Sabine Chabrillat

Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution

Hyperspectral images were acquired along the Front Range Urban Corridor in Colorado to determine the feasibility of identification and mapping of expansive clay soils with two, high-SNR imaging spectrometers. Swelling soils are a major geologic hazard, and cause extensive damage world-wide every year. The cost of postconstruction mitigation and standard engineering soil tests for creation of regional maps are immense. Smectite is the clay mineral group that has the greatest swelling potential and is responsible for most of the severe swelling soil damage observed in Colorado. Data sets were acquired from 1997 to 1999 with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Hyperspectral Mapper (HyMap). Using a matched filtering algorithm, maps of exposed clay material were produced, despite a strong vegetation cover. Among those exposures, spectral discrimination and identification of variable clay mineralogy such as smectite, smectite/illite, and kaolinite, in decreasing order of swelling potential hazard, was possible. The comparison of the results from the two sensors showed that higher spatial resolution provided purer image endmembers in more heterogeneous sites, but did not exhibit more endmembers and did not identify new natural outcrops that a lower spatial resolution data set would miss in a homogeneous terrain. However, an increase in the signal-to-noise ratio (SNR) of the instrument by pixel summation made possible the identification of low reflectance exposures. This work demonstrates that, using recent instruments and well-established methodologies, imaging spectrometry can be of practical help for the detection and mapping of expansive clays.

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R 2=0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R 2=0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.

Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing

Abstract Important environmental parameters in arctic periglacial landscapes (i.e. permafrost temperature, active-layer depth, soil moisture, precipitation, vegetation cover) will very likely change in a warming climate. The thawing of permafrost, especially, might cause massive landscape changes due to thermokarst and an enhanced release of greenhouse gasses from the large amounts of carbon stored in frozen deposits, resulting in positive climate-warming feedback. For the identification, mapping, and quantification of such changes on various scales up to the entire circum-Arctic, remote sensing and spatial data analysis are essential tools. In this study an extensive field-work dataset including spectral surface properties, vegetation, soils, and geomorphology was acquired in the largest Arctic delta formed by a single river, the Siberian Lena River Delta. A portable field spectrometer (ASD FieldSpec Pro FR®) was used for spectral surveys of terrain surfaces, and optical satellite data (Landsat Enhanced Thematic Mapper (ETM+), CHRIS-Proba) were used for the characterization, manual mapping, and automatic classification of typical periglacial land-cover units in the Lena Delta. Qualitative data from soils, vegetation, soil moisture, and relief units were correlated with the field-spectral data and catalogued for a wide variety of surface types. The wide range of micro- and meso-scale variations of periglacial surface features in the delta results in distinctive spectral characteristics for different land-cover units. The three main delta terraces could also be spectrally separated and characterized. The present dataset provides a basis for further spectral data acquisitions in the Lena Delta and for comparisons with periglacial surfaces from other regions.

Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 μm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (μFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4–2.5 μm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils’ chemical and physical composition. The study revealed the potential of the TIR (for clay: R2 = 0.93, RMSEP = 0.66% and for sand: R2 = 0.93, RMSEP = 0.82%) and its combination with the solar reflective region (for organic carbon: R2 = 0.95, RMSEP = 0.04%) for retrieving soil properties in typical soils of semi-arid regions. The models’ drivers confirmed the opto-physical base of most of the soils’ constituents (clay minerals, silicates, iron oxides), and emphasizes the TIR’s advantage for soils with compositions dominated by quartz and kaolinite.

Quantitative Soil Spectroscopy

1 Section of Remote Sensing, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany 2The Remote Sensing Laboratory, Department of Geography and Human Environment, Tel-Aviv University, P.O. Box 39040, Ramat Aviv, 69978 Tel-Aviv, Israel 3 Soil and Landscape Program, CSIRO Land and Water, Bruce E. Butler Laboratory, Clunies-Ross Street Black Mountain, P.O. Box 1666, Canberra, ACT 2601, Australia 4 Soil Science Department, Luiz de Queiroz College of Agriculture, Sao Paulo, University of Piracicaba, SP 13418-900, Brazil

Mineralogy-swelling potential relationships for expansive shales

This study examines the extent to which mineralogy and swelling potential can be correlated in the expansive clays and shales along the Colorado Front Range Urban Corridor. 182 undisturbed samples were collected from 20 sites from Boulder to Pueblo. Sites were selected in Cretaceous shales, including the Pierre Shale, that have been uplifted into steeply dipping strata near the foothills of the Rocky Mountains, and that are well known to be hazardous to residential and light commercial developments in this region. For each sample, mineralogy was determined by x-ray diffraction and swelling potential was obtained from moisture content (w), suction (h), and clod volume (V) measurements in terms of the suction potential (dh/dw) and suction-compression index (dV/dh) parameters used in the classification scheme McKeen proposed in 1992. Swelling potentials were also obtained on more limited suites of samples with conventional and labor-intensive schemes including Seed and Chens schemes based on tests for grain-size distribution and Atterberg limits, and with swell-consolidation measurements in response to saturation, consolidation, and rebound in an oedometer. The results show the percent total smectite provides a useful index of the swelling potential concept defined by Seed and correlates reasonably well with the swelling potential indices developed by Seed, Chen, and McKeen. However, the percent total smectite does not correlate well with conventional swell-consolidation test indices. The causes for this lack of correlation appear to be placement and environmental factors such as the initial moisture content, stress history, and surcharge loading that are beyond the scope of Seeds swell potential concept and Seed, Chen, and McKeens swelling potential indices.

Development of land degradation spectral indices in a semi-arid Mediterranean ecosystem

The goal of this study is to develop remote sensing desertification indicators for drylands, in particular using the capabilities of imaging spectroscopy (hyperspectral imagery) to derive soil and vegetation specific properties linked to land degradation status. The Cabo de Gata-Nijar Natural Park in SE Spain presents a still-preserved semiarid Mediterranean ecosystem that has undergone several changes in landscape patterns and vegetation cover due to human activity. Previous studies have revealed that traditional land uses, particularly grazing, favoured in the Park the transition from tall arid brush to tall grass steppe. In the past ~40 years, tall grass steppes and arid garrigues increased while crop field decreased, and tall arid brushes decreased but then recovered after the area was declared a Natural Park in 1987. Presently, major risk is observed from a potential effect of exponential tourism and agricultural growth. A monitoring program has been recently established in the Park. Several land degradation parcels presenting variable levels of soil development and biological activity were defined in summer 2003 in agricultural lands, calcareous and volcanic areas, covering the park spatial dynamics. Intensive field spectral campaigns took place in Summer 2003 and May 2004 to monitor inter-annual changes, and assess the landscape spectral variability in spatial and temporal dimension, from the dry to the green season. Up to total 1200 field spectra were acquired over ~120 targets each year in the land degradation parcels. The targets were chosen to encompass the whole range of rocks, soils, lichens, and vegetation that can be observed in the park. Simultaneously, acquisition of hyperspectral images was performed with the HyMap sensor. This paper presents preliminary results from mainly the field spectral campaigns. Identifying sources of variability in the spectra, in relation with the ecosystem dynamics, will allow the definition of spectral indicators of change that can be used directly to derive the desertification status of a land.

Research opportunities for studying land degradation with spectroscopic techniques

Desertification is a land degradation problem of major importance in the arid regions of the world. Deterioration in soil and plant cover have adversely affected nearly 70 percent of the drylands as mainly the result of human mismanagement of cultivated and range lands. Overgrazing, woodcutting, cultivation practices inducing accelerated water and wind erosion, improper water management leading to salinisation, are all causes of land degradation. In addition to vegetation deterioration, erosion, and salinisation, desertification effects can be seen in loss of soil fertility, soil compaction, and soil crusting. Combating desertification involves having an accurate knowledge on a current land degradation status and the magnitude of the potential hazard. We present here a new project that aims at deriving a global simplified Land Degradation Index (LDI) from hyperspectral remote sensing data. Indeed, specific soil properties directly linked to soil degradation status, such as chemical properties, organic matter content, mineralogical content, soil crusting, and runoff, as well as vegetation content and degradation status, could be derived from high-spectral resolution imagery. Then, global maps assessing drylands desertification status could be routinely developed. This paper, after a brief review of land degradation processes and assessment, discusses the capabilities of hyperspectral imagery for land degradation assessment.

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

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.