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Soil Erosion Watch — A Bootstrapped Approach to Identify the World’s Degrading Soils

A deep dive into the underbelly of Earth Observation (EO) environmental monitoring applications: Soils. And why they matter in the fight against climate change

Identifying Bare Soil in Space and Time

First of all, we are interested in land that is bare at least once in a given time period. In the case of agricultural areas, this translates into areas under tilling or bare fallow, and for other land uses, an area where vegetation only grows very sparsely. The assumption is that surfaces that are always covered by living (and to some extent, dead) biomass are much less prone to erosion, so we want to draw attention to those (intermittent) bare surfaces.

I lied. Bare soil can be observed occasionally in the middle of the rainforest, in the form of mineral-rich clearings, also referred to as “Bais”, and which, contrary to bare soil in farmland and rangeland, has tremendous ecosystem value (read here why). This one was spotted in the Sangha-Mbaéré district of the Central African Republic. Not even the Google satellite mosaics generated from years of data manage to be fully cloud-free in that part of the World…
Bare soil Synthetic Composite using GEOS3 for the year 2020 in the Nakuru County of Kenya. These are the pixels we are interested in, and no cloudy pixels in sight ☀️😎

Modelling Soil Erosion Hazard

Tackling soil erosion modelling at the global scale in a scientifically rigorous way is challenging to say the least, but with the advent of high temporal frequency, open EO data like Sentinel-2, empirical modelling of such phenomena have become a whole lot more reliable. The widely used Universal Soil Loss Equation, initially introduced by Wischmeier & Smith, 1978, looks like this:

R — Rainfall erosivity factor (

Rainfall erosivity accounts for the combined effect of rainfall duration, magnitude and intensity, as well as taking into account the frequency of erosive events over a longer time period. The global rainfall erosivity dataset produced by Panagos et al., 2017 was used for this purpose, and can be downloaded on the ESDAC portal.

K — Soil erodibility factor (−1)

The K-factor expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. The global equation for the soil erodibility factor was taken from Renard et al., 1997:

LS — Slope length (L) and steepness factor (S) (dimensionless)

The combined LS-factor describes the effect of topography on soil erosion. We applied Panagos et al. 2015's approach to derive it:

Rills represent the first step of water erosion on slopes, and can eventually lead to more severe erosion in the form of gullies
  • Their temporal variability is limited to long-term trends (multi-year). So unless, through some divine powers, we would be able to control global rainfall patterns in clacks of fingers (R), or flip the full soil column upside down (K), or even move mountains the literal sense (LS), the estimates of these factors should hold for a few years. In fact, the R-factor used is essentially based on rainfall records from the 2000–2010 period, and the K-factor on covariates representative of the year 2017 at best. It isn’t ideal, but is the best available to derive this global baseline estimation.
The mountains that Mr Montague moves are metaphorical, and does not have an impact on the LS-factor estimation

V — Vegetation factor (dimensionless)

The V-factor, which is a spin-off of the C-factor in the original RUSLE equation, is a more logical way of looking at the (positive) impact of cover vegetation in mitigating soil erosion. Indeed, while the previous 3 factors were erosion-inducing, this one is erosion-controlling.

L — Landscape factor (dimensionless)

The L-factor is a factor incorporating the erosion-controlling effect of landscape features, such linear alterations to interrupt rainfall runoff in the form of physical field boundaries or terraces. Karydas & Panagos, 2018 model this effect in the following way:

Result of the 3x3 pixell (30m) Sobel filter, highlighting within-field punctual and linear features, which has a positive impact on soil erosion

A — Average annual soil erosion rate in soil mass per unit area per year (t.ha−1.year−1)

The metric we are after. This is a ballpark approximation for the areas where bare soil is exposed, and is the best we can do considering the global approach applied and the open datasets available to do so.


  1. Bare Soil RGB composite: This is the layer resulting from the GEOS3 bare soil synthetic data generator from Demattê et al., 2020. The default data displayed is that of Kenya for the year 2020.


The right panel offers a list of buttons to generate custom output for any world sub-administrative areas.

Global Charts

The three global charts to the right illustrate the following:

  1. The bare soil frequency histogram, summarizing the distribution of bare soil pixel’s bare frequency. Unsurprisingly, the distribution is ramping up towards the high frequency values, as the majority of the pixels observed correspond to land degraded beyond arable.
  2. The annual soil loss rate histogram. The distribution was normalized using the natural logarithm function because the majority of the soil erosion rates typically fall between 0 and 10 t.ha-1.year-1, with a runaway effects of a few values peaking at 30+ t.ha-1.year-1, which are invisible in a plot if not normalized. The further to the right the peak of the distribution is, the more significant the soil loss hazard is in the area. The skewness of the distribution towards the right is also an important indicator of soil erosion hazard. All values below 0 represent minor erosion (< 1 t.ha-1.year-1), so if the histogram barely exceeds 0 on the x-axis, the region is generally speaking not prone to erosion.

On-Draw FCover plots

A nice feature of the App is that it allows the user to draw a rectangle, polygon or a point to plot an FCover profile (green curve) calculated from Sentinel-2 at the corresponding location. If a polygon or rectangle is drawn, the mean value corresponding to the polygon is returned. Moreover, the dates (temporally aggregated in 15 days time intervals) flagged as bare soils are plotted on the graph as brown dots, and their values correspond to the spatially and temporally averaged value of those observed bare soil pixels.



Remote monitoring | Soil Regeneration

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William Ouellette

Regenerating degraded 🌍 and eroded soils 🌱, one pixel at a time🛰️