Imaging Spectroscopy for Soil Mapping and Monitoring

Abstract

There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy-and decision-makers. Soil and imaging spectroscopy, based on visible-near-infrared and shortwave infrared (400-2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from airand spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world's soil resources.

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

“…It is likely that the LUCAS samples in other European regions are not numerous enough [29] or not representative of the soil variability of the investigated area. Until now, remote sensing or airborne spectroscopy was used for small areas that are mostly rather homogeneous in parent material and soil forming factors [30]. Castaldi et al [9] demonstrated that sampling strategies based on the feature space, where the spectral bands were used as ancillary data, were most efficient when the Demmin area was stratified, according to 'soil scapes,' i.e., distinguishing sandy and clay topsoil (Figure 1).…”

“…Castaldi et al [9] demonstrated that sampling strategies based on the feature space, where the spectral bands were used as ancillary data, were most efficient when the Demmin area was stratified, according to 'soil scapes,' i.e., distinguishing sandy and clay topsoil (Figure 1). Other factors that are likely to improve the SOC prediction models are the absence of a soil crust (since it does not represent the spectral signal of the topsoil [30] and the large variability in SOC content). Gomez et al [31] reported that, in contrast to clay and calcium carbonate content, SOC could not be predicted in Mediterranean environments due to the very low and rather constant SOC content.…”

On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments

“…The explicit representation of water reservoirs -and other infrastructure -is indeed likely to result in more realistic soil parameters, a hypothesis whose verification depends on the availability of observations about soil physical properties for large spatial domains. In turn, this highlights the importance of studies aimed to infer such properties from remotely sensed images (Chang and Islam, 2000;Chabrillat et al, 2019). A related topic that may also deserve future research is the robustness of these models with respect to changes in the operations or physical characteristics of dams.…”

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

“…Firstly, optical RS captures only the properties of the uppermost centimeters of the top-soil and cannot provide information about the entire soil body. Secondly, atmospheric effects and sensor constraints [119], soil moisture contents [120], soil roughness, or soil surface coverage by vegetation or plant residues [117] can interfere with the spectral signal [121].…”

“…See Chabrillat et al [121] for a recent review of the potential of upcoming spaceborne hyperspectral imagery for global soil mapping and monitoring. Table 2 provides an overview of the recent and future spaceborne hyperspectral missions with their sensor characteristics deriving soil characteristics and traits.…”