Description: The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability.

Description: Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.

Description: The sampling strategy for mapping soil properties from remote sensing imagery entails making decisions aboutsampling pattern, size and location. The availability of a consistent number of ancillary data strongly related to thetarget variable allows applying sampling strategies that optimally cover the feature space. This study aims at eval-uating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the samplinglocations in order to collect a calibration dataset as basis for multivariate statistical modelling of the Soil OrganicCarbon (SOC) content. Remote sensing spectra can be exploited first to set the sampling strategy and then as inde-pendent variables for the prediction models of the target variables. We tested different sampling strategies based onthe feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellitedata acquired in Demmin (north-Est Germany). Some selection algorithms require to set the number of samples inadvance (random, Kennard-Stones and conditioned Latin Hypercube algorithm) and others automatically providethe ideal number of sampling units (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC contentand the spectra extracted at the sampling locations were used to build random forest (RF) models. The accuracyof the RF estimation models was evaluated on an independent dataset. The highest Sentinel-2 ratio of performanceto deviation (RPD) for the validation set was obtained using Puchwein (RPD: 2.5), and Kennard-Stones (RPD:2.4) algorithm. A strong positive correlation was detected between the standard deviation of the calibration datasetand the validation accuracy. The efficiency of the sampling strategies, as ratio between accuracy and number ofsamples per hectare, is highest using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. The achieved results demonstrated that Sentinel-2 and EnMAP data can be exploited to build a reliablecalibration dataset for SOC mapping; moreover the efficiency of the sampling strategy selection can be improvedusing algorithms that provide the number of sampling units. For EnMAP, the different selection algorithms pro-vided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provideda more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided in thiscase lower SOC variability and lower prediction accuracy than compared to Sentinel-2. This was probably due toEnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.

Description: The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing thecapability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the sametime, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), thevariable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regionalscales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parentmaterials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration withsoil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariateregression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lowerthan that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 inLuxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of theairborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions.The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while theimportance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences ofVIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong differencein terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variabilityof the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOCvariability both within field and at regional scale.