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/$ -

doi:10.1016/j.

* Correspon

E-mail add

see front matter D 2005 Published by Elsevier B.V.

catena.2005.05.007

ding author.

ress: c.king@brgm.fr (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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