Towards the Assessment of Soil-Erosion-Related C-Factor on European Scale Using Google Earth Engine and Sentinel-2 Images

Dimitrios D Alexakis,Stelios Manoudakis,Christos Polykretis,Athos Agapiou

doi:10.3390/rs13245019

Dimitrios D Alexakis, Stelios Manoudakis + Show 2 more

Open Access

https://doi.org/10.3390/rs13245019

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Abstract

Soil erosion is a constant environmental threat for the entirety of Europe. Numerous studies have been published during the last years concerning assessing soil erosion utilising Remote Sensing (RS) and Geographic Information Systems (GIS). Such studies commonly employ empirical erosion models to estimate soil loss on various spatial scales. In this context, empirical models have been highlighted as major approaches to estimate soil loss on various spatial scales. Most of these models analyse environmental factors representing soil-erosion-influencing conditions such as the climate, topography, soil regime, and surface vegetation coverage. In this study, the Google Earth Engine (GEE) cloud computing platform and Sentinel-2 satellite imagery data have been combined to assess the vegetation-coverage-related factor known as cover management factor (C-factor) at a high spatial resolution (10 m) considering a total of 38 European countries. Based on the employment of the RS derivative of the Normalised Difference Vegetation Index (NDVI) for January and December 2019, a C-factor map was generated due to mean annual estimation. National values were then calculated in terms of different types of agricultural land cover classes. Furthermore, the European C-factor (CEUROPE) values concerning the island of Crete (Greece) were compared with relevant values estimated for the island (CCRETE) based on Sentinel-2 images being individually selected at a monthly time-step of 2019 to generate a series of 12 maps for the C-factor in Crete. Our results yielded identical C-factor values for the different approaches. The outcomes denote GEE’s high analytic and processing abilities to analyse massive quantities of data that can provide efficient digital products for soil-erosion-related studies.

Highlights

Soil erosion is a physical process that constitutes a major environmental threat in the European continent and worldwide [1]. xtensive research initiatives have been applied to support the study of soil erosion monitoring, assessment and mitigation in recent years [2,3]
The main objective of this study is to explore the capabilities of Google Earth Engine (GEE) to effectively estimate C-factor related to land cover on a European scale by using Normalised Difference Vegetation Index (NDVI) data products from Sentinel-2 satellite images
We mainly focused on agricultural and pasture land classes since (a) the agricultural areas are more prone to soil erosion, (b) the NDVI approach is more efficient on these classes and (c) the soil loss phenomena have an economic impact on these land use classes, providing valuable information

Summary

Introduction

Soil erosion is a physical process that constitutes a major environmental threat in the European continent and worldwide [1]. xtensive research initiatives have been applied to support the study of soil erosion monitoring, assessment and mitigation in recent years [2,3]. Xtensive research initiatives have been applied to support the study of soil erosion monitoring, assessment and mitigation in recent years [2,3]. Soil erosion is a physical process that constitutes a major environmental threat in the European continent and worldwide [1]. Geospatial technologies such as Remote Sensing (RS) and Geographic Information Systems (GIS) have played a crucial role in this task, offering the opportunity to researchers to explore extensive regions on various time and spatial scales. Empirical models can be characterised as the most widely applied and accepted worldwide [4]. Equation—RUSLE) constitute the main representatives of empirical models [5].

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