Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

Abstract

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

Evaluating the capability of a UAV‐borne spectrometer for soil organic carbon mapping in bare croplands

“…It should be noted that the SOC variation in this study was relatively small compared to other studies using UAV‐based spectroscopy (Aldana‐Jague et al, 2016; Biney et al, 2021; Laamrani et al, 2019). The successful prediction of SOC using UAV‐derived spectra demonstrates an adequate performance and sensitivity of hyperspectral sensor to derive effective information from noisy signals (caused by surface disturbing conditions) given the fine spectral resolution, even when the variation range of the target variable is small.…”

“…It should be noted that the SOC variation in this study was relatively small compared to other studies using UAV-based spectroscopy (Aldana-Jague et al, 2016;Biney et al, 2021;Laamrani et al, 2019).…”

“…Several studies have investigated using multispectral (Aldana‐Jague et al, 2016; Biney et al, 2021) and hyperspectral (Laamrani et al, 2019) imaging sensors on UAV platforms to assess SOC contents. As opposed to imaging sensors, the point spectrometer used in this study provides very high spectral resolution at less than 1 nm, but has a limited capability to identify ground objects.…”

“…Due to its high spatial resolution and small influence of atmospheric factors, close‐range UAV‐based spectroscopy has shown large potential to capture the field‐scale variability of SOC and to aid better and more targeted agricultural management. UAV‐based spectroscopy can be based on multispectral camera (Aldana‐Jague et al, 2016; Biney et al, 2021) or hyperspectral imagery (Laamrani et al, 2019). However, at present these approaches require massive soil sampling efforts for the development of local spectroscopic prediction models, and this remains an important bottleneck (Aldana‐Jague et al, 2016; Brown et al, 2005).…”

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

“…Zhou et al [36], Zhang et al [43], and Paul et al [44] used land use type as a variable to predict SOC content, but the results showed that it did not contribute much to the model. In recent studies, remote sensing data were used to predict SOC content based on multiple land use types, while the results showed that the accuracy of SOC prediction using satellite remote sensing data was lower than that using airborne remote sensing and laboratory reflectance spectra data [19,45]. Therefore, it is possible to improve the prediction accuracy of SOC based on satellite remote sensing data under a single land use type.…”