Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an Arid Region

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (EC_e), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R^2 of 0.85 and 0.80 for EC_e, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of EC_e, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R^2 = 0.81, 0.89 and 0.73, respectively, compared to R^2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (EC_e), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments

The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy

Laboratory imaging spectroscopy can be used to explore physical and chemical variations in soil profiles on a submillimetre scale. We used a hyperspectral scanner in the 400 to 1000 nm spectral range mounted in a laboratory frame to record images of two soil cores. Samples from these cores were chemically analyzed, and spectra of the sampled regions were used to train chemometric PLS regression models. With these models detailed maps of the elemental concentrations in the soil cores could be produced. Eight different spectral pretreatments were applied to the sample spectra and to the resulting images in order to explore the influence of these pre-treatments on the estimation of elemental concentrations. We found that spectral preprocessing has a minor influence on chemometry results when powerful regression algorithms like PLSR are used

Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field

Short communication: Laboratory imaging spectroscopy of soil profiles

An imaging spectrometer in a laboratory rack was used to examine soil profiles. Images in the 400–1000 nm range wih 4nm spectral resolution and less than 0.1mm spatial resolution of the top 30cm of the soil were acquired. These images can be used to analyse the spatial distribution of chemical and physical soil characteristics and for discrimination and classification of horizons and inclusions. Three-dimensional characterisations of soil properties are possible by recording images of series of parallel slices

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

A timber volume regression model applicable to the state and communal forest area of the federal German state of Rhineland-Palatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellite-based tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model’s performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R2. Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R2). The model achieved an adjusted R2 of 0.48 and a cross-validated root-mean-square error (RMSEcv) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R2 (5% in RMSEcv) compared to the simple model only containing ALS height metrics (adjusted R2=0.36, RMSEcv=51.7%).ISSN:1612-4677ISSN:1612-466

Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopyn : a case study from Egypt

Soil salinization is a progressive soil degradation process that reduces soil quality and decreases crop yields and agricultural production. This study investigated a method that provides improved estimations of soil salinity by using visible and near-infrared reflectance spectroscopy as a fast and inexpensive approach to the characterisation of soil salinity. Soil samples were collected from the El-Tina Plain on the northwestern Sinai Peninsula in Egypt and measured for electrical conductivity (ECe) using a saturated soil-paste extract. Subsequently, the samples were scanned with an Analytical Spectral Devices spectrometer (350-2,500 nm). Three spectral formats were used in the calibration models derived from the spectra and ECe: (1) raw spectra (R), (2) first-derivative spectra smoothened using the Savitzky-Golay technique (FD-SG) and (3) continuum-removed reflectance (CR). The spectral indices (difference index (DI), normalised difference index (NDI) and ratio index (RI)) of all of the band-pair combinations of the three types of spectra were applied in linear regression analyses with the ECe. A ratio index that was constructed from the first-derivative spectra at 1,483 and 1,918 nm with an SG filter produced the best predictions of the ECe for all of the band-pair indices (R-2=0.65). Partial least-squares regression models using the CR of the 400-2,500 nm spectral region resulted in R-2=0.77. The multivariate adaptive regression splines calibration model with CR spectra resulted in an improved performance (R-2=0.81) for estimating the ECe. The results obtained in this study have potential value in the field of soil spectroscopy because they can be applied directly to the mapping of soil salinity using remote sensing imagery in arid regions

Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region

From global monitoring to regional forest management there is an increasing demand for information about forest ecosystems. For border regions that are closely connected ecologically and economically, a key factor is the cross-border availability and consistency of up-to-date information such as the forest type. The combination of existing forest information with Earth observation data is a rational method and can provide valuable contribution to serve the increased information demand on a transnational level. We present an approach for the remote sensing-based generation of a transnational and temporally consistent forest type information layer for the German federal states of Rhineland-Palatinate and Saarland, and the Grand Duchy of Luxembourg. Existing forest information data from different countries were merged and combined with suitable vegetation indices derived from Landsat 8 and Sentinel-2 imagery acquired in early spring. An automated bootstrap-based approximation of the optimum threshold for the distinction of “broadleaved” and “coniferous” forest was applied. The spatially explicit forest type information layer is updated annually depending on image availability. Overall accuracies between 79 and 96 percent were obtained. Every spot in the region will be updated successively within a period of expectably three years. The presented approach can be integrated in fully automated processing chains to generate basic forest type information layers on a regular basis.Peer Reviewe

Permafrost soil complexity evaluated by laboratory imaging Vis‐NIR spectroscopy

The biogeochemical functioning of soils (e.g., soil carbon stabilization and nutrient cycling) is determined at the interfaces of specific soil structures (e.g., aggregates, particulate organic matter (POM) and organo‐mineral associations). With the growing accessibility of spectromicroscopic techniques, there is an increase in nano‐ to microscale analyses of biogeochemical interfaces at the process scale, reaching from the distribution of elements and isotopes to the localization of microorganisms. A widely used approach to study intact soil structures is the fixation and embedding of intact soil samples in resin and the subsequent analyses of soil cross‐sections using spectromicroscopic techniques. However, it is still challenging to link such microscale approaches to larger scales at which normally bulk soil analyses are conducted. Here we report on the use of laboratory imaging Vis–NIR spectroscopy on resin embedded soil sections and a procedure for supervised image classification to determine the microscale soil structure arrangement, including the quantification of soil organic matter fractions. This approach will help to upscale from microscale spectromicroscopic techniques to the centimetre and possibly pedon scale. Thus, we demonstrate a new approach to integrate microscale soil analyses into pedon‐scale conceptual and experimental approaches