The use of optical remote sensing for large-scale soil mapping

Soils are often at the heart of the services that ecosystems deliver, not only in terms of food production, but also in filtering, the cycling of nutrients, the storage and regulation of water and in providing habitats for soil biota. As a result of overuse, soils and their functions are under increasing pressure. Degradation of soils in the form of erosion, dust storms, salinisation, pollution, compaction, depletion, decomposition of organic matter and destruction of soil aggregates, is the result. The mapping of soil properties and functions and the monitoring of changes over time are important to secure soil functions in the future. Especially, spatially distributed soil information has become more important with the use of global and regional models, which often require full coverage soil information. The use of remote sensing can offer spatial and temporal quantitative soil information of extended areas, which can be acquired with limited fieldwork. Remote sensing under laboratory conditions or infield studies in semi-arid areas has shown promising results for soil purposes. However, when acquiring data with airborne or satellite sensors of extended areas in temperate zones, limitations by vegetation coverage and soil surface variations are driving coherent spatiotemporal data collection. The use of multi-temporal data in agricultural areas can be used to increase the bare soil area captured by remote sensing data. The alternation of crops results in bare fields at the moment of seeding and harvesting. We used spectrally and spatially high-resolution data from an airborne imaging spectrometer of three consecutive years to create a multi-temporal composite. This composite contained more than double the amount of bare soil pixels as compared to a singular acquisition. Global linear soil surface variations could be compensated based on the spectral differences only. In order to compensate for local non-linear soil surface variations, however, quantitative information was needed. Based on independent datasets of soil moisture and soil surface roughness, we were able to correct per wavelength for these local non-linear variations with a relative simple algorithm. The advantage of the independent datasets is that the used algorithm can be applied to all imaging spectroscopy data with known soil moisture and/or soil surface roughness. A better soil surface roughness dataset is needed in order to improve the results. Additionally, the concept of the multi-temporal composite was also applied to Landsat time series from 1985 to 2017. Although spectrally less detailed, these sensors provide denser time series and larger extents than the high-resolution airborne data. About 5 years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). We show the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making. This thesis shows that optical remote sensing for soil purposes offers valuable spatial soil information. Spectrally and spatially high-resolution data are able to show in-field variations. These are not covered by more standard soil mapping approaches like digital and conventional soil mapping. The need to correct for soil surface variations is, however, necessary in order to give a realistic picture of these in-field variations. The temporally high-resolution, but spectrally less detailed, data provides soil maps with very similar patterns compared to the available digital soil map. In areas with limited alternating crops, the use of remote sensing for soil purposes is limited. Finally, we discuss to which extent the focus of soil studies with remote sensing data should be on the standardisation of protocols and the necessary pre-processing steps. A further development of the scientific community in this context is desirable, especially since there is a need to map, monitor and model soil changes. The present work has shown the added value of remote sensing data and products for these purposes.