Catena 62 (2005) 79–93
www.elsevier.com/locate/catena
The application of remote-sensing data to monitoring
and modelling of soil erosion
C. King*, N. Baghdadi, V. Lecomte, O. Cerdan
BRGM, Department of Natural Hazards and Land Use Planning, 3 Av. Cl. Guillemin,
BP 6009, 45060 Orleans Cedex, France
Abstract
This paper gives a brief synthesis of the information obtainable from remote-sensing data and
how it can be related to two significant functions of catchment hydrology, namely, the processes of
production and transfer. After presenting examples of the type of information that can be derived
from remote sensing (characterisation of soil surface by different wavelengths, temporal changes of
surface states, incision and geometry of possible water pathways on the surface, etc.), we examine
how this information can provide parameters for input into runoff and erosion models. Finally, we
assess the progress in assimilating remote-sensing data into deterministic models of storm runoff.
D 2005 Published by Elsevier B.V.
Keywords: Remote sensing; Runoff prediction model; Soil erosion
1. Introduction
The application of remote sensing to runoff and erosion studies follows two main lines
of research: on the one hand, the monitoring of signals which describe spatio-temporal
variations of soil surface characteristics, and on the other, major improvements in the
methods used to relate these monitored signals with hydrological and geomorphological
variables relevant to model runoff and erosion processes.
Satellite data provide a significant information source for mapping, monitoring and
predicting current rapid desertification—the advance of deserts to the detriment of
0341-8162/$ -
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* Correspon
E-mail add
see front matter D 2005 Published by Elsevier B.V.
catena.2005.05.007
ding author.
ress: [email protected] (C. King).
C. King et al. / Catena 62 (2005) 79–9380
cultivated land which already affects 3.6 billion ha worldwide. In Europe, a third of fertile
soils is threatened by desertification related to acidification, increasing population density
and intensification of agriculture (Oldeman et al., 1991).
Runoff models have evolved considerably over the last few years both in number and
conception. One of the key points in this evolution is currently the inclusion of soil surface
characteristics, together with their spatial distribution and temporal evolution. These
characteristics have a strong influence on runoff production, which determines the
partition of rain between the soil and its surface, and on the transfer function, which
controls the movement of water over the surface and within the soil. We now recognise
both the importance of soil surface conditions and the need to take into account the spatial
and temporal heterogeneity of the physical medium (Valentin and Bresson, 1992,
Ambroise, 1998). This evolution has therefore directed the community towards an
understanding of the temporal and spatial organisation of systems, and remote sensing can
play a significant role in acquiring relevant data. We also observe a change in the nature of
models, with a strong development of physical models which explicitly incorporate spatial
generalization (Baudez et al., 1997; Blanchard et al., 1999; Wassenaar, 2001).
In order to study the current or potential contribution of remote sensing to this scenario,
we have examined what variables are accessible, whether they are appropriate as initial
parameters required by the models, what degree of use is currently attained (i.e., initial
input or recurrent and periodic input) and what are the limiting factors.
2. Hydrological variables accessible through remote sensing
2.1. Variables linked to the production function
Soil surface characteristics have a major influence on infiltration, thus affecting the
production of runoff to basin outlets and streams. Soil surface roughness, soil porosity, soil
texture and initial moisture content are, amongst others, parameters used by hydrologists
to describe water infiltration. Most of these parameters are not directly accessible by
satellite and only indirect or empirical information is obtainable.
2.1.1. Runoff-contributing areas and their temporal variations derived from optical
sensors
In the context of loamy soils, which represent a third of agricultural soils in Europe, it
has been established that soil loss recorded within a catchment is influenced by the
proportion of areas producing runoff and contributing to runoff at the outlet and by their
connectivity (Auzet et al., 1990). The size and connectivity of areas contributing to runoff
are directly correlated to land use and agricultural practices, which control vegetation
cover. Both canopy and ground cover (plant residue or plant stems) have a significant
impact in enhancing infiltration, reducing flow rates and velocity and protecting the soil
surface from rainfall energy, hence, reducing soil detachment by raindrops. Thus, a first
interesting indicator is to distinguish whether agricultural soils are bare or partly covered
during periods when rainfall is critical (Fig. 1). This information can easily be obtained by
remote sensing, because of significant difference in the spectral response of bare soils and
(a) (b) (c)
Fig. 1. Contributing areas (a) viewed from the ground, (b) perceptible within a SPOT image of a catchment taken
in winter, and (c) estimated by multispectral classification (in white and clear grey tones).
C. King et al. / Catena 62 (2005) 79–93 81
vegetated areas (in several classes) in the optical domain (SPOT, Landsat TM, ASTER,
Ikonos, Quickbird).
In addition, remote sensing provides information on the temporal variations of the
contributing areas and on record changes that can affect the hydrological behaviour of
catchments (Mathieu et al., 1996). In the example developed for Normandy (Fig. 2), a
twofold increase in the areas contributing to runoff was observed over a 7-year period
(Blanchard et al., 1999). We could thus observe the pressure of the common agricultural
policy favouring the choice of cash crops and the intensive management of large fields
(Souchere et al., 2003a,b).
2.1.2. Roughness from radar data
Soil surface roughness affects various hydrologic and erosion processes (surface
depression storage, water infiltration, overland flow velocity as well as overland flow
organisation) (Govers et al., 2000; Cerdan et al., 2002b; Darboux et al., 2002). Baghdadi et
al. (2002) have demonstrated the possibility of deriving surface roughness (in three
classes) for bare soils from the backscattering coefficient for high-incidence radar images
(Radarsat 478) (Fig. 3). Moran et al. (2002) has also shown that synthetic aperture radar
(SAR) was sensitive to differences in soil roughness (related to tillage) for dry bare soils.
This parameter can be used in distributed runoff and erosion models as described at the
end of this paper.
2.1.3. Surface cover and hyperspectral data
Many agricultural practices have been developed to reduce runoff risk (terraces,
conservation tillage, grassed waterways, etc.). Amongst these, and as noted above, the use
of crop residues, either left on the surface or incorporated with a mulch tillage, protects the
0 1 2 3 4 5
rms mean [cm]
-20
-16
-12
-8
-4
0
Bac
ksca
tter
ing
coef
fici
ent
[dB
]
RADARSAT 47°
sigma_0 [dB] = -9.81 - 9.46 exp (-k rms) ; R2 = 0.76
Fig. 3. Derivation of surface roughness using the backscattering coefficient of high-incidence (478) Radarsatimages (Baghdadi et al., 2002).
10
15
20
25
30
35
40
45
1989 1992 1995 1998
% R
unof
f co
ntri
buti
ng a
reas
Sâ‚ne le Bourg (102)
Bourville (83)
Goupillières (180)
Yvetôt-Caudebec (169)
Villers-Ecalles (239)
St Pierre de Bénouville (43)
Val de Saâne (121)
Ganzeville (99)
Fongueusemarre (120)
Fig. 2. Increasing in runoff contributory surfaces between 1990 and 1997 for nine catchments in Normandy from
SPOT XS images (Blanchard et al., 1999).
C. King et al. / Catena 62 (2005) 79–9382
C. King et al. / Catena 62 (2005) 79–93 83
soil from rain drop impacts and mechanical soil desegregation, and reduces runoff rates. It
is worthwhile trying to characterise the amount of soil cover due to crop residues as it can
represent a significant fraction of the total bare soil cover. In the experiment carried out by
CARTEL in the European Floodgen project, radiometric hyperspectral measurements
clearly allowed discrimination of crop residues in the middle infrared domain. Fig. 4
shows that residues can be distinguished from bare soil using either an approach based on
spectral indices in the middle infrared (MIR) or near-infrared (NIR) domains, or an
approach based on mixed spectral analysis (MSA) (Arsenault and Bonn, 2000).
2.1.4. Towards estimates of infiltration parameters
The benefit of surface state characterisation derived from remote sensing lies in the
possible parameterisation of spatial and temporal distributions of the conditions
controlling infiltration, rather than in direct quantitative measurement. However, the
infiltration coefficient itself is not directly measurable. It requires complementary
experiments to develop empirical relationships between soil surface conditions and
infiltration coefficients over a large number of measurements. This was achieved in Haute
Normandie in the case of the European Floodgen project. Experimental surveys provided
the data to build a look-up table (Table 1) with, on the one hand, relations between ground
data (type of crusts, roughness and crop cover) and infiltration rates and, on the other,
soil surface characteristics classes derived from remote sensing (land use, roughness). In
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Wavelength (microns)
Ref
lect
ance
Fig. 4. Spectra of cereal residues (bold line) and of bare moist silty soils (dotted line) (survey GER 2100). The
spectrum of cereal residues (the highest) is characterized by specific absorption peaks of ligno-cellulose at 1.68
Am, 1.93 Am, 2.10 Am and the bands 1.45 Am to 1.48 Am and 2.27 Am to 2.38 Am. The soil spectra are dominated
by water absorption at 1.45 Am, 1.95 Am and 2.5 Am (Arsenault and Bonn, 2000).
Table 1
Relationship between soil surface characteristics and infiltration capacity classes (in brackets are the value in mm/h for the silty soils of the Pays de Caux, France) (Cerdan
et al., 2002a; Lecomte-Morel et al., 2001)
Characteristics observed
in the field
(a) Type of crust (b) Land-use classes
Roughness Crop cover Non-crusted Structural Transitional Sedimentary Roughness Fields and
woods
Well
covered
Poorly
covered
Bare soils Urban
N10 cm 61–100% 0 (50) 0 (50) 0 (50) 2 (10) N2 cm 0 (50) 1 (20) 1 (20) 1 (20) 4 (2)
21–60% 1 (20)
0–20% 1 (20)
5–10 cm 61–100% 0 (50) 0 (50)
21–60 % 1 (20)
0–20% 1 (20) 2 (10) 3 (5)
2–5 cm 61–100% 0 (50) 1 (20) 2 (10)
21–60% 1 (20) 2 (10) 3 (5)
0–20%
1–2 cm 61–100% 1–2 cm 2 (10) 3 (5)
21–60%
0–20% 1 (20) 2 (10) 3 (5) 4 (2)
0–1 cm 61–100% 0 (50) 1 (20) 2 (10) 3 (5) 0–1 cm 3 (5) 4 (2)
21–60% 1 (20) 2 (10) 3 (5) 4 (2)
0–20% 2 (10)
(a) From ground data (type of crusts, roughness and crop cover) and (b) on the basis of parameters derived from remote-sensing data (land use, roughness).
C.Kinget
al./Caten
a62(2005)79–93
84
C. King et al. / Catena 62 (2005) 79–93 85
Table 1, the information provided by remote sensing are in column (b): optic images
provide the various components of land uses (line 2), and inside the bare soil class, radar
images provide the roughness classes. The relation with infiltration classes are empirically
established on the basis of experimental data (Cerdan et al., 2002a; Lecomte-Morel et al.,
2001).
The hyperspectral and microwave radar domains give hope for systematic acquisition
of measured data (dry vegetation cover, surface soil moisture or snow cover, Schmugge et
al., 2002). However, significant limiting factors remain that prevent the systematic
inversion of multi-incidence radar signals (effects of slope on the signal, effects of the
direction of orientated roughness, concomitant effects of surface moisture, etc.) and the
estimation of infiltration from soil surface conditions detected by remote sensing still
remains empirical.
2.2. Variables linked to the transfer function
In terms of runoff processes, the number of characteristics that are accessible from
remote sensing and that can be introduced into the parameterisation of transfer functions
are limited. It includes topographic characteristics (slope, morphology), networks of
natural or artificial incisions and patchiness of land units. Further, their availability
considerably depends on the technical characteristics of the sensors giving access to Earth-
observation images.
2.2.1. Slope and morphology
Topographic data are a prerequisite to any hydrologic or geomorphologic studies. The
basic requirement is a digital elevation model (DEM) from which elevation, slope
intensity and aspect can be calculated. Whilst ground-based methods like traditional
surveying and GPS measurements are still used, topographic surveying now has a long
history of using Earth observation and especially airborne methods. In operational
hydrologic modelling as used by the services in charge of flood monitoring and
forecasting, the requirement in vertical accuracy is less than 50 cm near the rivers. This
can only be satisfied with DEM built from aerial laser. The other sources of DEM are
far from this precision. DEM from optic images such as ASTER, SPOT 5, Ikonos or
Quickbird have a vertical resolution of up to a few meters. DEM from radar images,
such as ERS, Radarsat or SRTM might in the best case have a vertical resolution of 10
m, and by SAR interferometry, the most favourable case is 1 m if atmospheric effects
can be eliminated.
Whatever the source of the DEM, we must bear in mind the importance of the
assessment of errors related to the precision of the DEM particularly when calculating flow
directions (Puech, 2000). Nevertheless, three-dimensional data on slope gradient, slope
curvature and the relative positions of points in the same catchment still remain
determining factors in the modelling of sheet or linear flow.
2.2.2. Incisions
Objects that are currently visible using operational systems such as SPOT 1 to 4 or
Landsat TM are incisions formed at geomorphological scales, i.e., incisions of the
C. King et al. / Catena 62 (2005) 79–9386
hydrographic network, which is only suitable for global studies at large basin or
continental scale, for time intervals of the order of interglacials or even longer. At the
spatial and temporal scales of human-induced accelerated erosion, direct indicator of
preferential flowpaths is, for example, visible at the surface of eroded soils in the form of
incisions (rills, gullies, etc.). They appear on images as linear traces similar to one-
dimensional data. Easily recognisable on aerial photos or on simulation of the future
PLEIADE/CNES Very High Spatial Resolution (VHSR) sensor (sub-metric resolution),
these objects are now accessible by photo-interpretation with currently operational systems
(Ikonos or Quickbird with 1 m of resolution) (Fig. 5). However, automatic retrieval of
these features are not available until now due to the heterogeneity of the object itself as
well as the heterogeneity of their environment. Systematic photo-interpretation would help
to refine the flow network calculated from DEM.
2.2.3. Fragmentation of the land
King (2001), using the STREAM model (Cerdan et al., 2002a), demonstrated the
influence of the relative positioning (aggregation/dispersion) of the runoff-contributing
areas (RCA) inside a catchment. For the same rainfall event, we observed a twofold
increase of the runoff volume produced at the outlet of a cultivated catchment (ca. 90 ha)
depending on the geographical distribution of the RCA: numerous small and scattered
RCA or a few compact and wide RCA.
simulation PLEIADE 1999
10 m
gullies
Fig. 5. Detection of incisions on simulations of the future PLEIADE VHSR sensor.
Very low moderatelow high
Fig. 6. Taking into account the density of polygons corresponding to runoff contributing areas (Blanchard et al.,
1999).
C. King et al. / Catena 62 (2005) 79–93 87
Through remote sensing, we can picture land fragmentation and acquire information
concerning the spatial organisation of fields and plots. A high density stands for coalescent
parcels, potentially increasing the volume of overland flow at the outlet. To take into
account the density (i.e., the degree of fragmentation or coalescence) of areas in the same
category within each catchment, we calculate the number and the size of parcels or groups
of parcels that incorporate areas with similar characteristics (Fig. 6) (Blanchard et al.,
1999).
3. Assimilation of remote-sensing data into runoff and erosion models
We shall now examine the current and potential contribution of remote sensing to
runoff and erosion models on the basis of three examples: (1) an empirical erosion model
at the regional scale, (2) a simple method of evaluating contributing surfaces, (3) an
expert-based distributed runoff and erosion model working at the catchment (0.1–10 km2)
and rainfall event scales.
ERODIBILITY OFPARENT MATERIAL
SOIL CHARACTERISTICS
TOPOGRAPHY LAND USE PRECIPITATION
SLOPE AND AREA DRAINED
SENSITIVITYTO crusting
POTENTIAL SENSITIVITY OFSOILS TO EROSION
EROSION RISK5 classes
HEIGHT AND INTENSITY OF
RAINFALL
SPATIAL INTEGRATION
Fig. 7. Principle of the INRA empirical erosion risk model.
C. King et al. / Catena 62 (2005) 79–9388
3.1. Contribution of remote sensing to a regional empirical model
There are numerous empirical erosion models that rely on the four factors, topography,
land use, intrinsic characteristics of the soil and characteristics of the rainfall events (De Roo,
1993). A regional empirical model developed by Le Bissonnais et al. (1998; 2002) was used
in the administrative region of Haute Normandie (4000 km2). In its initial evaluation (Fig. 7),
a database was elaborated to describe all possibilities of erosion encountered in the area. In
such amodel, the role of remote sensing is very modest; it facilitates the updating of the land-
use components that drives part of the mechanism generating runoff and erosion.
Maps derived from such a qualitative analysis allow the localisation and ranking of the
degree of intensity and the probability of occurrence of erosion risk at the catchment scale. A
regional erosion atlas has been delivered to the regional authorities so as to better target
measures for reducing runoff and mudflow risks (Souadi et al., 2000) (Fig. 8). There exists
Annual Soil Erosion Risk in Haute Normandie (F)Intergration by catchment
Very highhighmoderatelow
very lowbuilt up areas
no data
5 0 5 10 kmN
Fig. 8. Regional map of erosion risk in Haute Normandie (Souadi et al., 2000).
C. King et al. / Catena 62 (2005) 79–93 89
several other studies which used operational optical images (SPOT imagery, Landsat) to
derive land cover information in order to assess regional soil erosion risk in Canada (Cyr et
al., 1995), in Lebanon (Khawlie et al., 2000) or for Europe (Commission of the European
Communities, 1991).
3.2. Contribution of remote sensing for quantitative evaluation of contributing areas
Without going as far as estimating actual erosion, it is possible to adopt a quantitative
approach for those surfaces that can produce runoff. To exploit the role of remote-sensing
data better, contributing areas were estimated operationally within the European Floodgen
project (King, 2001) which has enabled the analysis of catchment in terms of runoff risk.
The study has been very instructive: both in Normandy (France) and Lombardy (Italy), a
marked increase in the proportion of contributing areas is clearly demonstrated within each
catchment. This can be explained by pressure from the Common Agricultural Policy,
which has favoured the rapid extension of intensive agriculture to the detriment of
grassland and winter cereals. In addition, the variations in spatial distribution of
contributory surfaces (degree of fragmentation) have confirmed this analysis (Blanchard
et al., 1999). All these factors favourable to runoff have evolved towards a degradation of
the environmental conditions. These results are consistent with the recent increase in mud
flows in the same region.
In this approach, remote sensing provides information on erosion that, although
incomplete, is nevertheless objective, quantitative and synoptic of those regions requiring
priority action.
3.3. Contribution of remote sensing to the STREAM model
We now consider whether the third example, an expert-based distributed runoff and
erosion prediction model working at the catchment (0.1–10 km2) and rainfall event
R2 = 0.99
0
10 000
20 000
30 000
40 000
50 000
60 000
0 10 00020 000
30 00040 000
50 00060 000
STREAM simulationwith ground data (m3)
STR
EA
M-T
ED
sim
ulat
ions
wit
h re
mot
e se
nsin
g da
ta (
m3 )
Real events
Standard events
1:1
Fig. 9. Correlation between runoff volumes predicted by the STREAM model fed by field data and by the
STREAM-TED model fed by satellite data (Lecomte-Morel et al., 2001).
Table 2
STREAM and STREAM-TED performances with different input data compare to measured runoffs (eight
observed rainfall events) (Lecomte-Morel et al., 2001)
Model Input data R2 ME (m3) RMSE (m3) AUE (%) EF
(0) STREAM Ground 0.94 110.84 1271.73 34.39 0.90
(1) STREAM Ground–agricultural features 0.93 1.79 1193.23 42.82 0.91
(2) STREAM-TED Ground, reclassified 0.93 41.87 1225.18 39.24 0.90
(3) STREAM-TED Remote sensing 0.94 �216.56 1028.21 45.69 0.93
C. King et al. / Catena 62 (2005) 79–9390
scales can be fed with satellite data. The model used is STREAM, which has been
calibrated and validated on the silty soils of Normandy (Cerdan et al., 2002a, 2002c;
Souchere et al., 2003a,b). It requires parameters at the plot and catchment scales. Input
data for the original STREAM model are (i) ploughing direction, (ii) orientated
roughness and (iii) slope aspect for the determination of the runoff circulation network;
and (i) structural soil surface state (crusting stage), (ii) random roughness, (iii)
percentage of vegetation cover and (iv) antecedent rainfall amount for computing the
runoff volumes. The STREAM-Ted model is a modified version which was developed to
incorporate remote-sensing data instead of field data. It requires slope aspects, land-use
classification from remote-sensing data, surface roughness indices from Radarsat and
antecedent rainfall amount.
STREAM-TED was tested against STREAM with data coming from an experimental
catchment (Bourville, 1100 ha) for the months of January, February and March 1998
(Landsat TM data give the evaluation of land use and those from Radarsat 478 give the
cartography and roughness of bare soils, both taken for the same period of February
1998) (King et al., 2005). A good correlation is obtained between the runoff predictions
by STREAM and STREAM-TED, whatever the type of rainfall events considered
(Lecomte-Morel et al., 2001) (Fig. 9 and Table 2). These results confirm that it is
possible to extend the application of a model (1) outside the catchment where it was
calibrated and (2) free of new ground data. Optical and radar satellite data can be
substituted successfully which opens up new possibilities for quantifying runoff at the
regional scale. This study only considered one period; however, the temporal monitoring
of the parameters previously described is of course possible by remote sensing. This
brings perspectives for simulating catchment hydrological responses according to various
climatic or land use scenarios.
4. Conclusion
To direct future research on the use of remote sensing for soil conservation, the
community will benefit from the impetus of current research, including better
consideration of soil surfaces characteristics and the understanding of their influence
on infiltration and on the spatial distribution of runoff and sediment transport in the
landscape. Table 3 is a brief abstract of the main current requirements expected from
remote sensing. The first set of data linked to the production function is shown to be
particularly dependent on the spectral quality of the sensor in order to attain good
Table 3
Summary of data accessible through remote sensing
Data linked to Variables Dimension Characteristics
required
Spatial
requirement
Temporal
requirement
Production
function
Runoff-contributing areas
(bare soils, crusted,
non permeable, truncated, etc.)
2D Spectral
resolution
200 m2 Every month
Vegetation cover (degree of cover,
type of cover, presence of
residue, etc.)
2D Spectral
resolution
200 m2 Every month
Changes of surface states 4D Multi-temporal
series
200 m2 Every month
Transfer
function
Linear incisions (rills, gullies,
furrows, natural or artificial
waterways, etc.)
1D High spatial
resolution
0.50 m Every season
Fragmentation of land 2D Spatial and
spectral
resolution
2500 m2 Every year
Slope and morphology 3D Stereoscopic
data
Vertical 2 m –
C. King et al. / Catena 62 (2005) 79–93 91
discrimination between surface states. The second set of data linked to the transfer
function is particularly related to the spatial resolution of the sensors (Table 3).
Research efforts are needed to achieve a better quantitative description of soil
surface features as, for example, the derivation of soil surface storage (Darboux et al.,
2002), friction or infiltration coefficients. Much more experimental data are required as
well as a necessary empirical stage before relations can be established between the
signals discriminated by remote sensing and the physical parameters describing
hydrological and geomorphological processes. There are also new sensors to be
investigated for possible improvements: very high optical resolution, high-reso-
lution spectral, and multi-incidence and multi-polarization radars (A-SAR, Radar-
sat, etc.).
Spatial heterogeneity of hydrological variables is one of the main obstacles to
effective and operational modelling at the catchment or regional scale, assimilation of
input remote sensing has therefore a great potential. We are already reaching an
encouraging stage for predicting runoff by integrating optical and radar data into a
spatial runoff model based on the description of soil surface characteristics (STREAM-
TED). We must continue in this way with models validated for specific contexts. A
robust experimental protocol will be necessary to validate and confirm the efficiency of
such assimilation.
Acknowledgments
This article was made possible due to the support of the BRGM Research
Programme (01METR0) and the FLOODGEN European programme (CEE-CEO
ENV4CT 96 0368).
C. King et al. / Catena 62 (2005) 79–9392
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