There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet [1
]. It has been answered with a growing number of soil policies and regulations around the world concerned with, e.g., increasing soil degradation and loss of organic carbon in top soils, and aiming at more soil management and soil protection. Soil scientists are being challenged to provide assessments of soil conditions from local to global scales [3
]. However, only a few countries have the necessary survey and monitoring programs to meet these new needs and existing global datasets are out-of-date. A particular issue is the clear demand for a new regional to global coverage with accurate, up-to-date, and spatially referenced soil information as expressed by the scientific community, farmers and land users, and policy and decision makers [5
Remote sensing observations offer the possibility of continuous soil mapping and monitoring. They can provide an efficient cost-effective means to determine surface soil composition provided that the soils are exposed at the surface and the technologies are accurate enough to deliver the information needed. The interest in the use of non-invasive sensing methods such as reflectance spectroscopy for the remote determination of mineralogical composition in planetary surfaces has been demonstrated since the 1970s with the development of databases of mineral spectra in the laboratory (e.g., [6
]). Comparatively faster than traditional measuring techniques, spectroscopy can exploit the information carried by reflectance in the visible and near-infrared (VNIR: 400–1100 nm) and shortwave infrared (SWIR: 1100–2500 nm) part of the electro-magnetic spectrum to measure soil properties. Reflectance spectroscopy is an indirect method: soil inference is based on empirical models calibrated by linking spectral data with soil parameters analyzed by reference methods. The main chemical components in soils that interact with electromagnetic radiation, termed chromophores, are in free water OH, clay mineral lattice, organic matter, and non-clay minerals, such as iron oxides, carbonates, and salts [7
]. Soils are complex mixtures of components producing poorly defined spectra due to numerous absorptions that are often weak, overlapping and interacting with each other causing masking and shifting effects. Hence, spectral unmixing techniques such as those used for determining mineral abundances [9
] cannot simply be used to determine soil composition and therefore predictions are often obtained through multivariate statistics. Early studies, such as [10
], started to produce soil property predictions based on multivariate statistics. Since then, most of the studies adopted similar multivariate statistics quantitative approaches and spectroscopy has been exploited in the laboratory to predict soil properties such as organic carbon [12
], and texture [13
]. As a consequence, the prediction of soil properties based on spectroscopy showed a tremendous increase in the last decades [14
]. Recent reviews ([15
]) listed soil properties that could be determined by means of diffuse reflectance spectroscopy including soil water content, clay, sand, soil organic carbon (SOC), Cation Exchange Capacity (CEC), exchangeable Ca and Mg, total N, pH, and showed that soil spectroscopy has the potential to aid and supplement soil survey. Nowadays soil spectroscopy has become a recognized laboratory method presented as an alternative to wet chemistry for soil monitoring [17
At the remote sensing scale, imaging spectroscopy using airborne sensors has shown the potential to map and quantify topsoil properties in many studies [18
], the most successful ones being the applications for soil properties that are directly related to the chromophores such as iron oxides, clay, SOC. Alike with laboratory approaches, the Partial-Least-Square (PLS) approach was the most often used tool in the past decade to predict quantitative surface soil properties from imaging spectroscopy data (see, e.g., [19
]). Most studies were successful at local scale, when the soils were exposed at the surface and vegetation cover and moisture content were low. Upcoming spaceborne sensors are currently being built like EnMAP [26
] from Germany and HISUI from Japan both planned to be launched in 2019. SHALOM, a joint initiative of Italy and Israel, HypXIM from France and HypsIRI from the USA are in the design phase. The upcoming availability of these high signal-to-noise ratio spaceborne imaging spectroscopy data is expected to provide a major step toward the operational quantitative monitoring of soil surfaces at large scales. Indeed, these instruments could therefore provide global spectroscopic data for mapping quantitative soil properties at low costs, and could allow accurate assessment and monitoring of soil erosion such as e.g., carbon loss or increase when degradation processes or recultivation effects in soils are present. In comparison to existing satellite sensors, for which current initiatives for global soil mapping already exists (such as MODIS Africa [27
], and ASTER Australia [28
]), imaging spectroscopy would allow to derive more identification of mineralogy and more quantitative soil products. Nonetheless, advances are still necessary to fully develop imaging spectroscopy soil products that can support, in a credible manner, global digital mapping and monitoring of soils. The expected potential of future hyperspectral satellites has to be demonstrated in case studies, and limitations in the current methodologies for global quantitative determination of soil properties have to be overcome. These limitations include: PLS models that need manual fine-tuning, use of non transferable local/regional soil models, the need of local ground truth databases, and also the effects of noise (vegetation, moisture, roughness, etc.). For this, recent studies looked at the potential of the future EnMAP sensor for local land cover and vegetation mapping based on simulated EnMAP data [29
], and one study looked at the potential of future sensors for soil properties mapping based on noise- and spatially-degraded spectral images enhancing the effects of spatial scale resolution [31
]. In addition, few studies looked at the issue of the operationability of the predictions linked with harmonized methodologies for applications at regional to global scale [32
], or at the issue of whether many local/regional soil prediction models or a global model could be set up [36
]. The issue of the capability of future hyperspectral sensors for soil properties mapping using a full satellite simulator and semi-operational methodologies has never been tackled and the accuracy of the soil products that can be achieved in such a case has never been evaluated.
In this context, the main objective of this paper is to test and validate the capability of upcoming spaceborne imaging spectroscopy in comparison to airborne imaging spectroscopy in case studies for the quantitative prediction of common soil properties using state-of-the-art semi-operational methodologies. The central aspects of this paper are: (a) iron oxide, clay and SOC determination were selected as studied soil properties because they are important as they relate to soil fertility and soil structure, and the expected feasibility of their prediction based on optical remote sensing has also already been demonstrated at the airborne scale; (b) the EnMAP sensor is taken as a representative of future high quality spaceborne sensor, for which a simulator has already been developed, taking into account sensor spectral, radiometric, and spatial characteristics, flight and illumination conditions; (c) an automated PLS approach is used that does not include fine-tuning and is representative of more operational processing procedures for upcoming global applications; and (d) the evaluation of the prediction accuracy is in focus with using conventional error measures but also uncertainty measures, and a spatial structure analysis is included to assess the spatial distribution of the soil maps at spaceborne (30 m) compared to airborne scale (2.6 and 4.5 m). The test sites, ground truth data and associated imaging spectroscopy datasets including EnMAP simulations are presented in Section 2.1
. The description of the processing methodology of the paper is presented in Section 2.2
including the processing workflow, autoPLSR procedure, model performance statistics, and spatial structure analysis using semivariograms to evaluate the coherence of the spatial mapping. The resulting soil maps, spectroscopic models, and semivariogram analysis are presented in Section 3
and subsequently discussed.
Soil spectroscopy based on laboratory, field, and airborne data was shown to be an adequate method for the mapping of the spatial distribution of soil surface properties such as iron oxide, clay, and SOC content, moving into the quantitative domain based on multivariate statistics methodologies, as long as soil chromophores are present, the soils are well exposed and homogeneously distributed, and local ground data are available. In this paper, the potential of spaceborne spectroscopy data for the delivery of soil products is investigated. Nowadays, PLS approaches applied to soil spectroscopy have been recognized for its potential to deliver fast and low-cost high quality geo-referenced soil maps for the assessment of soil properties and for soil degradation indicators, and are used here.
With the upcoming launch of the next generation of imaging spectroscopy sensors (e.g., EnMAP, HISUI, HyspIRI, HypXIM, and SHALOM), a major step is expected towards global soil surface mapping from space using imaging spectroscopy. Nevertheless, in the frame of the preparation program for the EnMAP satellite mission to be launched in 2019 with more than 240 spectral bands covering the VNIR-SWIR at a pixel size of 30 m and a high signal-to-noise ratio, a central question at the forefront of research is the potential of the upcoming sensor system for surface soil properties mapping including the expected accuracy.
This paper presents a first study focusing on the test and validation of the EnMAP sensor for the quantitative spatial mapping of iron oxide, clay, and SOC content in semi-arid Mediterranean Spain and temperate area in Luxembourg. An emphasis is placed on the use of semi-operational method (autoPLSR approach in the EnMAP-Box), the evaluation of prediction accuracy, the use of conventional error measures but also uncertainty measures, and spatial structure analyses comparing airborne with spaceborne systems.
The overall results show that EnMAP data derived soil models are able to predict iron oxide, clay, and SOC with an R2
of the validation dataset between 0.53 and 0.67 compared to airborne imagery with R2
of the validation dataset between 0.64 and 0.74. The correlation between EnMAP and airborne imagery prediction results is in all cases higher than a Pearson coefficient of correlation of 0.86.
Although the slight decrease in prediction model performance, the spatial distribution of the soil properties is in general coherent between the simulated EnMAP and the airborne mapping.
The variance contributor analysis and semivariograms show a highlighted importance of resolution adapted sampling strategies for the simulated EnMAP case. Adapting to this can potentially increase the performance of future multivariate models.
The analyses of the variograms show that spatial structures predicted based on simulated EnMAP are well representative of the predicted spatial structures based on the airborne imagery with systematically lower calculated semivariance (averaging effect). The differences between EnMAP and airborne mapping are associated with heterogeneous areas where much finer detail and local variations are present in airborne soil maps and mixed pixels at EnMAP scale cannot represent variations at very small scale.
The shape of the semivariograms is coherent with local conditions for SOC and clay (crop fields, and geomorphic unit).
The automatic PLS procedure included in the EnMAP-Box is adequate to derive good soil prediction models which perform in an expected range (with RPIQ > 2.2 for the airborne data) and might be suitable for model building in an operational environment as long as adequate ground truth data are available.
This paper was a first example concerning case studies from two different soil environments using semi-operational multivariate statistics for the quantitative prediction of soil properties using simulated EnMAP satellite imagery. In general, this work demonstrates the high potential of upcoming spaceborne hyperspectral missions for soil science studies but has also shown the need for future adapted strategies to cope with the lower spatial resolution. Nevertheless, compared with airborne soil maps at much finer scale, simulated EnMAP images at 30 m scale with good spectral resolution and estimated signal-to-noise ratio similar to sensor tests were able to deliver regional soil maps that are coherent with previous analyses in the region.
Other factors that influence the prediction accuracy (e.g., spectral noise like atmosphere, surface roughness, sensor noise and illumination) are inherently included in error measures used and should be considered. We carried out a variance analysis to at least distinguish between modeling and data errors. The analysis showed that around 70%–80% of the variance of the results is due to uncertainties in the spectral data itself.
Further work should focus on the strategy to cope with degraded satellite signal compared to airborne hyperspectral imagery including field effects and the larger spatial resolution by developing adapted ground sampling strategies for independent validation of the soil models. In particular, more developments are needed on the methodological approaches to check the suitability of current and future improved soil algorithms for global soil mapping, and look at the availability of adequate methodologies for soil model building using appropriate databases for model calibration. One main avenue of research concerns the use of recently available regional and global soil spectral databases to calibrate the soil spectral models and further develop the capabilities for operational quantitative soil mapping from space.