Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel (original) (raw)
Related papers
2012
The potential of the visible-near infrared (Vis-NIR; 400-2500 nm) laboratory spectroscopy for the estimation of soil properties has been previously demonstrated in the literature, and the Vis-NIR spatial spectroscopy is expected to provide direct estimates of these properties at the soil surface. The aim of this work was to examine whether Vis-NIR airborne spectroscopy could be used for mapping eight of the most common soil properties, including clay, sand, silt, calcium carbonate (CaCO 3 ), free iron, cation-exchange capacity (CEC), organic carbon and pH, without mispredicting the local values of these properties and their spatial structures. Our study was based on 95 soil samples and a HyMap hyperspectral image available over 192 bare soil fields scattered within a 24.6 km² area. Predictions of soil properties from HyMap spectra were computed for the eight soil properties using partial least squares regression (PLSR). The results showed that 1) four out of the eight soil properties (CaCO 3 , iron, clay and CEC) were suitable for mapping using hyperspectral data, and both accurate local predictions and good representations of spatial structures were observed and 2) the application of prediction models using hyperspectral data over the study area provided statistical characterizations within soilscape variations and variograms that describe in details the short range soil variations. All results were consistent with the previous pedological knowledge of the studied region. This study opens up the possibility of more extensive use of hyperspectral data for digital soil mapping of these successfully predicted soil properties.
Precision Agriculture, 2011
Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R 2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.
Environmental Monitoring and Assessment, 2017
Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomizedsystematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.
Soil mapping at regional scale using ASTER and VNIR spectroscopy
The use of satellite data as a measure of spatial and spectral variability for soil mapping constitutes the link between proximal and remote sensing. This paper proposes a sparse sampling approach which makes use of constrained Latin Hypercube to determine the spatial and spectral variability in soil properties at a regional scale. The sampling approach was successful in representing major variability. In addition, the spectral similarity between field and laboratory spectra was high and therefore the field spectra are suitable for soil property analysis. Of course, vegetation influences the field spectra and therefore it is recommended to select spectra based on low NDVI-values.
Hyperspectral remote sensing as an alternative to estimate soil attributes
REVISTA CIÊNCIA AGRONÔMICA, 2015
Minimizing environmental impacts and increasing crop productivity depend mainly on the knowledge of chemical, physical and mineralogical characteristics of the soil attributes. However, traditional methods are timeconsuming and costly. The objective of this study was to determine and validate a method to quantify soil attributes using UV-Vis-NIR Spectroscopy as an alternative to conventional methods of soil analyses. The work comprised two main phases: (1) creation and calibration of statistical models to determine the soil attributes derived from spectral data extracted from soil samples collected in area 1, (2) validation of statistical models in area 2 and correlations between the estimated and observed values (conventional method) for each soil attribute. The equations of the attributes Fe 2 O 3 , Al 2 O 3 , and clay reached R 2 > 0.80 and may be applied to a different database than the one that was used to generate the equations, provided that they belong to the same study site.
Geoderma, 2005
Efficient tools to measure within-field spatial variation in soil are important when establishing agricultural field trials and in precision farming. The object of the study was to investigate if a combination of two techniques, principal component analysis (PCA) and geostatistics, could reveal spatial soil variation from near infrared reflectance (NIR) spectroscopy data and thereby replace more conventional, viz. laborious and expensive, soil analyses. NIR spectrum is known to reveal information about important soil chemical, physical and biological properties and has been used in soil science for years. Three soil variables, total carbon (Tot-C), clay content and pH, were used as reference variables. The study was carried out on one site (200Â160 m) in eastern Sweden with a Eutric Cambisol soil type where a sampling grid of 20Â20 m was established. From the grid nodes, 99 samples were collected to a depth of 10 cm. The soil was analyzed by NIR and the data were decomposed by PCA. The first two principal components (PC 1 and PC 2) explained 85% of the total variance and therefore these two PCs were selected for further assessment of spatial variation by variography and kriging. PC 1 showed the strongest spatial dependence with a range of 148 m and a nugget close to zero. The variogram for PC 1 was robust and the kriging map expressed a clear pattern. The range of spatial correlation varied between the three reference soil variables. Tot-C expressed a low spatial dependence with a high proportion of nugget, whereas clay content and pH expressed spatial dependence at a range of 54 and 46 m, respectively. Neither of the traditional soil variables showed as strong spatial dependence as PC 1 of NIR. The advantage of the NIR-PCA strategy is that the first PCs will capture the spectral bands that express the largest variation regardless of what the NIR bands correlate to and, hence, PC 1 will always explain the variation of the soil properties that in each specific case have the largest influence on the PCA model. In conclusion, the NIR-PCA strategy seems to be an efficient and reliable strategy to use when determining the soil spatial variation in a field.
Journal of Arid Environments, 2010
Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25 Â 25 m sampling grid over a 32 ha (800 Â 400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and/or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0.71), but also high bias (-414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0.76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database. Finally, the clay predicted map obtained with standardisation using the Focused-Legacy database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.
Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, São Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500 nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy cen-troids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r 2 of 0.92, and a satisfactory r 2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R 2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.
Evaluating near infrared spectroscopy for field prediction of soil properties
This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district,~7 km 2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0-0.10 and 0.40-0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.