Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data

Abstract

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

“…Since 2019, the PRecursore IperSpettrale della Missione Applicativa (PRISMA) with 239 spectral bands between 400 and 2505 nm has delivered images with 30 m resolution [30]. Approaches of satellite-based SOC modeling have been carried out from Hyperion [31][32][33], CHRIS-PROBA [34], simulated PRISMA [35] and PRISMA [36]. As the Environmental Mapping and Analysis Program (EnMAP) [37] was just launched on 1 April 2022 and is currently in the commissioning phase, some studies have considered simulated EnMAP for the assessment of SOC content [38,39] till actual EnMAP data becomes available.…”

“…Since 2015 and then 2017, when Sentinel-2A, followed by Sentinel-2B were launched, the Sentinel-2 (S2) time-series equipped with the MultiSpectral Instrument (MSI, 13 spectral bands) provided not only wide spatial coverage over swaths of 290 km, but also 10 to 20 m resolution (10 spectral bands) and a 5-day revisit. The advent of such time-series favored the renewal of the satellite-derived spectral models and particularly for SOC, using either single date acquisitions [44,[47][48][49][50][51][52][53][54][55] or multi-date approaches [36,[56][57][58][59]. In addition, some authors used Sentinel-1 synthetic aperture radar (SAR) images in their approach, either separately [50,55,58] or directly as covariates within their modeling [55,60].…”

“…20 m resolution (10 spectral bands) and a 5-day revisit. The advent of such time-series favored the renewal of the satellite-derived spectral models and particularly for SOC, using either single date acquisitions [44,[47][48][49][50][51][52][53][54][55] or multi-date approaches [36,[56][57][58][59]. In addition, some authors used Sentinel-1 synthetic aperture radar (SAR) images in their approach, either separately [50,55,58] or directly as covariates within their modeling [55,60].…”

“…SVM has been reported by Diaz-Uriarte and de Andrés [117] to return comparable results to RF models. The cubist technique was also applied to satellite images in some recent studies predicting SOC [36,57,59,81]. It is a form of the piecewise linear decision tree that partitions the response data into subsets with which their characteristics are similar with respect to the prediction [101].…”

“…Figure 5. Plot of performance indicators versus range (top) or standard deviation (bottom) of measured SOC contents for PLSR models[26,31,34,36,44,[48][49][50]54,55,58,67].…”