Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

Gomez, Cécile; Gholizadeh, Asa; Borůvka, Luboš; Lagacherie, Philippe

doi:10.5194/isprsarchives-xl-3-w3-439-2015

Cited by 2 publications

(1 citation statement)

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“…The reflectance at different wavelengths is affected by the physical, chemical and mineralogical properties of the soils and can be used to predict different soil properties with multivariate methods or machine learning algorithms. Examples of predicted soil properties include clay and sand content (Gomez, Gholizadeh, Boruvka, & Lagacherie, 2015;Lagacherie et al, 2013;Ouerghemmi, Gomez, Naceur, & Lagacherie, 2011;Selige, Böhner, & Schmidhalter, 2006), organic carbon (Gomez, Viscarra Rossel, & McBratney, 2008), total nitrogen (Selige et al, 2006), CaCO 3 (Lagacherie, Baret, Feret, Madeira Netto, & Robbez-Masson, 2008), and iron and cation exchangeable capacity (Gomez, Lagacherie, & Coulouma, 2012). Although airborne hyperspectral data have high spatial and spectral resolutions, they are often more sensitive to atmospheric inference (e.g., cloud) and easily affected by the unevenness of the ground surface (Gomez, Lagacherie, & Coulouma, 2008).…”

Section: Introductionmentioning

confidence: 99%

Synergistic use of hyperspectral imagery, Sentinel‐1 and LiDAR improves mapping of soil physical and geochemical properties at the farm‐scale

Zhang

Hartemink

Huang

et al. 2021

European J Soil Science

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Airborne imaging spectroscopy data provide soil and vegetation information over relatively large areas at high spatial resolutions (<5 m). We combined airborne hyperspectral data with space-borne data to map soil properties and investigate the contributions of the different sensor data to the mapping accuracy. The study was conducted on a 330-ha farm in south-central Wisconsin, USA, where soils are relatively young and soil variation is high. Seventy-three soil samples (0-10 cm depth) were taken from cropped fields before planting. The soil data were used with remote sensing data for mapping clay, silt, sand, total carbon (TC), Mg, Al, Si, Fe, Ca, Ti, Mn and Zr. Three types of variables were compared: (a) DEM + Sentinel-1 & 2, as these are easy-to-obtain, (b) hyperspectral data with high-spatial resolution, and (c) hyperspectral data + DEM + Sentinel-1, to evaluate if the prediction can be improved by combining hyperspectral data with DEM + Sentinel-1, and if combining DEM + Sentinel-1 with hyperspectral data had higher prediction accuracy than combination with Sentinel-2 data. The partial least square regression (PLSR) model was used for establishing relationships between soil and remote sensing data. It was found that airborne hyperspectral imaging can accurately map the spatial distributions of soil clay content, Si and Fe concentrations. Combining hyperspectral data with DEM and Sentinel-1 improved the performance of models for mapping a range of soil properties (e.g., clay, silt, sand, Al, Ti, Mn and Zr). Total C, Mg and Ca concentrations cannot be predicted from the combination of hyperspectral data, Sentinel-1 and terrain attributes. In a highly heterogeneous landscape, surface soil properties can be accurately mapped combining LiDAR DEM, Sentinel-1 and hyperspectral data. Highlights• Airborne imaging spectroscopy, DEM and Sentinel-1 were used to map surface soil properties.• Hyperspectral data can accurately map clay, Si and Fe.• Combining hyperspectral data with DEM, Sentinel-1 improved prediction accuracy of several soil properties.

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Section: Introductionmentioning

confidence: 99%

Synergistic use of hyperspectral imagery, Sentinel‐1 and LiDAR improves mapping of soil physical and geochemical properties at the farm‐scale

Zhang

Hartemink

Huang

et al. 2021

European J Soil Science

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Representation the Properties of the Central-Forest Reserve Southern Taiga Forest Soils in Remote Hyperspectral Measurements

Puzachenko,

Baibar,

Puzachenko

2023

Izvestiâ Akademii nauk. Rossijskaâ akademiâ nauk. Seriâ biologi

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It is studied the representation of the texture and color of the soil on the Mansell scale, measured to a depth of 135 cm with an interval of 5 cm on the transect (length 6 km) with a step of 20 m, in hyperspectral data of the Hyperion instrument for 9 measurement periods from January to September. It was found that satellite image for May 24 is most closely related to all the characteristics under consideration. For the soil texture – the largest relationships are observed for depths of 7–15 cm, 45 cm, 75 cm and 120 cm; for color hue – 3–10 cm and 95–135 cm; for color value – 7–15 cm, 25–30 cm and 65–70 cm; for color chroma – 7–10 cm, 40–50 cm, 75 cm and 100 cm. The texture of soil is most related with wavelengths 579–702 nm and 529 nm, color hue – 641–691 nm, color value – 569–702 nm and 518–539 nm, color chroma – 569–702 nm, 508–529 nm and 732–763 nm.

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Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

Cited by 2 publications

References 20 publications

Synergistic use of hyperspectral imagery, Sentinel‐1 and LiDAR improves mapping of soil physical and geochemical properties at the farm‐scale

Synergistic use of hyperspectral imagery, Sentinel‐1 and LiDAR improves mapping of soil physical and geochemical properties at the farm‐scale

Representation the Properties of the Central-Forest Reserve Southern Taiga Forest Soils in Remote Hyperspectral Measurements

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