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 RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

The potential of Self-Organising maps clustering to characterise a harvested peatland using airborne radiometric data and OS digital elevation model.

2021 ◽  
Author(s):  
Eve daly ◽  
David O Leary
Keyword(s):  
Water Content ◽  
Digital Elevation Model ◽  
Expert Knowledge ◽  
Self Organizing Map ◽  
Near Surface ◽  
Restoration Project ◽  
Raised Bogs ◽  
Digital Elevation ◽  
Elevation Model ◽  
Radiometric Data

&lt;p&gt;Peatlands are becoming recognized as important carbon sequestration centres. Through restoration projects of peatlands in which the water table is raised, they may become carbon neutral or possibly carbon negative. Restoration projects require a knowledge of intra-peat variation across potentially large spatial areas. This is often difficult with traditional in-situ point measurements. The integration of multidimensional geophysical datasets and digital elevation models, combined with modern data analytical techniques, may provide a rapid means of accessing intra-peat variation. In this study, an airborne radiometric survey, being flown nationally over the Republic of Ireland, combined with a digital elevation model, is used to delineate areas within an industrial peatland where peat thickness is less than 1m. Radiometric data are particularly suited to peat studies as they are sensitive to water content and peat thickness and require relatively little expert knowledge to utilise. Peat, as a mostly organic material, acts as a low signal environment where variations in the signal are linked to intra-peat variation of thickness, density and/or water content. This study uses an unsupervised machine learning, self-organizing map clustering methodology to group the study site into three zones interpreted as 1) the edge of the bog where peat layer is thinning or there is influence on the radiometric signal from non-peat soils outside of the bog, 2) the normal peat conditions where thickness and saturation appear as a relative constant in the radiometric response, and 3) areas where the peat is either thinner or drier. A ground geophysical survey was conducted to verify this interpretation. The delineation of such spatial variations in the radiometric response could aid any restoration project in the initial stages or act as a baseline study to monitor changes to the peatland during and after a restoration project is complete. Future work will see this methodology extended to other peatland types such as blanket bogs and natural raised bogs, as well as the integration of concurrent airborne electromagnetic data to link the near-surface radiometric response to the deeper vadose zone and define a more comprehensive classification scheme for these peatland sites.&lt;/p&gt;

scholarly journals Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging

2019 ◽  
Vol 55 (6) ◽  
pp. 4785-4800 ◽  
Author(s):  
Tyson E. Ochsner ◽  
Evan Linde ◽  
Matthew Haffner ◽  
Jingnuo Dong
Keyword(s):  
Soil Moisture ◽  
Regression Kriging

scholarly journals A multi-level strategy for anticipating future glacier lake formation and associated hazard potentials

2010 ◽  
Vol 10 (2) ◽  
pp. 339-352 ◽  
Author(s):  
H. Frey ◽  
W. Haeberli ◽  
A. Linsbauer ◽  
C. Huggel ◽  
F. Paul
Keyword(s):  
Swiss Alps ◽  
Bed Topography ◽  
Aster Gdem ◽  
Digital Elevation ◽  
Lake Formation ◽  
Level 3 ◽  
Multi Level ◽  
Four Levels ◽  
Elevation Model

Abstract. In the course of glacier retreat, new glacier lakes can develop. As such lakes can be a source of natural hazards, strategies for predicting future glacier lake formation are important for an early planning of safety measures. In this article, a multi-level strategy for the identification of overdeepened parts of the glacier beds and, hence, sites with potential future lake formation, is presented. At the first two of the four levels of this strategy, glacier bed overdeepenings are estimated qualitatively and over large regions based on a digital elevation model (DEM) and digital glacier outlines. On level 3, more detailed and laborious models are applied for modeling the glacier bed topography over smaller regions; and on level 4, special situations must be investigated in-situ with detailed measurements such as geophysical soundings. The approaches of the strategy are validated using historical data from Trift Glacier, where a lake formed over the past decade. Scenarios of future glacier lakes are shown for the two test regions Aletsch and Bernina in the Swiss Alps. In the Bernina region, potential future lake outbursts are modeled, using a GIS-based hydrological flow routing model. As shown by a corresponding test, the ASTER GDEM and the SRTM DEM are both suitable to be used within the proposed strategy. Application of this strategy in other mountain regions of the world is therefore possible as well.

scholarly journals LANDSLIDE SUSCEPTIBILITY MODELLING IN SELECTED STATES ACROSS SE. NIGERIA

2020 ◽  
Vol 4 (1) ◽  
pp. 23-27
Author(s):  
R. O. E. Ulakpa ◽  
V.U.D. Okwu ◽  
K. E. Chukwu ◽  
M. O. Eyankware
Keyword(s):  
Landslide Susceptibility ◽  
Landsat Imagery ◽  
Control Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Landsat 8 ◽  
Aerial Photos ◽  
Anambra State ◽  
Digital Elevation ◽  
Elevation Model

Identification and mapping of landslide is essential for landslide risk and hazard assessment. This paper gives information on the uses of landsat imagery for mapping landslide areas ranging in size from safe area to highly prone areas. Landslide mitigation largely depends on the understanding of the nature of the factors namely: slope, soil type, lineament, lineament density, elevation, rainfall and vegetation. These factors have direct bearing on the occurrence of landslide. Identification of these factors is of paramount importance in setting out appropriate and strategic landslides control measures. Images for this study was downloaded by using remote sensing with landsat 8 ETM and aerial photos using ArcGIS 10.7 and Surfer 8 software, while Digital Elevation Model (DEM) and Google EarthPro TM were used to produce slope, drainage, lineament and elevation. From the processed landsat 8 imagery, landslide susceptibility map was produced, and landslide was category into various class; low, medium and high. From the study, it was observed that Enugu and Anambra state ranges from high to medium in terms of landslide susceptibility, Imo state ranges from medium to low.

scholarly journals The 1513 Monte Crenone rock avalanche: numerical model and geomorphological analysis

2022 ◽  
Vol 77 (1) ◽  
pp. 21-37
Author(s):  
Alessandro De Pedrini ◽  
Christian Ambrosi ◽  
Cristian Scapozza
Keyword(s):  
Numerical Modelling ◽  
Rock Avalanche ◽  
Historical Reconstruction ◽  
Field Mapping ◽  
Digital Elevation ◽  
Lake Formation ◽  
Detachment Zone ◽  
Elevation Model ◽  
Geomorphological Analysis

Abstract. As a contribution to the knowledge of historical rockslides, this research focuses on the historical reconstruction, field mapping, and simulation of the expansion, through numerical modelling, of the 30 September 1513 Monte Crenone rock avalanche. Earth observation in 2-D and 3-D, as well as direct in situ field mapping, allowed the detachment zone and the perimeter and volume of the accumulation to be determined. Thanks to the reconstruction of the post-event digital elevation model based on historical topographic maps and the numerical modelling with the RAMMS::DEBRISFLOW software, the dynamics and runout of the rock avalanche were calibrated and reconstructed. The reconstruction of the runout model allowed confirmation of the historical data concerning this event, particularly the damming of the valley floor and the lake formation up to an elevation of 390 m a.s.l., which generated an enormous flood by dam breaching on 20 May 1515, known as the “Buzza di Biasca”.

scholarly journals Quaternary formations mapping in the region of Volta Grande do Rio Uruguai (Brazil)

2021 ◽  
Vol 41 ◽  
pp. e174174
Author(s):  
Sandra Cristina Deodoro ◽  
William Zanete Bertolini ◽  
Plinio da Costa Temba
Keyword(s):  
Particle Size Analysis ◽  
Size Analysis ◽  
Valley Bottom ◽  
Model Soil ◽  
Fluvial Dynamics ◽  
Digital Elevation ◽  
Topographic Attributes ◽  
History Of ◽  
Elevation Model

Quaternary formations (detrital and weathered materials) are an important natural resource for different areas of scientific investigation, from understanding their relation to erosive processes and morphodynamic processes that create landforms or to understanding the history of the first human settlements (geoarcheology). Quaternary coverings can be formed in situ or be transported by external geologic agents. Regarding soils, Quaternary formations are related to landscape topography and are transformed according to the characteristics of this topography. Hence, classifying and mapping these soils is not always easy. The present article aims to map the Quaternary formations along a stretch of the Uruguay River basin  known as Volta Grande (SC/RS-Brazil), by using  topographic attributes derived from the SRTM GL1-Up Sampled digital elevation model, soil particle-size analysis, and a generated Multiresolution Index of Valley Bottom Flatness (MRVBF) index . The results of the analysis show that: (i) colluvium is the predominant Quaternary formation in the study area; (ii) there is a predominance of clay, corroborating previous studies of the region; (iii) the spatial distribution of the study area’s  Quaternary formations reflect local slope dynamics based on morphology and topographic position; and, (iv) the existence of colluvium-alluvium on the Uruguay River’s banks indicates that slope attributes contributed to the pedogeomorphological dynamics of the study area and not only fluvial dynamics. Based on the results, the methodology applied in this study might be useful for pedogeomorphological studies, notably in the analysis and mapping of Quaternary formations, despite some of its limitations.

scholarly journals Simulation Of Surface runoff For Upper Tapi Subcatchment Area (Burhanpur Watershed) Using SWAT

2014 ◽  
Vol XL-8 ◽  
pp. 391-397 ◽  
Author(s):  
V. Shivhare ◽  
M. K. Goel ◽  
C. K. Singh
Keyword(s):  
Surface Runoff ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Temperature And Precipitation ◽  
Digital Elevation ◽  
Arc Gis ◽  
Monthly Runoff ◽  
Hydrologic Unit ◽  
Water Surplus ◽  
Elevation Model

Water related activity that takes place in one part of a river basin may have consequence in the other part. Any plan related to inter basin transfer of water from a water surplus basin to a deficit basin has to take into account the water availability and demands under the present and future scenarios of water use. Watershed is a hydrologic unit where all stream exit from the common outlet. In the present study, Tapi subcatchment area (Burhanpur watershed) located in inter-state basin of Madhya Pradesh and Maharashtra, India, is selected for the estimation of surface runoff using SWAT model. The SWAT works in conjunction with Arc GIS 9.3. Various parameters Digital Elevation Model (DEM), slope derived from DEM, Landuse/Landcover (LULC) and NBSSLUP soil data and temporal data for temperature and precipitation was used as input for the model to predict runoff at the catchment outlet. The model was run from the year 1992 to 1997. The performance of the model in terms of simulated runoff was evaluated using statistical method and compared simulated monthly flow with the observed monthly flow values from 1992 to 1996 to a significant extent. The coefficient of determination (R<sup>2</sup>) for the monthly runoff values for 1992 to 1996 was observed to be 0.82, 0.68, 0.92, 0.69.

scholarly journals Prediction of soil classes in a complex landscape in Southern Brazil

2019 ◽  
Vol 54 ◽  
Author(s):  
Jean Michel Moura-Bueno ◽  
Ricardo Simão Diniz Dalmolin ◽  
Taciara Zborowski Horst-Heinen ◽  
Luciano Campos Cancian ◽  
Ricardo Bergamo Schenato ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Model Performance ◽  
Spatial Prediction ◽  
Support Vector ◽  
Kappa Index ◽  
Digital Elevation ◽  
Complex Landscape ◽  
Elevation Model

Abstract: The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression, and support vector machine. The accuracy of the models was evaluated by the kappa index (K), general accuracy (GA), and class accuracy. The prediction models were sensitive to the disproportionate sampling of soil classes. The best predicted map achieved a GA of 71% and K of 0.59. The use of the covariate set chosen by expert knowledge improves model performance in predicting soil classes in a complex landscape, and random forest is the best model for the spatial prediction of soil classes.

scholarly journals The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

2020 ◽  
Vol 12 (9) ◽  
pp. 1358 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Jinjie Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Ratio Model ◽  
Retrieval Model ◽  
Backscattering Coefficient ◽  
Strong Control

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

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