Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data

Geoderma ◽  
2005 ◽  
Vol 128 (3-4) ◽  
pp. 234-248 ◽  
Author(s):  
Ahmed Douaik ◽  
Marc Van Meirvenne ◽  
Tibor Tóth
Keyword(s):  
Maximum Entropy ◽  
Soil Salinity ◽  
Bayesian Maximum Entropy ◽  
Soft Data ◽  
Spatio Temporal

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 Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach

2010 ◽  
Vol 25 ◽  
pp. 97-102 ◽  
Author(s):  
I. Hussain ◽  
J. Pilz ◽  
G. Spoeck
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Prediction Error ◽  
Bayesian Model ◽  
Space Time ◽  
Bayesian Maximum Entropy ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Monsoon Period ◽  
Spatio Temporal

Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974–2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions.

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