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.
Highlights
Soil organic carbon (SOC) content is one of the leading indicators for soil state assessment
For prediction accuracy of SOC, the field spectroscopy was better, the low SOC content within the field makes it difficult to compare the actual performance between Unmanned Aircraft Systems (UAS) and Sentinel-2
The difference was small, the UAS imagery was slightly better than the Sentinel-2 output
Summary
Soil organic carbon (SOC) content is one of the leading indicators for soil state assessment. Numerous drawbacks, including complex and unpredictable environmental conditions, and numerous soil-forming conditions, limit the efficiency and performance of their estimation. Due to these unfavorable factors, the mapping of SOC and its attributes requires time and money [2,3]. There is a global surge toward the need for fast and less costly techniques for efficient quantification of SOC content In response to these challenges, the emersion of proximal soil sensing (PSS) and remote sensing (RS) approaches is described as a useful detection tool for evaluating and analyzing several soil parameters including SOC [4,5,6]. A physical contact is needed to obtain signal from the target using the spectrometer sensor (within 2m apart) [7]
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