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Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland

  • Dr. Simon Scheper - Research | Consulting | Teaching
  • Swiss Federal Institute for Forest, Snow and Landscape Research - WSL

Abstract and Figures

The decrease in vegetation cover is one of the main triggering factors for soil erosion of grasslands. Within the Revised Universal Soil Loss Equation (RUSLE), a model commonly used to describe soil erosion, the vegetation cover for grassland is expressed in the cover and management factor (C-factor). The site-specific C-factor is a combination of the relative erosion susceptibility of a particular plant development stage (here expressed as soil loss ratio SLR) and the corresponding rainfall pattern (here expressed as R-factor ratio). Thus, for grasslands the fraction of green vegetation cover (FGVC) determines the SLRs. Although Switzerland is a country dominated by grassland with high percentages of mountainous regions and evidence for high erosion rates of grassland exists, soil erosion risk modeling of grasslands and especially of mountainous grasslands in Switzerland is restricted to a few studies. Here, we present a spatio-temporal approach to assess the dynamics of the C-factor for Swiss grasslands and to identify erosion prone regions and seasons simultaneously. We combine different satellite data, aerial data, and derivative products like Climate Change Initiative (CCI) Land Cover, Swissimage false-color infrared (Swissimage FCIR), PROBA-V Fraction of green Vegetation Cover (FCover300m), and MODIS Vegetation Indices 16-Day L3 Global (MOD13Q1) for the FGVC mapping of grasslands. In the spatial mapping, the FGVC is extracted from Swissimage FCIR (spat. res. 2 m) by linear spectral unmixing (LSU). The spatially derived results are then fused with the 10-day deviations of temporal FGVC derived by FCover300m. Following the original RUSLE approach, the combined FGVC are transformed to SLRs and weighted with high spatio-temporal resolved ratios of R-factors to result in spatio-temporal C-factors for Swiss grasslands. The annual average C-factor of all Swiss grasslands is 0.012. Seasonal and regional patterns (low C in winter, high C in summer, dependency on elevation) are recognizable in the spatio-temporal mapping approach. They are mainly explicable by the R-factor distribution within a year. Knowledge about the spatio-temporal dynamic of erosion triggering factors is of high interest for agronomists who can introduce areal and time specific selective erosion control measures and thereby reduce the direct costs of mitigation as well as erosion measures.
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Remote Sensing of Environment
journal homepage:
Mapping spatio-temporal dynamics of the cover and management factor (C-
factor) for grasslands in Switzerland
Simon Schmidt
, Christine Alewell
, Katrin Meusburger
Environmental Geosciences, University of Basel, Bernoullistrasse 30, CH-4056 Basel, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
Monthly soil erosion modeling
Soil loss ratio SLR
Vegetation dynamics
CCI land cover
The decrease in vegetation cover is one of the main triggering factors for soil erosion of grasslands. Within the
Revised Universal Soil Loss Equation (RUSLE), a model commonly used to describe soil erosion, the vegetation
cover for grassland is expressed in the cover and management factor (C-factor). The site-specic C-factor is a
combination of the relative erosion susceptibility of a particular plant development stage (here expressed as soil
loss ratio SLR) and the corresponding rainfall pattern (here expressed as R-factor ratio). Thus, for grasslands the
fraction of green vegetation cover (FGVC) determines the SLRs. Although Switzerland is a country dominated by
grassland with high percentages of mountainous regions and evidence for high erosion rates of grassland exists,
soil erosion risk modeling of grasslands and especially of mountainous grasslands in Switzerland is restricted to a
few studies. Here, we present a spatio-temporal approach to assess the dynamics of the C-factor for Swiss
grasslands and to identify erosion prone regions and seasons simultaneously. We combine dierent satellite data,
aerial data, and derivative products like Climate Change Initiative (CCI) Land Cover, Swissimage false-color
infrared (Swissimage FCIR), PROBA-V Fraction of green Vegetation Cover (FCover300m), and MODIS
Vegetation Indices 16-Day L3 Global (MOD13Q1) for the FGVC mapping of grasslands. In the spatial mapping,
the FGVC is extracted from Swissimage FCIR (spat. res. 2 m) by linear spectral unmixing (LSU). The spatially
derived results are then fused with the 10-day deviations of temporal FGVC derived by FCover300m. Following
the original RUSLE approach, the combined FGVC are transformed to SLRs and weighted with high spatio-
temporal resolved ratios of R-factors to result in spatio-temporal C-factors for Swiss grasslands. The annual
average C-factor of all Swiss grasslands is 0.012. Seasonal and regional patterns (low C in winter, high C in
summer, dependency on elevation) are recognizable in the spatio-temporal mapping approach. They are mainly
explicable by the R-factor distribution within a year. Knowledge about the spatio-temporal dynamic of erosion
triggering factors is of high interest for agronomists who can introduce areal and time specic selective erosion
control measures and thereby reduce the direct costs of mitigation as well as erosion measures.
1. Introduction
Among all soil erosion risk factors in USLE-type (Universal Soil Loss
Equation) and USLE based soil erosion models (e.g., RUSLE Revised
Universal Soil Loss Equation), the cover and management factor namely
C-factor is the one most sensitive as it follows plant growth and rainfall
dynamics (Wischmeier and Smith, 1978;Nearing et al., 2005). The C-
factor represents the eect of cropping and management practices on
soil erosion rates by water (Renard et al., 1997). The factor can be
expressed as a combination of crop and plant systems, management,
and rainfall pattern (Wischmeier and Smith, 1978). Following the
USLE-original approach (Wischmeier and Smith, 1978;Schwertmann
et al., 1987), a site-and time-specic C-factor is derived by the ratio of
soil losses (soil loss ratio SLR) of a particular crop stage period (for
arable land) or plant development stage (for grassland) weighted by its
corresponding fraction of rainfall erosivity (R-factor ratio; Renard et al.,
1997). Thus, the rainfall erosivity is considered twice in the RUSLE: as
R-factor and as a weighting factor of the C-factor (Schwertmann et al.,
1987). Alternatively, SLRs are a multiplication of sub-factors (previous
land use, canopy cover, surface cover, surface roughness, soil moisture;
Renard et al., 1997). C-factor values are equaling 1 for bare soil of the
reference plot and reach a minimum in forests (C-factor = 0.0001;
Wischmeier and Smith, 1978). The C-factor is the most adjustable factor
by land use management (Durán Zuazo and Rodríguez Pleguezuelo,
2008;Maetens et al., 2012;Biddoccu et al., 2014;Eshel et al., 2015;
Biddoccu et al., 2016) with the highest amplitude of spatial and
Received 21 December 2017; Received in revised form 29 March 2018; Accepted 4 April 2018
Corresponding author.
E-mail addresses:, (S. Schmidt), (C. Alewell), (K. Meusburger).
Remote Sensing of Environment 211 (2018) 89–104
0034-4257/ © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (
temporal variation among all the RUSLE factors (Zhang et al., 2011;
Estrada-Carmona et al., 2016). Thus, the factor can easily alter by a
change of policy and farming strategies (McCool et al., 1995;Panagos
et al., 2015a). An alteration of the support practice factor (P) (e.g.,
introducing of stone walls, grass margins, contour farming, terracing)
often requires higher nancial investments and soil conservation sub-
sidies (Panagos et al., 2015b, 2015c). Other important soil erosion risk
factors such as rainfall erosivity (R), soil erodibility (K) and topography
(LS) are mainly determined by natural conditions and are relatively
more independent from anthropogenic interventions.
SLRs of grassland are preferably determined by vegetation cover
fraction in contrast to arable land where plant type and/or rotation is
the inuencing factor (Schindler Wildhaber et al., 2012). The fractional
vegetation cover is one of the most critical factors in soil erosion
modeling as it describes a negatively exponential or negatively linear
relationship (according to the dierent types of vegetation) to soil
erosion (McCool et al., 1995;Puigdefábregas, 2005;Vrieling et al.,
2008). A dense vegetation cover protects the soil against the raindrop
splash eect (Schwertmann et al., 1987), causes a stabilization of the
soil structure by plant roots (Jury and Horton, 2004;Pohl et al., 2009),
enriches soils by soil organic carbon, leads to soil aggregation (Lugato
et al., 2014), reduces runoffflow velocity (Bochet et al., 2006), and thus
mitigates the susceptibility to soil loss (Durán Zuazo and Rodríguez
Pleguezuelo, 2008;Zhou et al., 2008;Wang et al., 2009;Butt et al.,
2010;Sun et al., 2013). As such, grassland cover has a high protective
function for soils (Martin et al., 2010;Schindler Wildhaber et al., 2012).
However, due to disturbance (García-Ruiz et al., 2015;Merz et al.,
2009;Meusburger and Alewell, 2014;Sutter, 2007;Sutter and Keller,
2009;Panagos et al., 2014a), harsh climate and snow processes
(Ceaglio et al., 2012;Meusburger et al., 2014), the vegetation cover can
be disturbed and the consequent soil losses might be substantial. If
vegetation cover is partially (66% fractional vegetation cover, Felix and
Johannes, 1995) or nearly completely reduced (Frankenberg et al.,
1995), erosion rates are considerably higher (4.4. t ha
20 t ha
, respectively). Switzerland is a country dominated by
grassland (Jeangros and Thomet, 2004). Nonetheless, up to now, soil
erosion risk modeling is mainly restricted to arable land although evi-
dence for high erosion rates of grasslands exists (Alewell et al., 2009;
Martin et al., 2010;Meusburger et al., 2010a, 2010b, 2014;Konz et al.,
2012;Meusburger and Alewell, 2014;Alewell et al., 2014).
Commonly, remote sensing approaches to determine the C-factors
(Vrieling, 2006;Zhang et al., 2011;Panagos et al., 2014a) are not
calculating SLRs but frequently assess the C-factor directly without
weighting SLRs with the intra-annual distribution of rainfall erosivity to
assess C-factors in the original sense of (R)USLE. Remote sensing
methods for C-factor determination are often based on vegetation in-
dices like the Normalized Dierence Vegetation Index (NDVI). NDVIs
are directly transformed to C-factors by a linear (de Jong et al., 1998)or
exponential regression (van der Knijet al., 2000) or related to eld
observations (Karaburun, 2010;Vatandaşlar and Yavuz, 2017). NDVI
based C-factor modeling also exists for determining the C-factor for
mountainous grasslands (regions of Korea, Lee and Won, 2012; China,
Zhang and Li, 2015; Kyrgyzstan, Kulikov et al., 2016; Turkey,
Vatandaşlar and Yavuz, 2017). However, drawbacks of that technique
are its uncertainty due to the poor correlation with vegetation attri-
butions, the soil reectance, and the changing vitality of plants (de
Jong, 1994;Vrieling, 2006;Asis and Omasa, 2007;Montandon and
Small, 2008;Meusburger et al., 2010a;Grauso et al., 2015;Panagos
et al., 2015b). As an alternative to NDVI-based approaches, spectral
unmixing can estimate the fractional abundance of green vegetation
(here called the fraction of green vegetation cover FGVC) and bare
soils/bedrock simultaneously (Paringit and Nadaoka, 2003;
Guerschman et al., 2009) which are related to C-factors after including
rainfall erosivity (Yang, 2014). Spectral unmixing techniques (e.g.,
linear spectral unmixing LSU) are used in many erosion studies to de-
termine C-factors over the last years (Hill et al., 1995;Ma et al., 2003;
Lu et al., 2004;de Asis and Omasa, 2007;de Asis et al., 2008;de Jong
and Epema, 2010;Meusburger et al., 2010a, 2010b). An advantage of
spectral unmixing compared to traditional hard classication methods
is the decomposition of mixed pixels in its corresponding component
fractions rather than assigning them to a unique single class (Foody,
2006). Under consideration of the NDVI-related disadvantages, de Asis
and Omasa (2007),de Asis et al. (2008) and Yang (2014) perform a
comparative analysis of C-factors, derived from NDVI- and LSU-ap-
proaches, which result in better results for LSU. A relationship between
C-factor and canopy cover fraction can be observed in various studies.
Zhang et al. (2003) and Gao et al. (2012) determine an exponential
decrease of the C-factor with an increase in canopy cover of grasslands.
Wischmeier and Smith (1978) also observed a negatively exponential
relationship of decreasing C-factors with increasing coverage in their
empirical experiments on the USLE plots.
The (R)USLE factors C and R are highly dynamic with a clear annual
cycle (Wischmeier and Smith, 1978;Renard and Freimund, 1994;
Vrieling, 2006;Vrieling et al., 2014;Möller et al., 2017) in contrast to
the rather constant RUSLE-factors K and LS (Panagos et al., 2012;
Alexandridis et al., 2015). The status of grasslands is diversied within
a year owing to the natural growth cycle, periodical cutting of hay, or
pasture farming (Wiegand et al., 2008). Despite, this spatio-temporal
variability of the C-factor for grasslands, it is often parameterized
without accounting for the spatial variability within a land cover unit
(Ozcan et al., 2008;Bosco et al., 2009;Efthimiou, 2016;Mancino et al.,
2016) nor for the temporal variations (Wang et al., 2002). Hawkins
(1985) stated already that the complications of time and spatial var-
iations in site properties are usually not consideredby applying the
USLE. Alexandridis et al. (2015) conclude that a dynamic approach
focusing on C-factors for the four seasons or 12 months of a year might
help to reduce errors in the annual soil loss compared to a single annual
C-factor. Vrieling et al. (2008, 2014) follow a multi-temporal and
spatial approach to assess the riskiest erosion periods of the year for
Brazil and Africa. López-Vicente et al. (2008) capture erosive periods
among a year for a study area in the mountains of the Central Spanish
Pyrenees by a dynamic approach on a monthly scale. Such time-de-
pendent assessments of soil loss are relevant to support policy makers
and farmers to protect the soil more targeted like it was done by López-
Vicente et al. (2008).Panagos et al. (2012, 2016) and Karydas and
Panagos (2016, 2017) propose a monthly time-step to be appropriate
for soil erosion modeling. The same resolution was already proposed by
Wischmeier and Smith (1965).Grazhdani and Shumka (2007) modeled
the soil erosion rate for Albania on a monthly scale. A combination of
both spatially and temporally varying R- and C-factors lead to a more
dynamic soil erosion risk assessment and simultaneously allows the
identication of susceptible seasons and regions (Panagos et al., 2014a;
Ballabio et al., 2017;Möller et al., 2017). As it is shown in Meusburger
et al. (2012),Schmidt et al. (2016) and Ballabio et al. (2017), the R-
factor of Switzerland also has a high intra-annual variability with clear
regional patterns.
So far, most of the existing national C-Factor maps either do not
include grassland areas (Friedli, 2006;Alexandridis et al., 2015), do not
consider the temporal variations of vegetation cover and management
(Friedli, 2006;Bosco et al., 2009;Panagos et al., 2015b), nor taking
rainfall erosivity for C-factor calculation into account. An assessment
following the original approach by Wischmeier and Smith (1978) to
derive C-factor maps with a high spatio-temporal resolution based on
SLRs and spatio-temporal R-factor ratios does not yet exist on a national
scale. We aim to (i) determine the fractional vegetation cover with a
linear spectral unmixing of orthophotos (2 m spatial resolution), and
(ii) quantify the temporal change of vegetation fraction (10 days tem-
poral resolution) to (iii) assess the spatial and temporal patterns of the
C-factor based on SLRs and high-spatio-temporal R-factor ratios.
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
2. Material and methods
2.1. Swiss grassland areas
Switzerland is a country with a high heterogeneity of climatic, to-
pographic and edaphic conditions. Hills and mountains cover more
than one-third of the state. The Swiss elevation ranges can be clustered
in elevation zones (in m a.s.l. modied after Ellenberg et al., 2010:
Colline zone < 800; Montane > 8001800; Subalpine > 18002300;
Alpine > 23002700; Subnival > 27003100; Nival > 3100), which
are typical for the plant development in the Swiss Alps. Owed to these
natural conditions, permanent grassland is the predominant land use in
about 28% of the territory of Switzerland with a share of 72% of the
total agricultural area (Bötsch, 2004;Jeangros and Thomet, 2004;
Schmidt et al., submitted). Grassland is the prevailing land use type at
elevations above 1500 m a.s.l. (Hotz and Weibel, 2005). Almost half
(46%) of the grassland area is therefore designated as alpine grassland
(Hotz and Weibel, 2005). Alpine soils have been managed by humans
for about 500 years already, but an intensication of the usage and
management of grasslands can be observed since the last 50 years
(Jeangros and Thomet, 2004;Bätzing, 2015;Alewell et al., 2008).
Changes in grassland cover are expected due to land use and climate
2.2. Datasets for C-factor mapping
We subdivided the datasets of the C-factor mapping approach into
data for the spatial and for the temporal assessment. In the spatial
modeling approach, we used a high spatial resolution false-color in-
frared orthophoto (0.25 to 0.50 m; G R NIR) mosaicked of a set of 3432
tiles. This orthophoto mosaic called Swissimage FCIR (Swisstopo, 2010)
is recorded with a Leica ADS80 airborne digital sensor, containing the
channels green (533587 nm), red (604664 nm) and near-infrared
(833920 nm). The production process of Swissimage FCIR is based on
an along-track scanning from east to west that generates stripes of aerial
photos during each ight. The scheduling of the ights of the used
version of Swissimage FCIR was in the years 2012, 2013, 2014 and
2015 between the months March and September. In the preprocessing
step, the aerial photos have undergone a georeferencing, orthor-
ectication, mosaicking, and clipping to tiles of 4375 m × 3000 m by
Swisstopo. We reduced the le size (original le size 1.17 Gigabytes per
tile) and the spatial resolution by resampling to 2 m for a more
straightforward data handling.
The temporal variations of grassland cover in Switzerland are de-
rived from time series of 10-day fractions of the green vegetation cover
(FCover300m, spatial resolution 300 m; Smets et al., 2017) as a product
from PROBA-V. The FGVC is expressed in percentages from 0% (no
vegetation cover) to 100% (full vegetation cover). PROBA-V is a sa-
tellite with an assembled vegetation (V) instrument to image the global
land surface vegetation regularly (Blair, 2013).
A long-term recording sequence (20052015) of 16-day vegetation
indices (MOD13Q1, spatial resolution 250 m; Didan et al., 2015)of the
Moderate Resolution Imaging Spectroradiometer (MODIS) is used as
supplementary data. Based on MOD13Q1, we determine the day of the
year (DOY) with the highest NDVI values to be used as an indicator date
for a maximum in plant growth (Leilei et al., 2014). This information is
relevant for normalizing dierent recording periods of the Swissimage
to the date of the peak growing period. A data accuracy modication
was applied for MOD13Q1. Not processed or lled data, marginal data,
and cloudy grid cells were substituted either by the preceding or suc-
ceeding good data or snow/ice data. With this routine, unreliable pixels
were adjusted by the temporally closest reliable values.
We used the Swiss National Grassland Map of the year 2015
(Schmidt et al., submitted) for clipping the previously mentioned da-
tasets to the grassland extent. Further, the dynamics of the long-term
snow occurrence in Switzerland (Fig. S1) are derived from the Climate
Change Initiate (CCI) Land Cover provided by the European Space
Agency (ESA) (Arino and Ramoino, 2017). Elevation zones are ex-
tracted from the Swiss digital elevation model (SwissAlti3D, Swisstopo,
2017a). An overview of all used datasets is provided in Table 1. Data
processing was done in ENVI 5.3., ESRI ArcGIS 10.3.1., and GDAL 2.1.3.
2.3. Concept of C-factor mapping for Swiss grasslands
Firstly, we derived the spatial pattern of Fraction of Green
Vegetation Cover (FGVC
) by LSU from the high spatial resolution
Swissimage FCIR (Section 2.3.1). Secondly, we used FCover300m to
estimate the temporal changes in the FGVC (FGVC
2.3.2). Both approaches, the high spatial and the high temporal one are
combined (Chen et al., 2015;Zhang and Li, 2015) via a normalizing
procedure to result in a set of monthly FGVC maps for Switzerland
(Section 2.3.3). This procedure involves the normalization of the or-
thophoto mosaic Swissimage FCIR with the temporal variations in ve-
getation cover of FCover300m to a given base date. The normalized
high spatial and temporal FGVC
maps of Swiss grasslands were
then converted to SLR maps. The relationship of SLR and the fraction of
vegetation cover (FVC) is based on measured data in alpine grasslands
by Martin et al. (2010) and Schindler Wildhaber et al. (2012). SLRs
were derived from the measured sediment yield for the given FVC
classes proportional to an uncovered soil surface (SLR 100%;
Schwertmann et al., 1987). SLR and FVC describe an exponential re-
lationship (Eq. (1),Fig. 1). The SLRs are multiplied by the corre-
sponding proportion of rainfall erosivity (R
) to result in the C-factor
according to the original approach by Wischmeier and Smith (1978)
and Schwertmann et al. (1987). Monthly R
for Swiss grasslands with
a spatial resolution of 100 m can be obtained from Schmidt et al.
(2016). The processing workow and manipulation of data is visualized
in Fig. 2.
FVC0.048 (1)
2.3.1. Spatial modeling of fraction of green vegetation cover (FGVC
by linear spectral unmixing
Spectral unmixing assumes that the spectrum measured by a sensor
Table 1
Datasets used for C-factor modeling of Swiss grasslands.
Dataset Derived information Resolution Source
Swissimage FCIR Spatial distribution of FGVC
0.25 m spatial resolution, spectral bands NIR, R, G Swisstopo, 2010
FCover300m Temporal variation of FGVC
10-day temporal resolution (2014 to 2016) Smets et al., 2017
with maximum NDVI 16-day temporal resolution (2005 to 2015) Didan et al., 2015
Swiss National Grassland Map Extent of Swiss grasslands of 2015 300 m spatial resolution, improved with swissTLM3D and vector25 Schmidt et al., submitted
CCI Land Cover Dynamic long-term snow occurrence 500 m spatial resolution, annual resolution (1992 to 2015) Arino and Ramoino, 2017
SwissAlti3D Digital elevation model 2 m spatial resolution Swisstopo, 2017a
Rainfall erosivity Rainfall erosivity of Swiss grasslands 100 m spatial resolution, based on 87 rainfall stations Schmidt et al., 2016
FGVC Fraction of Green Vegetation Cover.
DOY Day of the Year.
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
and represented as a mixed pixel is a combination of the spectra of
components within the instantaneous eld of view. As such, the re-
ectance of a mixed pixel is a mixture of distinct spectra (Roberts et al.,
1993;Gilabert, 2000;Heidari Mozaar et al., 2008). In spectral un-
mixing techniques, the mixed pixel is decomposed into a collection of
endmembers and a set of fractional abundances according to the end-
members (Keshava and Mustard, 2002). The image endmembers, also
called pure pixels, are at the vertices of the image simplex in an n-
dimensional space (Smith et al., 1985). Pixels dened as endmembers
are relatively unmixed with other endmember signals (Rogge et al.,
2007). Among the spectral mixture methods, the LSU is by far the most
common type (de Asis and Omasa, 2007). LSU assumes that the in-
coming radiation only interacts with a single component of surface and
is represented in a mixed pixel without multiple scattering between
dierent components (van der Meer, 2010). Although this is a crucial
assumption, the eects of intimate association between the components
have been found to be relatively minor (Kerdiles and Grondona, 1995).
LSU is expressed as the spectral reectance (R
) of the mixed pixel in
band i as followed (Smith et al., 1990;Hill et al., 1995;de Asis et al.,
=+ =
εRfr fand
iij i
where j is the number of endmembers, f
the fraction of the pixel area
covered by the endmember j, r
itself is the reectance of the end-
member j in band i and ε
the residual error in band i. In the present
case, the sum of all fractions (f
) is constrained to a value of 1 (100%;
Heinz and Chein-I-Chang, 2001). A root-mean-square-error (RMSE) of
the residuals for each pixel indicates the error between the measured
and the modeled spectra whereas M is the total number of bands
(Roberts et al., 1999;Dennison and Roberts, 2003;Bachmann, 2007):
()2 ( )2
is the measured and b
is the modeled signal of all the bands M. A
small RMSE indicates that endmembers are appropriately selected, and
its number is sucient (Mather and Koch, 2011). LSU of QuickBird data
was already applied with reasonable results for deriving vegetation
parameters for an alpine grassland catchment in Switzerland
= 0.85 in relation to ground truth measurements; Meusburger
et al., 2010a). However, QuickBird data is too cost intensive and het-
erogeneous for a national assessment and therefore rather applicable
for catchment studies like it was done by Meusburger et al. (2010a,
2010b).Guerschman et al. (2009) use the hyperspectral EO-1 Hyperion
in combination with MODIS data to result in a higher variety of end-
members with a spatial resolution of 1000 m. However, that spatial
resolution of fractional cover is relatively coarse to explain the spatial
patterns of the FGVC, SLRs and C-factors.
In the present study, orthophotos (Swissimage FCIR) with a national
coverage and resampled resolution of 2 m (resampled from 0.25 m to
0.5 m) were used. The spatial assessment for deriving FGVC
Fig. 2) is based on all three bands of the Swissimage FCIR. ENVI 5.2
provides a Pixel Purity Index tool (PPI) to automatically identify the
most spectrally pure pixels of the image, designated to be the mixing
endmembers (Pal et al., 2011;RSI Research Systems, 2004). PPI works
with an iterative process by counting the number of times a pixel is
registered as extreme pixel for each run. Pixels that appear to be ex-
treme most often are then endmembers (González et al., 2010). We
performed 10.000 iterations with a threshold value of 2.5 and identied
a maximum of 100.000 pure pixels. The application of LSU can result in
n + 1 endmembers where n is the number of bands (Phillips et al.,
2005). PPI based on the three bands (G, R, and NIR) of Swissimage FCIR
and determined the following endmembers namely i) vegetation, ii)
bedrock, bare soil, asphalt, and iii) shade. These endmembers are the
typical groups of endmembers which are distributed all over the
grassland areas in the country (Roberts et al., 1993;Adams et al., 1995;
Theseira et al., 2003;Meusburger et al., 2010a). Although the spectrum
of water is relatively pure, water was not selected as an endmember
since it is occurring only locally (Adams et al., 1995).
Swissimage FCIR has undergone a Minimum Noise Fraction (MNF)
rotation before the selection of purest pixel and unmixing (Green et al.,
1988). The MNF rotation is a two-step principle component analysis
and used to determine the inherent dimensionality of the image data, to
improve the signal-to-noise ratio and reduce the processing time
(Boardman and Kruse, 1994;Nascimento and Dias, 2005). MNF can
improve the quality of the resulting abundance maps by a decorrelation
of the bands (van der Meer and de Jong, 2000). Furthermore, since the
Fig. 1. Negative exponential relationship of the fraction of
vegetation cover (FVC) and the soil loss ratio (SLR). The
relationship of FVC and SLR results from rainfall simulations
by Martin et al. (2010) (brown dots) and Schindler
Wildhaber et al. (2012) (green dots). (For interpretation of
the references to color in this gure legend, the reader is
referred to the web version of this article.)
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
spectra are neither purposed to be linked to laboratory and eld re-
ectance spectra nor to be meant for temporal approaches, a transfor-
mation of encoded-radiances in digital numbers (DN) was not required
in this study (Adams et al., 1995;van der Meer, 2002).
A well-known problem of FGVC mapping is its underestimation due
to the presence of dry vegetation (Meusburger et al., 2010a, 2010b).
This problem can either be addressed by long-wave spectral bands in
hyperspectral sensors at the expense of spatial resolution (Guerschman
et al., 2009) or by a calibration of the approach. As we aim to explain
the spatio-temporal dynamics in soil erosion for Switzerland, we
decided to preserve the high spatial resolution of our dataset (Swiss-
image FCIR) and followed the second option by using 1000 calibration
points (FGVC
) to calibrate the FGVC
(based on the LSU) and to
identify potential biases in the automated assignment of vegetation
abundances. These points are randomly set for grassland areas. The
is estimated user-driven for each point based on the 0.25 m
resolved Swissimage FCIR and RGB. Besides that, the types of vegeta-
tion (photosynthetic and non-photosynthetic grassland, clipped grass,
forest) or non-vegetation (shade, asphalt), slope degree and exposition
are recorded. Although the calibration procedure assesses dry vegeta-
tion, it is not to be dierentiated from bare soil in the LSU approach.
Thus, the endmember of bare soil includes e.g. non-photosynthetic
grassland. Thereby, the unmixed vegetation cover can be calibrated by
the biases of dry vegetation. The density of optimization points is
Fig. 2. Processing workow (rectangles) of the used and derived datasets (parallelograms; detailed description of the datasets see Table 1) to result in spatio-
temporal C-factors of Swiss grasslands.
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
37 km
, corresponding to one optimization point for each 6 to 6 km on
average. An acquisition of ground truth data with a representative
distribution in the eld is hardly feasible on a national scale.
2.3.2. Temporal mapping of fraction of green vegetation cover
and FGVC
Temporal variations of the fraction of green vegetation cover
) are provided within the FCover300m dataset. We aver-
aged three les of the same date by the years 2014 to 2016 to a short-
term mean fraction of green vegetation (FGVC
; see Fig. 2;Smets
et al., 2017). Each of the three years of FCover300m is represented by a
set of 36 les (108 les in total) in a 10-day resolution from 10th of
January to 31st of December. The deviation of FGVC
to a base
date is determined on a per pixel scale (FGVC
) to be used for
normalizing the FGVC
in the following Section 2.3.3. The proces-
sing of the FCover300m data is done within the Copernicus program
where FCover is derived from the leaf area index and further canopy
structural variables (Smets et al., 2017). Concerning its computation,
FCover300m is more robust than classical vegetation indexes like NDVI
which has stronger dependencies on geometry and illumination of
surface cover (Weiss et al., 2000;Fontana et al., 2008).
A series of 253 NDVI datasets from 2005 to 2015 of the MOD13Q1
(Didan et al., 2015) were used for determining this respective base date
as mean peak growing season indicated by the maximum NDVI within a
year (Leilei et al., 2014). Fontana et al. (2008) demonstrate that the
relationship between plant growth records in alpine grasslands and
NDVI is quite remarkable. Busetto et al. (2010) use a time series from
2005 to 2007 of MOD13Q1 to determine the start and the end of the
growing season of larches in the alpine region. For more robust results
we averaged all ten years by each specic recording date to derive a
mean NDVI per recording date for Switzerland. A correction of snow
cover like it was done by Busetto et al. (2010) was neglected in the
study as we are only focusing on the assessment of the peak growing
season and not on minimum NDVI. The maximum NDVI of all the
averaged datasets was selected for each cell and the corresponding DOY
assigned to the associated cell. If a cell contained a no data value, it was
skipped and the averaging done over the cells of the remaining year(s).
2.3.3. Merging of spatial and temporal fraction of green vegetation cover
As Swissimage is a mosaic of tiles recorded at heterogeneous dates,
the vegetation cover can be assumed to be dierent between tiles ac-
cording to its recording date. We used a normalizing process to make all
tiles comparable. Therefore, the FGVC
are normalized to a base
date. The spatial results, as well as the temporal results, are meant for
being combined to spatio-temporal FGVC
of grasslands (see
Fig. 2). First of all, we extracted the recording dates of each along-track
scanning stripe, and spatial joined the dates with the 3432 image tiles.
In cases of multiple recording dates, we used the mode to extract the
most common date. Tiles with same recording dates were aggregated to
a multiple tile mask (Fig. S2) and later used to clip the FGVC
cording to their recording dates.
tileset of a specic DOY i can be normalized to that
base date by weighting it with the the relative change of the
to the same base date as expressed in Eq. (4):
norm i spatial i deviation i spatial i (4)
Thus, tiles recorded early in the season where the plant growth can
be assumed to be low are weighed by a greater FGVC
compared to an image tile recorded close to the base date.
are merged to a new raster which represents a na-
tional map of FGVC at the dened base date. The normalized composite
raster of the base date can then be recalculated to other dates.
2.4. Spatio-temporal mapping of grassland C-factors by considering soil loss
ratios (SLRs) and rainfall erosivity (R-factor)
Originating from the FGVC
, the SLR can be calculated with
the relationship proposed in Eq. (1). SLRs express the ratio of soil loss of
an area with a certain plant development relative to an uncovered
surface (Renard et al., 1997). The SLRs are weighted with the ratio of
the total annual rainfall erosivity (R
) of the same period to result in
the C-factor. The R
can be derived from monthly R-factor maps
which exist with a high spatial resolution (100 m) for Switzerland
(Schmidt et al., 2016). Monthly rainfall erosivity maps (100 m spatial
resolution) for Switzerland are generated by regression-kriging of 10-
min rainfall records at 87 automated gauging stations (19.5 yrs. mea-
suring sequences) and with the use of up to ve spatial covariates. The
12 maps have a mean R
of 0.51 and a mean RMSE of
93.27 MJ mm ha
with highest uncertainties in winter
due to generally low rainfall erosivity. The authors have discussed the
variability of monthly R-factors for Switzerland in detail. R
can be
assessed by calculating the monthly fraction of R-factor of the sum of all
12 maps. For the present purpose of Swiss grasslands, the monthly
national maps of the R-factor are clipped to the extent of the improved
Swiss National Grassland Map (Schmidt et al., submitted). The R
maps are multiplied with the SLR maps for grassland to result in
monthly C-factor maps with a high spatial resolution. For each month
we averaged the three corresponding FGVC
maps to monthly
FGVC maps to comply with the temporal resolution of the R-factor
3. Results and discussion
3.1. Spatial pattern of the fraction of green vegetation cover of Swiss
The optimized LSU of the Swissimage FCIR enables the dier-
entiation of the FGVC
as well as the fractions for bare soil and
bedrock. Spatial patterns of FGVC
are visualized on a national
scale as well on a local level (Fig. 3). Such an analysis of the degree of
fractional vegetation cover is of high relevance when categorizing land
use for potential hot spots of erosion since it is more likely that an
erosion process starts from the uncovered or bare soil.
The dimensionality of the Swissimage FCIR stays unchanged after
noise segregation by MNF. The estimated ranges of FGVC
0.56% outliers outside the LSU constrained range of 0 to 1 (100%),
which indicates that one or more of the endmembers chosen for the
analysis is probably not well-characterized or that additional end-
members might be missing (RSI Research Systems, 2004). These out-
liers were omitted. They predominantly consisted of constructed en-
vironments (buildings, streets) that could not be masked in the
grassland areas (Schmidt et al., submitted). The RMSE of the LSU for
Switzerland is 22.6%. Higher uncertainties generally occur in the val-
leys of the Alpine foothill (Fig. 4). One reason for the high RMSE is the
incorrect separation of grassland from arable land due to the coarse
resolution (300 m) of the grassland map based on CCI Land Cover.
The mean FGVC of the 1000 calibration points (FGVC
; 61%)
identies a systematic underestimation of the mean FGVC
by 22% which is close to the mean RMSE. The highest discrepancy
between FGVC
and FGVC
mainly arises by an erroneous classi-
cation of non-photosynthetic vegetation (33% deviation), shades and
artifacts (42% deviation), and forested areas (46% deviation). The
segregation of non-photosynthetic vegetation and bare soil is impeded
due to the very similar spectral characteristics. Shaded areas and arti-
facts disrupt the spectral signal of vegetation cover which is visually
detectable but automatically assigned with a very low degree of cov-
erage. The pattern of discrepancy between FGVC
and FGVC
show a strong dependency to slope exposition. Highest deviations up to
34% are present at northern exposed slopes. All FGVC
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
calibrated by adding the amount of mean underestimation to each grid
cell. Subsequently, we used the calibrated FGVC
for all further
calculations. The accuracy of the LSU approach could be increased with
a more accurate grassland map and a higher number of spectral bands
as it was already discussed in Meusburger et al. (2010a). A new
orthophoto of Switzerland (Swissimage RS; Swisstopo, 2017b) with
four spectral bands (NIR, R, G, B) is about to be released in 2020. Such
an increase in bands could result in an additional endmember and
might improve the LSU.
Fig. 3. Spatial patterns of the fraction of green vegetation cover (FGVC
) and the orthophoto Swissimage RGB (bottom right) on dierent scales. The FGVC
presented on a national and a local scale (spat. Resolution 2 m). The Swissimage RGB (spat. Resolution 0.25 m) represents the landscape on the local level. (For
interpretation of the references to color in this gure legend, the reader is referred to the web version of this article.)
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
3.2. Temporal variation in the green vegetation cover of Swiss grasslands
The annual distribution of the mean FGVC
for Swiss grass-
lands visualizes the seasonal dynamic of grasslands with periods of
dormancy and growing (Fig. 5). Higher FGVC
lasts until the end
of October (approx. DOY 304) in lower elevations (Colline and Montane
zone) of northern Switzerland. According to FCover300m, an
below 40% is present for most of the Swiss grasslands from
December to February. The annual distribution of the FGVC
comprehensive and complies with the typical expectable grassland
plant growth cycle (Fontana et al., 2008;Filippa et al., 2015;Inoue
et al., 2015). The lack of FCover300m data mainly covers the northern
latitudes of Switzerland. According to the high solar altitude in
summer, missing values are relatively rare during that season. Winter
records are comprised of a higher number of no data values due to snow
cover (especially in the Nival zone), sun path and cloudiness (Camacho,
2016). Thus, erosion in winter continues to be a blank spot, because we
can neither observe changes in FGVC below the snow cover (which will
aect the SLR and C-factor) nor assess the erosivity induced by snow
movement and snowmelt (which will aect the R-factor) (Ceaglio et al.,
2012;Meusburger et al., 2014;Stanchi et al., 2014). We excluded no
data pixels (indicating snow) from the dataset if they are presented in
all the three averaged years. The FCover300m still is in demonstration
mode and has only undergone a validation over Europe yet (Camacho,
2016). Therefore, uncertainty could be introduced in the absolute
fraction of green vegetation cover. Nevertheless, as all the 10-day data
are assessed identically, the relative deviation of the values can be
deemed correctly.
Based on the MOD13Q1 data, the long-term (2005 to 2015) max-
imum NDVI of the most considerable proportion of pixels is DOY 177
(26th of June, Fig. S3). We used the 30th of June (DOY 181) as the base
date as this date has a high temporal proximity to the maximum NDVI
of our analysis. This is in agreement with Jonas et al. (2008) who
proposed the 6th of July as the mean date of the maximum height of
grassland cover for elevations between 1560 and 2545 m a.s.l..
According to model results by Garonna et al. (2014), the growing
season in the alpine zone starts at DOY 118 and lasts until DOY 266.
in relation to DOY 181 marks a positive trend from
DOY 181 to DOY 232 which determines the peak growing season for the
national grassland area (Table 2). The minimal FGVC in relation to DOY
181 is met on DOY 20 with a reduction of 58% in green vegetation
3.3. Spatio-temporal patterns of the fraction of green vegetation cover of
Swiss grasslands
The mean FGVC
of Swiss grasslands on DOY 181 (30th of
June; Fig. 6) is 60%. Grasslands next to the border of Austria (Cantons
Appenzell and St. Gallen) have the lowest FGVC
. These Can-
tons (see a map of Swiss cantons in Fig. S4) are fully dominated by
meadows and alpine pastures (Table 3;Federal Statistical Oce
Switzerland, 2017). As the management of these grasslands is very in-
tense (grazing, fodder), the FGVC
is comparatively low. In-
tense grazing causes a signicant limitation in grass growth (Bilotta
et al., 2007;Mayer et al., 2009) which results in a degradation of ve-
getation cover (Yong-Zhong et al., 2005). These regions have one of the
highest mean livestock unit (1.7 per ha; Table 3) and mean share of
grazing livestock farming (78.8%). Hence, most of the areas in the re-
gion are already mowed at the 30th of June (typical mowing period for
St. Gallen is DOY 166 to DOY 196; Zwingli, 2017). The whole Swit-
zerland experienced a land use intensication of grassland over the last
decades. It is apparent by an increase in stocking rates (~50% increase
of sheep numbers during 40 years) and an alteration in grazing systems
(permanent shepherding replaced by uncontrolled grazing, Troxler
et al., 2004).
3.4. Spatial and temporal hot-spots of C-factors on Swiss grasslands
The monthly maps (Fig. S5) are averaged to seasonal maps of C-
factors for grasslands (Fig. 7). They represent the high temporal and
Fig. 4. RMSE of the calculated abundances based on LSU for Switzerland. (For interpretation of the references to color in this gure legend, the reader is referred to
the web version of this article.)
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
spatial variability of the C-factors for grasslands throughout a year.
According to the modeling results, relative high C-factors in winter can
only be observed in the Jura mountain at the border to France and the
western Alps. These patterns are mainly controlled by the ratio of the
annual rainfall erosivity (R
;Fig. 8). The whole alpine range ex-
periences increased values in spring. The distribution of C-factors in
summer for Swiss grasslands is relative diuse with a spatial cluster in
the north-eastern region of Switzerland (Cantons Appenzell and St.
Gallen) which is a result by the low FGVC due to intense grassland land
use (see Section 3.3) and the high rainfall erosivity. Absolute C-factors
are decreasing in fall but with regional pattern of high C-factors at the
southern and eastern Alps. The minimum C-factors within a year are
covering the lowland areas of Switzerland in winter. Maximum C-fac-
tors are observable in the previously mentioned region of the Cantons
Appenzell and St. Gallen (close to the border of Austria) in summer.
The mean annual C-factor for Switzerland is 0.012 (Table 4). Lowest
mean C-factors of Swiss grasslands can be observed in January (0.003),
highest in the summer months July (0.024) and August (0.025) (Fig. 9).
The maximum C-factor in August is about 8 times higher than the
minimum C-factor in January. The trend marks an abrupt increase of C-
factors from April to August with a decrease in its low winter values.
The natural plant growth cycle determines the annual trend of FGVC. As
the C-factor is not solely related to FGVC but further a product of SLR
and weighted R-factor ratios, the trend of the C-factor is inuenced by
the regional and temporal rainfall erosivity pattern.
The rainfall erosivity, as well as the FGVC, is controlled by elevation
level (Fig. 10). The C-factors per month and elevation zone follow ty-
pical patterns. Highest C-factors can be observed in the Alpine zone.
The Subalpine, Alpine and Subnival zone show more than one peak
with highest C-factors. The Colline and Montane zone have only one
maximum in August. The C-factors in all elevation zones are lowest in
the winter months January and February. FGVC in winter is low due to
the reduced plant growth. The here excluded presence of snow cover in
winter results in a delay of increasing FGVC with elevation after melt-
out. The typical melt-out at elevations between 1560 and 2545 m a.s.l.
Fig. 5. Mean (2014 to 2016) FGVC
for Swiss grasslands. Mean FGVC
are derived and averaged from FCover300m from 2014 to 2016 (DOY = day of the
year). (For interpretation of the references to color in this gure legend, the reader is referred to the web version of this article.)
Table 2
Mean national deviation of FGVC (FGVC
) to the base date of DOY 181
(30th of June) by FCover300m.
to DOY
181 in %
to DOY
181 in %
10 Jan 10 57 191 Jul 10 2
20 Jan 20 58 201 Jul 20 3
31 Jan 31 55 212 Jul 31 3
41 Feb 10 53 222 Aug 10 3
51 Feb 20 51 232 Aug 20 1
59 Feb 28 50 243 Aug 31 2
69 Mar 10 49 253 Sep 10 6
79 Mar 20 44 263 Sep 20 9
90 Mar 31 39 273 Sep 30 14
100 Apr 10 31 283 Oct 10 20
110 Apr 20 25 293 Oct 20 45
120 Apr 30 24 304 Oct 31 34
130 May 10 22 314 Nov 10 40
140 May 20 20 324 Nov 20 45
151 May 31 17 334 Nov 30 48
161 Jun 10 10 344 Dec 10 53
171 Jun 20 5 354 Dec 20 56
181 Jun 30 0 365 Dec 31 56
DOY Day of the Year.
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
is recorded by Jonas et al. (2008) and Fontana et al. (2008) at DOY 147.
Large areas of Switzerland show a snow occurrence in winter (Fig. S1).
Protection of grassland soils by plant cover is relatively low in winter
but simultaneously aected by only very low rainfall erosivity. How-
ever, the tremendous impact of snow gliding on exposed soil surfaces
during winter might be a crucial impact (Meusburger et al., 2014).
Although the fraction of vegetation cover is increasing in summer for all
the grasslands, the weighting with the R
causes a high C-factor. As
discussed in Schmidt et al. (2016), a signicant fraction of the annual
rainfall erosivity is within the time window between June and Sep-
tember. The predominantly glaciated Nival zone (> 3100 m a.s.l.)
could not be considered due to a small proportion of grassland areas
(0.6% of the zone).
Cantons in the east of Switzerland (Fig. S6) have slightly higher C-
Fig. 6. Spatial pattern of the C-factor for grasslands in Switzerland for the base date DOY 181 (30th of June; spatial res. 2 m). (For interpretation of the references to
color in this gure legend, the reader is referred to the web version of this article.)
Table 3
Averaged seasonal FGVC
and agricultural intensity (Federal Statistical Oce Switzerland, 2017) of the year 2016 per Swiss Canton.
Canton Short name FGVC
(%) Livestock unit (per hectare) Grain farming
(%) Grazing livestock farming
annual winter spring summer fall
Aargau AG 44.5 30.6 50.2 55.0 40.6 1.2 24.1 37.2
Appenzell Ausserrhoden AR 28.7 16.8 29.6 37.5 28.3 1.5 0.1 85.8
Appenzell Innerrhoden AI 46.6 29.7 45.5 61.7 45.3 1.9 0 78.5
Basel-Landschaft BL 40.6 27.0 45.1 51.7 36.7 1 15.7 46.0
Bern BE 50.0 27.7 46.1 70.4 46.8 1.3 12.9 64.4
Fribourg FR 51.7 32.0 54.2 68.4 50.8 1.4 16.9 55.7
Glarus GL 48.4 20.5 34.0 72.4 40.7 1.3 0.1 95.4
Graubünden GR 43.0 21.2 28.1 60.6 36.3 0.9 1.7 77.0
Jura JU 58.3 35.1 61.3 77.7 56.2 1 15.1 65.0
Lucerne LU 52.2 36.1 56.6 65.7 49.7 2.1 9.7 56.3
Neuchâtel NE 58.2 29.3 56.5 79.9 58.7 0.9 8.3 63.4
Nidwalden NW 47.9 26.9 44.8 69.5 43.2 1.7 0 88.3
Obwalden OW 48.2 26.0 39.1 69.6 42.8 1.8 0 88.9
Schahausen SH 50.1 32.3 55.5 65.2 42.6 0.8 33.9 14.5
Schwyz SZ 49.4 27.6 46.0 68.5 45.8 1.4 0.4 86.5
Solothurn SO 50.7 30.4 54.5 67.2 47.5 1.1 18 53.1
St. Gallen SG 43.7 25.5 40.9 58.4 40.6 1.7 1.9 72.4
Thurgau TG 30.7 21.5 33.4 37.5 29.1 1.7 17.5 38.4
Ticino TI 46.5 24.5 29.1 63.7 40.6 0.8 4.6 40.2
Uri UR 48.7 21.1 31.1 68.3 40.8 1.2 0 92.0
Valais VS 45.3 22.0 30.9 61.6 38.6 0.7 2.7 40.3
Vaud VD 49.5 25.0 45.4 71.8 47.1 0.8 28.3 24.5
Zug ZG 55.7 32.6 60.0 73.4 54.2 1.7 5.7 70.6
Zürich ZH 50.6 30.7 55.1 65.4 48.8 1 19.2 40.3
of the total agricultural land.
of total farming.
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
factors in the month May to December which is also related to the
dierences elevation level (mean elevation of eastern cantons
1122 m a.s.l., western cantons 865 m a.s.l.) and dierent ratios of R-
factors. The elevation patterns become also visible by comparing the
northern and southern cantons (mean elevation 928 m a.s.l. and
1795 m a.s.l., respectively). The capturing of the relationship between
C-factor and elevation zone meets our expectations and conrms the
plausibility of the input parameters and modeling approach. Bosco et al.
(2009) already observed a relationship of C-factors and elevation level
based on literature values.
Kulikov et al. (2016) studied the temporal variations of C-factors of
Kyrgyz mountain grasslands. They observed decreasing C-factors from
April (immediately after snowmelt) to June in both of their study areas.
They assess the months April and May with the highest potential soil
loss owing to high C-factors with simultaneous high rainfall erosivity. A
soil erosion assessment for a watershed in Brazil (de Carvalho et al.,
2014) reveals highest soil loss in the rainy season where rainfall ero-
sivity is high and the C-factor low. Another combination of dynamic R-
and C-factors, done by Panagos et al. (2014b) for Crete in Greece, as-
sesses March as a month with high rainfall erosivity and low fractional
vegetation cover. Thus, it is important for C-factor assessment to con-
sider the relative timing of peak Cand peak R-Factor.
Panagos et al. (2015b) derived C-factors for grasslands for the 28
European Union member states from FCover300m and ranges of lit-
erature values. Their results present a mean European grassland C-
factor of 0.0435 which is about 3.5 times higher than the one for
Switzerland. However, C-factors in Mediterranean regions, which are
included in the mean European C-factor, are substantially higher than
ones in Central Europe. The surrounding countries of Switzerland have
mean national values between 0.0345 (Austria) to 0.0421 (Germany).
Switzerland's nationwide C-factor for grasslands (0.012) is 70% lower
than the mean of the four neighboring countries (0.0396). A dierent
seasonal trend and lower values compared to Panagos et al. (2015b)
and Kulikov et al. (2016) can be explained by the dierent methods to
compute C-factors and the neglecting of the rainfall erosivity.
Extensive pasture systems might have a positive eect on a dense
vegetation cover. Furthermore, rotation grazing systems or reduced
stocking rates supports the development of a better-closed vegetation
cover (Troxler et al., 2004). The exclusion (e.g., by fencing) of sus-
ceptible soils or spots with a reduced growth period due to a late melt-
out could eectively prevent soils from being mobilized. The re-
generation time of a degraded sward will take many years, and as long
as then the soil surface remains uncovered, it will be fragile and highly
prone to an expansion of soil degradation in the form of erosion.
The study of the dynamic soil erosion is of high importance as
growing seasons in the European Alps are about to be extended under
futures changing climates and shortened snow-cover periods (Dela
and Clot, 2001;Studer et al., 2005;Bänninger et al., 2006;Fontana
et al., 2008;Frei et al., 2017). A long-term eect of the prolonged
growing season for alpine plants would be the favoring of higher and
faster-growing plants with enhanced biomass production. More biomass
production increases the vegetation cover and lowers the C-factor in
summer (Rammig et al., 2010). Simultaneously, the warmer climate
and heavy precipitation events during fall and winter will result in
higher R-factors (after snowmelt; Fuhrer et al., 2006;Rajczak et al.,
2013;Rajczak and Schär, 2017). Sparsely covered soils in late fall
(before snow cover) and early spring are then more susceptible to
erosion by water. A signicant increase and intensication in the cold-
season precipitation is already observable for Switzerland (Widmann
and Schär, 1997;Schmidli et al., 2002;Schmidli and Frei, 2005).
4. Conclusion and outlook
We derived Swiss C-factor maps of grasslands from soil loss ratios
weighted with R-factor ratios in using the most state-of-the-art remote
sensing products for Switzerland (e.g., national orthophoto with an
original spatial resolution of 0.25 m (Swissimage FCIR) and a 10-day
time series of fractional green vegetation cover (FGVC, FCover300m)).
Fig. 7. Spatio-temporal variation of C-factors of Swiss grasslands per season (spar. res. 100 m). C-factors are a product of soil loss ratios and weighted rainfall
erosivity ratios. The seasonal C-factors are an average of three monthly C-factor maps. (For interpretation of the references to color in this gure legend, the reader is
referred to the web version of this article.)
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
The assessment enables the nationwide quantication of the C-factor of
grasslands in its dynamic throughout a year. C-factors are much higher
in winter than in summer due to the relation to rainfall erosivity ratio
and show the expected dependency on elevation gradient. The mean
annual C-factor of Swiss grasslands is 0.012 which complies with the C-
factor of October. An improved spectral resolution will be available
with the future Swissimage RS product which might increase the ac-
curacy and quality of the linear spectral unmixing results. However, the
present results can help to implement soil conservation strategies of an
adopted land use management. The identication of regions in
Switzerland and periods of the year with high C-factors in combination
with the dynamic R-factors might help agronomists to introduce se-
lective mitigation strategies for erosion control of Swiss grasslands. The
mitigation potential of soil erosion particularly relies on the C-factor
since the R-factor is climate driven and not directly to be altered by
human interventions. The utilized grassland areas of Switzerland are of
particular interest since grazing might degrade soil functions and sta-
bility and has an impact on soil cover. Grazing in alpine environments
usually takes place during the most susceptible season. As sediment
yield is reduced to a minimum under closed vegetation cover, priority
should be on keeping the vegetation coverage of grassland high. The
FGVC can be increased, and thus the C-factor lowered by avoiding
grazing on highly susceptible grassland or at least by paying more at-
tention to the choice of the grazing animal species and stocking num-
bers/ diversity. To capture the spread of degraded surfaces, the auto-
mated identication and classication of bare soil spots with a higher
spectral resolution is envisaged for future studies. Beyond the current
state of C-factors, the models can be linked to land use and climate
scenarios to get an idea of future impacts of soil erosion. As we de-
monstrated the usefulness and applicability of the C-factor and its re-
lation to the R-factor, this study also highlights the advantages of USLE-
type modeling. Individual computation and assessment of every single
factor result in a high transparency and veriability of USLE-based
erosion models. Each individual factor does not only have the ad-
vantage to be adjusted and evaluated on its own but also deliver va-
luable conclusions for other environmental issues.
Fig. 8. Monthly ratio maps of the annual rainfall erosivity (R-factor) of Swiss grasslands. Monthly R-factor ratios are the fraction of R-factor related to the total
annual R-factor sum. Rainfall erosivity maps of Switzerland are based on Schmidt et al. (2016). (For interpretation of the references to color in this gure legend, the
reader is referred to the web version of this article.)
Table 4
Mean C-factors of Swiss grasslands per month.
Month Mean C-factor of Swiss grasslands
January 0.003
February 0.004
March 0.005
April 0.005
May 0.018
June 0.016
July 0.024
August 0.025
September 0.015
October 0.012
November 0.013
December 0.008
Ø 0.012
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
Fig. 9. Seasonal distribution of national monthly R-factors (MJ mm ha
), soil loss ratios (SLR; %), and C-factors of Swiss grasslands. C-factors are a
product of soil loss ratios and weighted rainfall erosivity ratios. (For interpretation of the references to color in this gure legend, the reader is referred to the web
version of this article.)
Fig. 10. Mean monthly C-factors of grasslands for dierent elevation zones in Switzerland. (For interpretation of the references to color in this gure legend, the
reader is referred to the web version of this article.)
S. Schmidt et al. Remote Sensing of Environment 211 (2018) 89–104
This work was supported by the Swiss Federal Oce for the
Environment (FOEN) (grant numbers N° N222-0350 and N° P182-
1535). The authors would like to thank all data providers, namely
Swisstopo, Copernicus Global Land Services, European Space Agency
(ESA), and National Aeronautics and Space Administration (NASA) for
making their data available. Furthermore, we would like to thank the
anonymous reviewers for their valuable comments and suggestions to
improve the quality of the paper.
This work was supported by the Swiss Federal Oce for the
Environment (FOEN) (grant numbers N° N2220350 and N°
Declaration of interest
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... The linear relationship between the NDVI and the Cfollows: * where a and b are fitting parameters with values 2 and 1, respectively. This eq been used in numerous research efforts to generate C-factor [18][19][20], some of th in the European continent [18], making the choice of the specific values ideal for The estimated C-values were then cartographically visualised as a map to show distribution of the factor across Europe ( Figure 2). The vegetation coverage a quently, the corresponding NDVI values are inversely proportional to soil er nomena, which means that areas with high NDVI values are less prone to soil er C-values). ...
... The vegetation coverage a quently, the corresponding NDVI values are inversely proportional to soil er nomena, which means that areas with high NDVI values are less prone to soil er C-values). Furthermore, topographical variations also affect the NDVI-derive by affecting both the (a) spectrum reflectance property of the surface and (b) the of the vegetation [19]. A significant number of Sentinel-2 images (10 m spatial resolution) were employed in the GEE platform to create an overall NDVI-based C-factor mosaic for Europe. ...
... where a and b are fitting parameters with values 2 and 1, respectively. This equation has been used in numerous research efforts to generate C-factor [18][19][20], some of them applied in the European continent [18], making the choice of the specific values ideal for our study. The estimated C-values were then cartographically visualised as a map to show the spatial distribution of the factor across Europe ( Figure 2). ...
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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.
... Such combinations were assessed with the CP -Tool (Kupferschmied, 2019), which allows for the calculation of CP values that consider common crop rotation systems in Switzerland. The minimum CP values were particularly reduced to include typical values for permanent grasslands in Switzerland (∼ 0.01; Schmidt et al., 2018b). This simplified approach should be appropriate, considering (i) our focus on connectivity scenarios and linear landscape structures and (ii) the use of the Monte Carlo simulation with the sampling of a wide parameter space that accounts for the uncertainty in the land use classification. ...
... Moreover, the capacity of roads to connect runoff and sediments from arable land to surface waters in Switzerland was extensively described by Alder et al. (2015) and Schönenberger and Stamm (2021). Both studies used a similar semiqualitative modelling approach for identifying agricultural fields that were directly or indirectly (i.e. via the road and drainage networks) connected to surface waters. ...
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The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. Specifically, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM) using high-resolution spatial data (2 m × 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km2) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five subcatchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.
... For example, the score of erosion regulation ES is maximum (5) in Table 2 both for broad-leaved, coniferous, and mixed forests because the matrix only considers the growing season, i.e., full vegetation cover in early summer. However, the erosion control capacity of green vegetation varies throughout the year (Schmidt et al., 2018). Thus, a significant decrease in the score of erosion regulation ES should be expected for broad-leaved forests during wintertime (i.e., the leaf-off season), as stated by the works of Vatandaşlar et al. (2020) and Schmidt et al. (2018). ...
... However, the erosion control capacity of green vegetation varies throughout the year (Schmidt et al., 2018). Thus, a significant decrease in the score of erosion regulation ES should be expected for broad-leaved forests during wintertime (i.e., the leaf-off season), as stated by the works of Vatandaşlar et al. (2020) and Schmidt et al. (2018). ...
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Forested landscapes offer high provisioning capacities for many ecosystem services (ES), yet their capabilities may change in time due to multifaceted ES drivers. Therefore, assessing the changes in individual ES is critical for ecosystem-based management. This study analyzes the spatio-temporal changes in ES provided by a forest-dominated protected area in NE, Turkey. To this end, 18 ES were quantified and mapped using the ES matrix approach for 1985 and 2021. Then, the status of the ES and potential drivers of landscape changes were revealed through the assessment of demographic and management structure changes. The results showed that the multiple ES provisioning capacity of the landscape increased by 7% over 35 years. The capacities for “crops” and “livestock” ES decreased for the same period. The most prominent ES were “wild foods,” “erosion regulation,” and “knowledge systems.” Spatially, ES hotspots accumulated in the northern parts and the core zone of the protected area. The most significant changes occurred in the lowlands, mostly composed of degraded forests and coppices as of 1985 after their transformation into productive forests. The spatio-temporal changes in many ES can be attributed to the declaration of the landscape as a protected area in 1994. The removal of anthropogenic pressure and the impact of conservation management can be evaluated as the main drivers for the positive changes in the total ES capacity. Thus, sound policy structures and effective conservation strategies should be further encouraged for increasing protected areas’ capacities to provide the large array of ES.
... Several case studies have demonstrated that both factors are context-dependent and are influenced by many other factors, including the type and timing of crop cultivation and biophysical land management practices (Gabriels et al., 2003;Schmidt et al., 2018;Taye et al., 2018;Kebede et al., 2020). ...
... Climatic factors can greatly influence vegetation development and efficiency of land management practices, which can, in turn, affect the C-and P-factor values and soil erosion rates. The substantial variation in soil erosion rates across continents (Zhang et al., 2002;Yang et al., 2003;Borrelli et al., 2017), among countries in specific regions (Cerdan et al., 2010;Fenta, Tsunekawa, Haregeweyn, Poesen, et al., 2020), and across climatic zones (Xiong et al., 2019b) can be linked to differences in controlling factors such as vegetation cover and precipitation Schmidt et al., 2018). The greater C-factor values in areas with low rainfall and in arid to semi-arid climatic zones (Fig. 5) is likely attributable to the low vegetation cover, which provides less protection against raindrop impacts, runoff generation, and soil erosion (V asquez-M endez et al., 2010). ...
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Cover management and support practices largely control the magnitude and variability of soil erosion. Although soil erosion models account for their importance (particularly by C- and P-factors in the Revised Universal Soil Loss Equation), obtaining spatially explicit quantitative field data on these factors remains challenging. Hence, also our insight into the effects of soil conservation measures at larger spatial scales remains limited. We analyzed the variation in C- and P-factors caused by human activities and climatic variables by reviewing 255 published articles reporting measured or calculated C- and P-factor values. We found a wide variation in both factor values across climatic zones, land use or cover types, and support practices. The average C-factor values decreased from arid (0.26) to humid (0.15) climates, whereas the average P-factor values increased (from 0.33 to 0.47, respectively). Thus, support practices reduce soil loss more effectively in drylands and drought-prone areas. The global average C-factor varies by one order of magnitude from cropland (0.34) to forest (0.03). Among the major crops, the average C-factor was highest for maize (0.42) followed by potato (0.40), among the major orchard crops, it was highest for olive (0.31), followed by vineyards (0.26). The P-factor ranged from 0.62 for contouring in cropland plots to 0.19 for trenches in uncultivated land. The C-factor results indicate that cultivated lands requiring intensive site preparation and weeding are most vulnerable to soil loss by sheet and rill erosion. The low P-factor for trenches, reduced tillage cultivation, and terraces suggests that significantly decreased soil loss is possible by implementing more efficient management practices. These results improve our understanding of the variation in C- and P-factors and support large-scale integrated catchment management interventions by applying soil erosion models where it is difficult to empirically determine the impact of particular land use or cover types and support practices: the datasets compiled in this study can support further modeling and land management attempts in different countries and geographic regions.
... This factor can be highly variable both temporally and spatially since vegetation cover plays a significant role in mitigating soil erosion. C factor and its variation during the year can be calculated with the help of NDVI (Normalized Difference Vegetation Index) [36][37][38]. In this work, we did not have a multispectral/hyperspectral camera to calculate the NDVI value for each season and study site. ...
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Intense soil erosion in the northern part of the Gerecse Hills, Hungary, is causing significant damage to vineyards in the area. Three vineyards in the Neszmély Wine Region were investigated to quantify the amount of eroded soils. The method was based on monitoring vineyards for one-year between July 2019 and June 2020. Every season, a set of photographs of the vineyards were taken from an unmanned aerial vehicle. The images were processed in a photogrammetric workflow to produce high-resolution digital terrain models (DTMs) and orthophotos, which were used to estimate the soil loss using the Universal Soil Loss Equation (USLE) model. Particular attention was paid to the effect of seasonal variation in vegetation cover and rainfall, and the erosion control effect of the inter-row grassing already applied in the vineyards was also modelled. The results confirm and quantify the extent to which intense summer rainfall has a more significant effect on erosion compared to autumn or winter rainfall.
... Other scientists have used the change in cover or tillage in simulations that result in a quantified impact. For example, Schmidt et al., (2018) derived Swiss C-factor maps of grasslands from soil loss ratios weighted with R-factor ratios in using remote sensing products for Switzerland including national orthophoto with spatial resolution of 0.25m and a 10-day time series of fractional green vegetation cover (FGVC,FCover300m). Other scientists measure and simulate the effect of catch crops or, alternatively, of weeds, on soil erosion Cerda et al., 2018). ...
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This document is one of the outcomes of the Working Package 2 ‘Transferring knowledge for better use of data for evaluating the CAP’ which aims to support the transfer of various solutions included in the Evaluation Knowledge Bank to the CAP evaluation context. This document provides an example of using Earth Observation markers of the project ‘Sen4CAP - Sentinels for Common Agriculture Policy’ in the evaluation of soil erosion. This is a non-binding document, which serves as a knowledge transfer tool which will facilitate the transfer of the Evaluation Knowledge Bank content into practice. The drafting of this document has been carried out by evaluation experts in the context of the Evaluation Helpdesk’s Thematic Working Group (TWG) on the ‘Research projects to support better data for evaluating the CAP’. This document has been developed by Dimitris Skuras in collaboration with Sophie Bontemps (Sen4CAP) and Kornelis Oosterhuis (the Netherlands Enterprise Agency).
... For example, Baiamonte et al. (2019) investigated the RUSLE-R and RUSLE-C factor's time scale effects and their interand intraannual interactions in terms of soil erosion variability. Similarly, Schmidt et al. (2018) also studied on temporal patterns of vegetation to evaluate spatial and temporal variations of RUSLE-C by measuring the temporal variation of vegetation fraction factor based on soil loss rates and RUSLE-R factor ratios. Apart from these, other model researchers have drawn attention to the same issue and pointed out that seasonal changes on soil losses are particularly closely related to the R and C factors (Panagos et al. 2015). ...
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Time-dependent and phenology-based erodibility assessments in agricultural areas are extremely important for a more accurate evaluation of erosion. This paper aims to investigate soil erodibility factor (RUSLE-K) of the "Revised Universal Soil Loss Equation (RUSLE)" model in terms of phenological and seasonal variations in the 50 different winter wheat growing parcels with the interactions other dynamic RUSLE factors (RUSLE-R, RUSLE-C). For that, parcel-based erosion assessments were performed with the help of Dynamic Erosion Model and Monitoring System, digital elevation model, and satellite images in Polatlı, Ankara. Findings showed that RUSLE-K factor varied from 0.0150 to 0.0357 t ha h ha-1 MJ-1 mm-1 during the period the seeding germination to the end of the tillering from autumn to spring, and the lowest RUSLE-K was obtained when the plant was in the three-leaf stage. After the frost-free period, corresponding to the flowering and fertilization stages of the wheat plant, the RUSLE-K values changed between 0.0786 and 0.0976 t ha h ha-1 MJ-1 mm-1. This reveals that erodibility can vary up to nine times due to seasonality. However, the other dynamic model factors are not taken into consideration. Considering all dynamic factors on soil losses, the change coefficients from the highest to the lowest were obtained for RUSLE-R, RUSLE-K and RUSLE-C, respectively. These changes caused soil losses to change by 82% during the year. So, this study is expected to shed new light on studies of wheat or other commonly cultivated crops to accurately assess the water erosion risk as a significant land degradation problem.
The recent unanticipated heavy rain flooding in some parts of Iran caused severe soil erosion events, sediment and water transportation, and material deposition, imposing damages to humans, their activities, and industries. Currently, soil erosion is one of the most critical problems in Iran. Therefore, efficient soil management is necessary for optimal usage, which in turn, will reduce land degradation. The present study employs the Revised Universal Soil Loss Equation (RUSLE) for spatio-temporal estimation of Iran’s soil loss. This procedure has been performed using satellite images and GIS data. The primary data and materials included the digital elevation model, precipitation data collected from 70 meteorological stations, MODIS images, and physical and chemical soil parameters. The results demonstrated an average annual soil loss of 13.05 t/ha/year in Iran. The highest and lowest soil erosion rates occurred in January and July at the mean values of 2.1 and 0.15 t/ha, respectively. Besides, the spatial analysis showed that the highest erosion rates in the Northern and Western steep slopes took place in the Alborz and Zagros mountains, respectively. Also, the average annual soil loss was about 2.6 billion tons, which reduces soil productivity, especially in rangelands, forests, and water pollution. The study findings can be used to identify and prioritize the erosion hotspots of the country to improve the performance of soil conservation measures. Finally, soil loss can significantly be reduced by considering the dynamics of rainfall erosivity and vegetation cover and their effects on soil erosion. This procedure can be accomplished by determining the appropriate cropping pattern in agricultural lands and soil protection operations in erosion-sensitive areas.
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Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing's ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing's ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing's ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas.
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Erosion is one of the most important physical mechanisms causing landscape degradation. Many habitats can be affected as a result of soil losses. It is necessary to know the distribution and amount of soil losses in order to improve landscapes degraded by erosion. The Revised Universal Soil Loss Equation (RUSLE) is a mathematical model used to estimate soil losses. In this study, it is aimed to estimate and map the cover management factor (C factor), which is one of the parameters of the RUSLE model, on a monthly by remote sensing and Geographic Information System (GIS) in the Sarıkızlı basin. For RUSLE-C factor value, Normalized Difference Vegetation Index (NDVI) map was produced from satellite images and then spatially calculated using an exponential regression equation. RUSLE-C factor has the lowest value of 0.24 ± 0.20 in June and the highest value of 0.75 ± 0.18 in December. The most important feature of the RUSLE-C factor is that it helps in determining the areas sensitive to degradation by monitoring the change and how to take soil conservation measures. As a result, the spatial determination of landscape degradation in a faster time with remote sensing/GIS /erosion model integration will enable the reclamation to be done faster, economically, and accurately.
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So far, neither a grassland map, temporal analysis of the conversion of permanent grassland (PG) to other land uses nor the differentiation of permanent and temporal grassland exists for Switzerland. For the first time in Switzerland, we present a Swiss national grassland map for the year 2015 capturing the extent of both, permanent and temporal grasslands (here called grasslands)by intersecting the information of three datasets. We blended the high temporal resolution Climate Change Initiate (CCI) Land Cover of 2015 (processed by the European Space Agency (ESA)), with the high spatial resolution Swiss topographical landscape model “SwissTLM3D” and the landscape model “vector25” both provided by Swisstopo. The final data presents the spatial patterns and the national extent of Swiss grasslands. Furthermore, the recently published (April 2017) CCI Land Cover dataset allow extracting the extent of grasslands for 24 years (1992–2015) with a coarse spatial resolution of 300 m. We used the time series data of the grassland extent to produce annual PG maps from 1996 to 2015. That data enables the identification of the development of grassland extent over two decades. The Swiss national grassland map is used for investigating the spatio-temporal patterns of the soil erosion risk of Swiss grasslands (see Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland, doi:10.1016/j.rse.2018.04.008[1]).
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A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month-time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month-time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision-making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2.
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Projections of precipitation and its extremes over the European continent are analyzed in an extensive multimodel ensemble of 12 and 50 km resolution EURO-CORDEX Regional Climate Models (RCMs) forced by RCP2.6, RCP4.5, and RCP8.5 (Representative Concentration Pathway) aerosol and greenhouse gas emission scenarios. A systematic intercomparison with ENSEMBLES RCMs is carried out, such that in total information is provided for an unprecedentedly large data set of 100 RCM simulations. An evaluation finds very reasonable skill for the EURO-CORDEX models in simulating temporal and geographical variations of (mean and heavy) precipitation at both horizontal resolutions. Heavy and extreme precipitation events are projected to intensify across most of Europe throughout the whole year. All considered models agree on a distinct intensification of extremes by often more than +20% in winter and fall and over central and northern Europe. A reduction of rainy days and mean precipitation in summer is simulated by a large majority of models in the Mediterranean area, but intermodel spread between the simulations is large. In central Europe and France during summer, models project decreases in precipitation but more intense heavy and extreme rainfalls. Comparison to previous RCM projections from ENSEMBLES reveals consistency but slight differences in summer, where reductions in southern European precipitation are not as pronounced as previously projected. The projected changes of the European hydrological cycle may have substantial impact on environmental and anthropogenic systems. In particular, the simulations indicate a rising probability of summertime drought in southern Europe and more frequent and intense heavy rainfall across all of Europe.
Technical Report
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The Global Land (GL) Component in the framework of GMES Initial Operations (GIO) is earmarked as a component of the Land service to operate “a multi-purpose service component” that will provide a series of bio-geophysical products on the status and evolution of land surface at global scale. Production and delivery of the parameters are to take place in a timely manner and are complemented by the constitution of long term time series. The Version 1 of Collection 300m algorithm (known as GEOV3) has been defined by INRA in the framework of the FP7/ImagineS project ( It generates the leaf area index (LAI), the Fraction of absorbed PAR (FAPAR) and the fraction of vegetation cover (FCover) from the 333m resolution PROBA-V daily data and at 10-days frequency. These products have been developed with the objective to provide high level of consistency with Version 2 of Collection 1km products (known as GEOV2) when aggregated at the 1 km resolution. GEOV3 products are associated with quality assessment flags as well as quantified uncertainties. The algorithm provides real time estimation: this force to perform short term projection of the product dynamics. As compared to GEOV2, note two major changes: (i) the SWIR band is not used here since its resolution is roughly twice of that of VIS-NIR domain, (ii) it is not possible to use climatology as background information. This document presents the Quality Assessment results of the Collection 300m PROBA-V LAI, FAPAR and FCover products over Europe. The analysis is mainly focused on inter-comparisons of Historical product (GEOV3 HIS) with the Collection 1km Version 1 PROBA-V products (known as GEOV1), and the available ground reference maps over ImagineS demonstration sites. MODIS Collection5 products (MODC5) were also used for intercomparison. The consistency of the different consolidated periods of the GEOV3 product from NRT to historical products was also assessed over a selection of European validation sites. Note that the GEOV2 products were not available at the time of this study. The results show the overall good spatial and temporal consistency of the GEOV3, which lacks of precision in its first estimate (NRT). The accuracy assessment shows a disparity of results over the agricultural ImagineS sites, with an overall accuracy of 1.02 for LAI, 0.1 for FAPAR and 0.23 for FCover, and systematic positive bias detected for FAPAR in bare areas, and FCOVER regardless the