In this study, the performances of hyperspectral airborne and superspectral spaceborne spectral imaging to derive selected Soil Organic Carbon (SOC) were analyzed and compared in agricultural sites of the Czech Republic. The main aim was to assess the potential of superspectral Sentinel-2 satellite for the prediction and mapping of the attribute. The prediction accuracy based on airborne and spaceborne techniques in majority of the sites was adequate for SOC. Comparing the spatial distribution maps of SOC derived from the airborne and spaceborne data showed a similar trend at both platforms. The SOC maps also confirmed that in areas with a high level of SOC, Sentinel-2 was able to detect SOC even more precisely than the airborne sensors. Although a decrease in the model and map performances was obvious in the case of parameters with low contents. The findings of the current research showed that superspectral Sentinel-2 allows for the estimation and mapping of SOC. The study also emphasized the importance of the superspectral Sentinel-2 data in soil characteristics assessment with a frequent revisit-time over larger areas than it currently is with laboratory and airborne instruments. Certainly, the repeatability of the Sentinel-2 products is still a work in progress and with the Sentinel-2B, a revisit-time of five-day and the temporal frequency of cloud-free acquisitions will be further increased. Accordingly, much more data will be freely available in the near future, which will have a significant influence on the obtaining of high-quality soil data.
The ability of obtaining soil properties estimations from time and cost efficient remotely sensed techniques has been identified as a valuable technique as there is a great demand for larger amounts of good quality and inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Visible (Vis) and Near Infrared (NIR) spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis. The aim of this paper is to evaluate the abilities of Vis (350-700 nm) and near infrared (700-2500 nm) for prediction of soil nutrients. In this instance we implemented Savitzky-Golay algorithm and Stepwise Multiple Linear Regression (SMLR) to construct calibration models. The soil nutrients examined were soil Total Nitrogen (N), Available Phosphorus (P) and Exchangeable Potassium (K). Our results revealed the accuracy of SMLR prediction in each of the Vis and NIR spectral regions. The NIR produced more accurate predictions for N and K; however, higher significant correlation was obtained using the Vis for available P. This work demonstrated Vis and NIR spectroscopy could be considered as a good tool to assess soil nutrients in Malaysian paddy fields.
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