Estimation of Soil Properties Using HyperspectralVIS/IRSensors

Abstract

Knowledge of soil properties and processes are crucial to the understanding of the terrestrial hydrologic cycle and the functioning of terrestrial ecosystems. In this paper, we present the current state and potential of hyperspectral remote sensing techniques for quantitative retrieval of soil properties. Remote sensing is used to detect chemical and physical soil properties either (i) directly from the bare soil pixels, (ii) through advanced spectroscopy methods in mixed “soil‐vegetation‐litter” pixels, and (iii) by measurements of the overlying vegetated canopy to infer soil properties and moisture status. Optical‐geometric properties of soil surfaces reveal information on soil physical features, such as soil structure, crusting, and erosion. We also investigate the use of vegetation water indices to infer soil drying and wetting in the soil root zone. We conclude with a discussion on future needs and directions for remote sensing of soil properties.

Relationships between Moderate Resolution Imaging Spectroradiometer water indexes and tower flux data in an old growth conifer forest

“…The spectral region from 1550 to 1750 nm is best suited for leaf water content retrieval [45,[81][82][83]. Consistent with this, MODIS band 6 (1640 nm) was demonstrated by radiative transfer modeling to be more sensitive to variation of leaf water content [39] but is also sensitive to soil moisture [19,56,84] while MODIS band 5 was minimally affected by soil moisture. As a result, NDII had a larger dynamic range than NDWI.…”

“…This is especially true in the regrowing forests that are widespread over this region [29], thus it is likely that variation in soil moisture affects the NDII measurement. The band 5 water absorption feature is enhanced by the high NIR scattering in the leaf, hence, the NDWI is sensitive to changes in liquid water content of vegetation canopies, and relatively insensitive to soil surface moisture [84].…”

Estimation of Soil Organic Matter, Total Nitrogen and Total Carbon in Sustainable Coastal Wetlands

“…Zhang et al [35] estimated SOM and available potassium by using partial least squares (PLS) and least squares support vector machine (LS-SVM). Numerous studies have concentrated on modeling soil parameters from remote sensing techniques either from bare soil, or by inferring soil properties by vegetation cover [36,37]. However, applying hyper-spectral remote sensing technology to the topsoil nutrients in coastal wetlands remains limited [38].…”