<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than <italic>in situ</italic> measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R<sup>2</sup> values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.</p>
</abstract>

Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than in situ measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R2 values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Transactions on Aerospace Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its...

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Data publikacji: 01 lip 2021

Zakres stron: 1 - 11

DOI: https://doi.org/10.2478/tar-2021-0007

Słowa kluczowe
soil moisture content, remote sensing, hyperspectral data, bare soil, UAV

© 2021 Kubiak Katarzyna et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Data publikacji: 01 lip 2021

Zakres stron: 1 - 11

DOI: https://doi.org/10.2478/tar-2021-0007

Słowa kluczowesoil moisture content, remote sensing, hyperspectral data, bare soil, UAV

© 2021 Kubiak Katarzyna et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
soil moisture content, remote sensing, hyperspectral data, bare soil, UAV