scholarly journals Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

2020 ◽  
Vol 12 (23) ◽  
pp. 3916
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Qiyue Liu ◽  
Gengke Wang ◽  
Tao Yu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Maximum Entropy ◽  
Near Infrared ◽  
Bayesian Maximum Entropy ◽  
General Knowledge ◽  
Combined Approach ◽  
Soft Data ◽  
Digital Elevation ◽  
Source Data ◽  
Elevation Model

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.

scholarly journals Visible and near-infrared digital images for determination of ice velocities and surface elevation during a surge on Osbornebreen, a tidewater glacier in Svalbard

1997 ◽  
Vol 24 ◽  
pp. 255-261 ◽  
Author(s):  
Cecilie Rolstad ◽  
Jostein Amlien ◽  
Jon-Ove Hagen ◽  
Bengt Lundén
Keyword(s):  
Velocity Field ◽  
Near Infrared ◽  
Aerial Photographs ◽  
Correlation Technique ◽  
Digital Elevation ◽  
Compressive Flow ◽  
Tidewater Glacier ◽  
Extension Zone ◽  
Elevation Model ◽  
Cross Correlation Technique

A field of vectors showing the average velocity of the surging glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Système pour l’Observation de la Terre) and Landsat thematic mapper images. Crevasses which developed during the initial phase of the surge in the winter of 1986–87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field could be almost entirely determined with 1 month separation of the images, but only partly determined with images 1 year apart, due to changes of the crevasse pattern. The velocity field is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream. The velocity field, the rapid advance of the terminus and the development of transverse crevasses in the upper accumulation area within a 6 month period may indicate that the surge developed as a zone of extension starting near the terminus and propagating quickly upstream. The crevasse pattern in the images is therefore interpreted to be the result of the extension zone traveling upstream, and, as the whole glacier starts to slide, the crevasse pattern alters according to the bedrock topography.

scholarly journals Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2160
Author(s):  
Daniel Kibirige ◽  
Endre Dobos
Keyword(s):  
Soil Moisture ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Kriging ◽  
Environmental Covariates ◽  
Observatory Data ◽  
Digital Elevation ◽  
Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

scholarly journals Potential of Sentinel-2 Images for Estimating of Soil Resistivity over Agricultural Fields

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 18
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Electrical Resistivity ◽  
Near Infrared ◽  
Soil Layers ◽  
Digital Elevation ◽  
Combined Use ◽  
Input Variables ◽  
Elevation Model ◽  
High Level ◽  
Sentinel 2 ◽  
Soil Electrical Resistivity

The aim of this study is to assess the possibilities of the VNIR (Visible and Near InfraRed) and SWIR (Short Wavelength InfraRed) satellite data for estimating intra-plot patterns of soil electrical resistivity consistent with ground measurements. The methodology is based on optical reflectances that constitute the input variables of random forest, alone or in combination with parameters derived from a digital elevation model (DEM). Over a field located in southwestern France, the results show high level of accuracy for the 0–50 and 0–100 cm soil layers (with R² of 0.69 and 0.59, and a relative RMSE of 18% and 16%, respectively), the performances being lower for the 0–170 cm layer (R² of 0.39, relative RMSE of 20%). The combined use of optical reflectances with parameters derived from the DEM slightly improves the performances, whatever the considered layer. The influence of each reflectance on soil electrical resistivity estimates is finally analyzed, showing that the wavelengths acquired in the SWIR have a relative higher importance than VNIR reflectance.

scholarly journals Visible and near-infrared digital images for determination of ice velocities and surface elevation during a surge on Osbornebreen, a tidewater glacier in Svalbard

1997 ◽  
Vol 24 ◽  
pp. 255-261 ◽  
Author(s):  
Cecilie Rolstad ◽  
Jostein Amlien ◽  
Jon-Ove Hagen ◽  
Bengt Lundén
Keyword(s):  
Velocity Field ◽  
Near Infrared ◽  
Aerial Photographs ◽  
Correlation Technique ◽  
Digital Elevation ◽  
Compressive Flow ◽  
Tidewater Glacier ◽  
Extension Zone ◽  
Elevation Model ◽  
Cross Correlation Technique

A field of vectors showing the average velocity of the surging glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Système pour l’Observation de la Terre) and Landsat thematic mapper images. Crevasses which developed during the initial phase of the surge in the winter of 1986–87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field could be almost entirely determined with 1 month separation of the images, but only partly determined with images 1 year apart, due to changes of the crevasse pattern. The velocity field is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream. The velocity field, the rapid advance of the terminus and the development of transverse crevasses in the upper accumulation area within a 6 month period may indicate that the surge developed as a zone of extension starting near the terminus and propagating quickly upstream. The crevasse pattern in the images is therefore interpreted to be the result of the extension zone traveling upstream, and, as the whole glacier starts to slide, the crevasse pattern alters according to the bedrock topography.

scholarly journals Evaluating topographic wetness indices across central New York agricultural landscapes

2013 ◽  
Vol 10 (11) ◽  
pp. 14041-14093 ◽  
Author(s):  
B. P. Buchanan ◽  
M. Fleming ◽  
R. L. Schneider ◽  
B. K. Richards ◽  
J. Archibald ◽  
...  
Keyword(s):  
New York ◽  
Soil Moisture ◽  
Soil Properties ◽  
Flow Direction ◽  
Field Studies ◽  
Agricultural Landscapes ◽  
Mixed Effects Modeling ◽  
Digital Elevation ◽  
Low Pass ◽  
Elevation Model

Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWI) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches in the US. We calculated TWIs using over 400 unique formulations that considered different: Digital Elevation Model (DEM) resolution (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA; each field was visited 5–8 times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) LiDAR-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved the correlations.

scholarly journals Accuracy Assessment of Digital Elevation Models (DEMs): A Critical Review of Practices of the Past Three Decades

2020 ◽  
Vol 12 (16) ◽  
pp. 2630
Author(s):  
José L. Mesa-Mingorance ◽  
Francisco J. Ariza-López
Keyword(s):  
Accuracy Assessment ◽  
Great Majority ◽  
Professional Associations ◽  
Assessment Methods ◽  
Reference Source ◽  
Digital Elevation ◽  
Source Data ◽  
Data Source ◽  
Assessment Standard ◽  
Elevation Model

An analysis of almost 200 references has been carried out in order to obtain knowledge about the DEM (Digital Elevation Model) accuracy assessment methods applied in the last three decades. With regard to grid DEMs, 14 aspects related to the accuracy assessment processes have been analysed (DEM data source, data model, reference source for the evaluation, extension of the evaluation, applied models, etc.). In the references analysed, except in rare cases where an accuracy assessment standard has been followed, accuracy criteria and methods are usually established according to the premises established by the authors. Visual analyses and 3D analyses are few in number. The great majority of cases assess accuracy by means of point-type control elements, with the use of linear and surface elements very rare. Most cases still consider the normal model for errors (discrepancies), but analysis based on the data itself is making headway. Sample size and clear criteria for segmentation are still open issues. Almost 21% of cases analyse the accuracy in some derived parameter(s) or output, but no standardization exists for this purpose. Thus, there has been an improvement in accuracy assessment methods, but there are still many aspects that require the attention of researchers and professional associations or standardization bodies such as a common vocabulary, standardized assessment methods, methods for meta-quality assessment, and indices with an applied quality perspective, among others.

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