Remote Sensing

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2641 KiB

Open AccessArticle

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

by Érika F. M. Pinheiro, Marcos B. Ceddia, Christopher M. Clingensmith, Sabine Grunwald and Gustavo M. Vasques

Remote Sens. 2017, 9(4), 293; https://doi.org/10.3390/rs9040293 - 23 Mar 2017

Cited by 119 | Viewed by 9711

Abstract

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest [...] Read more.

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350–2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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5362 KiB

Open AccessArticle

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

by Yayong Sun, Shifeng Huang, Jianwei Ma, Jiren Li, Xiaotao Li, Hui Wang, Sheng Chen and Wenbin Zang

Remote Sens. 2017, 9(3), 292; https://doi.org/10.3390/rs9030292 - 20 Mar 2017

Cited by 25 | Viewed by 6530

Abstract

The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely [...] Read more.

The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm³·cm⁻³ and 0.039 cm³·cm⁻³ based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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2906 KiB

Open AccessArticle

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

by Veronika Kopačková, Eyal Ben-Dor, Nimrod Carmon and Gila Notesco

Remote Sens. 2017, 9(2), 134; https://doi.org/10.3390/rs9020134 - 6 Feb 2017

Cited by 34 | Viewed by 6213

Abstract

The study tested a data mining engine (PARACUDA^®) to predict various soil attributes (BC, CEC, BS, pH, C_org, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed [...] Read more.

The study tested a data mining engine (PARACUDA^®) to predict various soil attributes (BC, CEC, BS, pH, C_org, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R²), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations). Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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2043 KiB

Open AccessArticle

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

by Marcos B. Ceddia, Andréa S. Gomes, Gustavo M. Vasques and Érika F. M. Pinheiro

Remote Sens. 2017, 9(2), 124; https://doi.org/10.3390/rs9020124 - 3 Feb 2017

Cited by 22 | Viewed by 6264

Abstract

Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has [...] Read more.

Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4914 KiB

Open AccessArticle

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

by Xuefei Zhang, Tingting Zhang, Ping Zhou, Yun Shao and Shan Gao

Remote Sens. 2017, 9(2), 104; https://doi.org/10.3390/rs9020104 - 25 Jan 2017

Cited by 79 | Viewed by 27702

Abstract

Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they [...] Read more.

Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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6562 KiB

Open AccessArticle

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

by Huizeng Liu, Tiezhu Shi, Yiyun Chen, Junjie Wang, Teng Fei and Guofeng Wu

Remote Sens. 2017, 9(1), 29; https://doi.org/10.3390/rs9010029 - 3 Jan 2017

Cited by 34 | Viewed by 6177

Abstract

Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values [...] Read more.

Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4832 KiB

Open AccessArticle

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

by Daniel Žížala, Tereza Zádorová and Jiří Kapička

Remote Sens. 2017, 9(1), 28; https://doi.org/10.3390/rs9010028 - 1 Jan 2017

Cited by 58 | Viewed by 10941

Abstract

The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in [...] Read more.

The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in recent years. In our study, we bring an approach for assessment of soil degradation by erosion by means of determining soil erosion classes representing soils differently influenced by erosion impact. The adopted methods include extensive field sampling, laboratory analysis, predictive modelling of selected soil surface properties using aerial hyperspectral data and the digital elevation model and fuzzy classification. Different multivariate regression techniques (Partial Least Square, Support Vector Machine, Random forest and Artificial neural network) were applied in the predictive modelling of soil properties. The properties with satisfying performance (R² > 0.5) were used as input data in erosion classes determination by fuzzy C-means classification method. The study was performed at four study sites about 1 km² large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The influence of site-specific conditions on prediction of soil properties and classification of erosion classes was assessed. The prediction accuracy (R²) of the best performing models predicting the soil properties varies in range 0.8–0.91 for soil organic carbon content, 0.21–0.67 for sand content, 0.4–0.92 for silt content, 0.38–0.89 for clay content, 0.73–089 for Fe_ox, 0.59–0.78 for F_ed and 0.82 for CaCO₃. The performance and suitability of different properties for erosion classes’ classification are highly variable at the study sites. Soil organic carbon was the most frequently used as the erosion classes’ predictor, while the textural classes showed lower applicability. The presented approach was successfully applied in Chernozem and Luvisol loess regions where the erosion classes were assessed with a good overall accuracy (82% and 67%, respectively). The model performance in two Cambisol/Stagnosol regions was rather poor (51%–52%). The results showed that the presented method can be directly and with a good performance applied in pedologically and geologically homogeneous areas. The sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data. The future application of presented approach at a regional scale promises to produce valuable data on actual soil degradation by erosion usable for soil conservation policy purposes. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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1917 KiB

Open AccessArticle

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

by Morteza Sadeghi, Alireza Tabatabaeenejad, Markus Tuller, Mahta Moghaddam and Scott B. Jones

Remote Sens. 2017, 9(1), 17; https://doi.org/10.3390/rs9010017 - 28 Dec 2016

Cited by 43 | Viewed by 10616

Abstract

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the [...] Read more.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4291 KiB

Open AccessArticle

Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction

by Lanfa Liu, Min Ji, Yunyun Dong, Rongchung Zhang and Manfred Buchroithner

Remote Sens. 2016, 8(12), 1035; https://doi.org/10.3390/rs8121035 - 19 Dec 2016

Cited by 27 | Viewed by 6940

Abstract

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a [...] Read more.

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R²) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R² = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R² = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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7110 KiB

Open AccessArticle

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

by Sanne Diek, Michael E. Schaepman and Rogier De Jong

Remote Sens. 2016, 8(11), 906; https://doi.org/10.3390/rs8110906 - 2 Nov 2016

Cited by 43 | Viewed by 6470

Abstract

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by [...] Read more.

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R² and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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5380 KiB

Open AccessArticle

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

by José A. M. Demattê, Leonardo Ramirez-Lopez, Rodnei Rizzo, Marcos R. Nanni, Peterson R. Fiorio, Caio T. Fongaro, Luiz G. Medeiros Neto, José L. Safanelli and Pedro Paulo Da S. Barros

Remote Sens. 2016, 8(10), 826; https://doi.org/10.3390/rs8100826 - 8 Oct 2016

Cited by 13 | Viewed by 6396

Abstract

There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating [...] Read more.

There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400–2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R² 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R² > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since colors went from dark blue to light purple, and were related with soil texture and mineralogy of the region. The comparison between the traditional and digital soil maps showed a global accuracy of 69% for the TFS-1 map and 62% in the TFS-2, with kappa indices of 0.52 and 0.45, respectively. We randomly validated both digital and traditional maps with individual plots at field. We achieve a 75% and 80% agreement for digital and traditional maps, respectively, which allows us to conclude that traditional map is not necessarily the truth and digital is very close. The key of the strategy was to use bare soil image as a first step on the indication of soil variation in the area, indicating in-situ location for sample collection in all depths. The current strategy is innovative since we linked sensors from ground to space in addition with relief parameters and spectral libraries. The strategy indicates a more accurate map with less soil samples and lower cost. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4782 KiB

Open AccessArticle

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

by Nima Ahmadian, José A. M. Demattê, Dandan Xu, Erik Borg and Reinhard Zölitz

Remote Sens. 2016, 8(9), 738; https://doi.org/10.3390/rs8090738 - 9 Sep 2016

Cited by 12 | Viewed by 7826

Abstract

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These [...] Read more.

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIR_min method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R² = 0.94) are similar to those of the simulated SL. The correlation coefficient (R²) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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9555 KiB

Open AccessArticle

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

by Danfeng Sun and Wanbei Jiang

Remote Sens. 2016, 8(9), 714; https://doi.org/10.3390/rs8090714 - 31 Aug 2016

Cited by 6 | Viewed by 5417

Abstract

This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil [...] Read more.

This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0–20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R² (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R² = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0–20 dS·m⁻¹ and pH of 7–9) at which researchers and policy-makers require near-real-time crop management information. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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3422 KiB

Open AccessArticle

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

by Marilusa P. C. Lacerda, José A. M. Demattê, Marcus V. Sato, Caio T. Fongaro, Bruna C. Gallo and Arnaldo B. Souza

Remote Sens. 2016, 8(9), 701; https://doi.org/10.3390/rs8090701 - 26 Aug 2016

Cited by 46 | Viewed by 8385

Abstract

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil [...] Read more.

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R² = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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3146 KiB

Open AccessArticle

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

by Sérgio Henrique Godinho Silva, Giovana Clarice Poggere, Michele Duarte de Menezes, Geila Santos Carvalho, Luiz Roberto Guimarães Guilherme and Nilton Curi

Remote Sens. 2016, 8(8), 614; https://doi.org/10.3390/rs8080614 - 25 Jul 2016

Cited by 58 | Viewed by 6934

Abstract

Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This [...] Read more.

Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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3556 KiB

Open AccessArticle

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

by Andreas Steinberg, Sabine Chabrillat, Antoine Stevens, Karl Segl and Saskia Foerster

Remote Sens. 2016, 8(7), 613; https://doi.org/10.3390/rs8070613 - 22 Jul 2016

Cited by 77 | Viewed by 8472

Abstract

With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of [...] Read more.

With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fe_d), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R² between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R² between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4775 KiB

Open AccessArticle

Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

by Miriam Pablos, José Martínez-Fernández, María Piles, Nilda Sánchez, Mercè Vall-llossera and Adriano Camps

Remote Sens. 2016, 8(7), 587; https://doi.org/10.3390/rs8070587 - 11 Jul 2016

Cited by 52 | Viewed by 8515

Abstract

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: [...] Read more.

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R

\approx -

0.6 to −0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R

\approx -

0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R

\approx -

0.5 to −0.7; satellite R

\approx -

0.4 to −0.7) indicating SM–LST coupling, than in winter (in situ R ≈ +0.3; satellite R

\approx -

0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ∼0.12 m

^{3}

/m

^{3}

, stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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2260 KiB

Open AccessArticle

Estimation of Forest Topsoil Properties Using Airborne LiDAR-Derived Intensity and Topographic Factors

by Chao Li, Yanli Xu, Zhaogang Liu, Shengli Tao, Fengri Li and Jingyun Fang

Remote Sens. 2016, 8(7), 561; https://doi.org/10.3390/rs8070561 - 1 Jul 2016

Cited by 8 | Viewed by 6048

Abstract

Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and [...] Read more.

Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and available phosphorous (AvaP), is of particular importance for forest development and management. As an emerging technology, light detection and ranging (LiDAR) can capture the three-dimensional structure and intensity information of scanned objects, and can generate high resolution digital elevation models (DEM) using ground echoes. Moreover, great power for estimating forest topsoil properties is enclosed in the intensity information of ground echoes. However, the intensity has not been well explored for this purpose. In this study, we collected soil samples from 62 plots and the coincident airborne LiDAR data in a Korean pine forest in Northeast China, and assessed the effectiveness of both multi-scale intensity data and LiDAR-derived topographic factors for estimating forest topsoil properties. The results showed that LiDAR-derived variables could be robust predictors of four topsoil properties (SOM, Total N, pH, and Depth), with coefficients of determination (R²) ranging from 0.46 to 0.66. Ground-returned intensity was identified as the most effective predictor for three topsoil properties (SOM, Total N, and Depth) with R² values of 0.17–0.64. Meanwhile, LiDAR-derived topographic factors, except elevation and sediment transport index, had weak explanatory power, with R² no more than 0.10. These findings suggest that the LiDAR intensity of ground echoes is effective for estimating several topsoil properties in forests with complicated topography and dense canopy cover. Furthermore, combining intensity and multi-scale LiDAR-derived topographic factors, the prediction accuracies (R²) were enhanced by negligible amounts up to 0.40, relative to using intensity only for topsoil properties. Moreover, the prediction accuracy for Depth increased by 0.20, while for other topsoil properties, the prediction accuracies increased negligibly, when the scale dependency of soil–topography relationship was taken into consideration. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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2876 KiB

Open AccessArticle

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

by Matías Ernesto Barber, Francisco Matías Grings, Jesús Álvarez-Mozos, Marcela Piscitelli, Pablo Alejandro Perna and Haydee Karszenbaum

Remote Sens. 2016, 8(6), 458; https://doi.org/10.3390/rs8060458 - 8 Jun 2016

Cited by 15 | Viewed by 6482

Abstract

The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. [...] Read more.

The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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1787 KiB

Open AccessArticle

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

by Asa Gholizadeh, Luboš Borůvka, Mohammadmehdi Saberioon and Radim Vašát

Remote Sens. 2016, 8(4), 341; https://doi.org/10.3390/rs8040341 - 19 Apr 2016

Cited by 46 | Viewed by 7600

Abstract

Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400–1200 nm) and Short-Wave-Infrared (SWIR, 1200–2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new [...] Read more.

Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400–1200 nm) and Short-Wave-Infrared (SWIR, 1200–2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new Memory-Based Learning (MBL) method performs better than the other methods, namely: Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR) and Boosted Regression Trees (BRT). For this purpose, we chose soil texture (contents of clay, silt and sand) as testing attributes. A selected set of soil samples, classified as Technosols, were collected from brown coal mining dumpsites in the Czech Republic (a total of 264 samples). Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross-validation was used to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R²_cv) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEP_cv). Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms. We found that the MBL performs better than the other three methods by about 10% (largest R²_cv and smallest RMSEP_cv), followed by the SVMR. It should be pointed out that the other methods (PLSR and BRT) still provided reliable results. The study concluded that in this examined dataset, reflectance spectroscopy combined with the MBL algorithm is rapid and accurate, offers major efficiency and cost-saving possibilities in other datasets and can lead to better targeting of management interventions. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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2818 KiB

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Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

by Lin Bai, Cuizhen Wang, Shuying Zang, Yuhong Zhang, Qiannan Hao and Yuexiang Wu

Remote Sens. 2016, 8(2), 163; https://doi.org/10.3390/rs8020163 - 20 Feb 2016

Cited by 57 | Viewed by 10797

Abstract

The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity [...] Read more.

The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na₂CO₃ and NaHCO₃ in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R²_Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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4361 KiB

Open AccessArticle

Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

by Zhiqiang Xiao, Lingmei Jiang, Zhongli Zhu, Jindi Wang and Jinyang Du

Remote Sens. 2016, 8(1), 49; https://doi.org/10.3390/rs8010049 - 7 Jan 2016

Cited by 25 | Viewed by 6778

Abstract

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil [...] Read more.

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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