights into processes occurring in soils and sources of variabilities linked to soil dynamics in different scenarios of land management practices, precision agriculture, environmental pollution, and climate change