Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region

[1]  Diofantos G. Hadjimitsis,et al.  Linear 3-D transformations of Landsat 5 TM satellite images for the enhancement of archaeological signatures during the phenological cycle of crops , 2015 .

[2]  A. Walkley,et al.  AN EXAMINATION OF THE DEGTJAREFF METHOD FOR DETERMINING SOIL ORGANIC MATTER, AND A PROPOSED MODIFICATION OF THE CHROMIC ACID TITRATION METHOD , 1934 .

[3]  Apostolos Sarris,et al.  Integrated GIS and remote sensing analysis for landfill sitting in Western Crete, Greece , 2014, Environmental Earth Sciences.

[4]  Panos Panagos,et al.  Modeling soil erosion and river sediment yield for an intermountain drainage basin of the Central Apennines, Italy , 2014 .

[5]  Filippos Vallianatos,et al.  Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece , 2009 .

[6]  Diofantos G. Hadjimitsis,et al.  Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus , 2013 .

[7]  Gabriele Buttafuoco,et al.  Soil erosion assessment using proximal spectral reflectance in VIS-NIR-SWIR region in sample area of Calabria region (southern Italy) , 2012 .

[8]  G. Bouyoucos.  Hydrometer Method Improved for Making Particle Size Analyses of Soils1 , 1962 .

[9]  Gustavo Olague,et al.  Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation , 2019, Remote. Sens..

[10]  Kenneth G. Renard,et al.  Using monthly precipitation data to estimate the R-factor in the revised USLE , 1994 .

[11]  Patrick Hostert,et al.  Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites , 1998 .

[12]  I. Moore,et al.  Physical basis of the length-slope factor in the universal soil loss equation , 1986 .

[13]  Emanuele Santi,et al.  pplication of artificial neural networks for the soil moisture retrieval rom active and passive microwave spaceborne sensors , 2015 .

[14]  Panos Panagos,et al.  Towards prediction of soil erodibility, SOM and CaCO 3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran , 2018 .

[15]  Hamid Reza Pourghasemi,et al.  Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion , 2017 .

[16]  Luca Montanarella,et al.  Trends in Land Degradation in Europe , 2007 .

[17]  A. R. Mahmud,et al.  An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia , 2012, Environmental Earth Sciences.

[18]  Henry Scheyvens,et al.  Investigating the relationships between climate hazards and spatial accessibility to microfinance using geographically-weighted regression , 2019, International Journal of Disaster Risk Reduction.

[19]  Evdokia Tapoglou,et al.  A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation , 2014 .

[20]  Martijn J. Booij,et al.  Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data , 2018 .

[21]  Abderrazak Bannari,et al.  Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape , 2018, Remote. Sens..

[22]  Panos Panagos,et al.  Soil erodibility in Europe: a high-resolution dataset based on LUCAS. , 2014, The Science of the total environment.

[23]  R. Lal,et al.  Soil erosion and the global carbon budget. , 2003, Environment international.

[24]  Changxin Wang,et al.  Application of geographically weighted regression (GWR) in the analysis of the cause of haze pollution in China , 2019, Atmospheric Pollution Research.

[25]  H. Ramesh,et al.  Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin , 2016 .

[26]  Emanuele Lugato,et al.  Cost of agricultural productivity loss due to soil erosion in the European Union: From direct cost evaluation approaches to the use of macroeconomic models , 2018 .

[27]  Vassilis Detsis,et al.  Exploring Long-Term Impact of Grazing Management on Land Degradation in the Socio-Ecological System of Asteroussia Mountains, Greece , 2015 .

[28]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[29]  Kanwar L. Sahrawat,et al.  Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems , 2011 .

[30]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[31]  Hanspeter Liniger,et al.  Assessment of land degradation in Mediterranean forests and grazing lands using a landscape unit approach and the normalized difference vegetation index , 2017 .

[32]  Philippe Lagacherie,et al.  Combining measured sites, soilscapes map and soil sensing for mapping soil properties of a region , 2017 .

[33]  W. H. Wischmeier,et al.  Predicting rainfall-erosion losses from cropland east of the Rocky Mountains , 1965 .

[34]  Steffen Fritz,et al.  Building a hybrid land cover map with crowdsourcing and geographically weighted regression , 2015 .

[35]  Guiying Li,et al.  A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China , 2018, Remote. Sens..

[36]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[37]  Ndukwe E. Chiemelu,et al.  The Role of Surveying and Mapping in Erosion Management and Control: Case of Omagba Erosion Site, Onitsha Anambra State, Nigeria , 2013 .

[38]  Wolfgang Wagner,et al.  Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia , 2012, Remote sensing of environment.

[39]  Lydia M. Chabala,et al.  Application of Ordinary Kriging in Mapping Soil Organic Carbon in Zambia , 2017 .

[40]  S. D. Angima,et al.  Soil erosion prediction using RUSLE for central Kenyan highland conditions , 2003 .

[41]  Sabu Joseph,et al.  Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP , 2018 .

[42]  KimMinyoung,et al.  Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas , 2008 .

[43]  Xiangming Xiao,et al.  Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014. , 2018, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[44]  John E. Gilley,et al.  Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas , 2008 .

[45]  Panos Panagos,et al.  Estimating the soil erosion cover-management factor at the European scale , 2015 .

[46]  Sabine Grunwald,et al.  Regional modelling of soil carbon at multiple depths within a subtropical watershed. , 2010 .

[47]  Kwanele Phinzi,et al.  The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review , 2019, International Soil and Water Conservation Research.

[48]  M. H. Hamza,et al.  A GIS-based soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) (Lebna watershed, Cap Bon, Tunisia) , 2017, Natural Hazards.

[49]  George P. Karatzas,et al.  Assessing water erosion in Mediterranean tree crops using GIS techniques and field measurements: the effect of climate change , 2016, Natural Hazards.

[50]  Gabriele Buttafuoco,et al.  Studying the relationship between water-induced soil erosion and soil organic matter using Vis–NIR spectroscopy and geomorphological analysis: A case study in southern Italy , 2013 .