Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data

Abstract

Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

“…The availability of a sufficient number of LUCAS soil samples within the study area and their wide spatial distribution ( Figure 1) together with the large variability in terms of SOC content (Table 1) allowed calibrating reliable SOC models [9]. The only drawback in the LUCAS database is that it is based on a stratified random sampling strategy in geographical space (rather than feature space) [6].…”

“…Until now, remote sensing or airborne spectroscopy was used for small areas that are mostly rather homogeneous in parent material and soil forming factors [30]. Castaldi et al [9] demonstrated that sampling strategies based on the feature space, where the spectral bands were used as ancillary data, were most efficient when the Demmin area was stratified, according to 'soil scapes,' i.e., distinguishing sandy and clay topsoil (Figure 1). Other factors that are likely to improve the SOC prediction models are the absence of a soil crust (since it does not represent the spectral signal of the topsoil [30] and the large variability in SOC content).…”

“…One of these is the choice of the sampling strategy for a calibration dataset that is representative of the SOC variability of the investigated area. Castaldi et al [9] demonstrated that Sentinel-2 spectral data can support algorithms to select the sample locations that ensure a wide range of SOC content. However, a new field campaign and the related soil chemical analysis are time-consuming and expensive in comparison to using an existing soil spectral library.…”

Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

“…Sentinel-2 satellites have a 5-day revisit cycle, increasing the chance of obtaining cloud-free images at the necessary crop growth stage for the study sites. There has been much research interest in the use of Sentinel-2 data in agriculture to study crop nutrient status, biophysical variables and soil mapping in the local, regional and global scale [23][24][25][26]. There is also a potential to map sub-paddock variability and provide an indication of crop yield before harvest using Sentinel-2 data.…”

Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method

“…Furthermore, the reliability of some measures is increasingly being undermined [19]. Even more important is to locate reference polygons so that they represent all types of ecosystems occurring in the studied area [20].…”