Use of passive microwave remote sensing to monitor soil moisture

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Use of passive microwave remote sensing to monitor soil

moisture

Jean-Pierre Wigneron, T. Schmugge, Andre Chanzy, J.C. Calvet, Yann Kerr

To cite this version:

Jean-Pierre Wigneron, T. Schmugge, Andre Chanzy, J.C. Calvet, Yann Kerr. Use of passive microwave

remote sensing to monitor soil moisture. Agronomie, EDP Sciences, 1998, 18 (1), pp.27-43.

�hal-02695648�

Review

Use

of

_passive

microwave

remote

_sensing

to

monitor

soil moisture

Jean-Pierre

_Wigneron

_a

Thomas

_Schmugge

_b

André

_Chanzy

Jean-Claude Calvet

d

Yann

Kerr

e

a

*Inra, Bioclimatologie, Agroparc, 84914 _Avignon cedex _9, France

b

USDA/ARS, Hydrology Lab, BARC-West, Beltsville MD, USA

c

Inra, Sc du _{sol, Agroparc,} 84914 _Avignon cedex 9, France d

Météo France / CNRM, 42, av _Coriolis, 31057 Toulouse cedex 1, France e

CESBIO, _bpi 2801, 18, av E. Belin, 31401 Toulouse cedex 4, France

(Received 11 December 1997; accepted 21 _January 1998)

Abstract - Surface soil moisture is a

_key

variable to describe the water and _energy

_exchanges

at the land

surface/atmos-phere

interface. _However, soil moisture is

_highly

variable both

_spatially

and

_temporally.

Passive microwave

_remotely

sensed data have _great

_potential

for

_providing

estimates of soil moisture with

_good

_{temporal repetition}

_(on a

_daily

basis) and at

_regional

scale _{(∼10 km).} This _paper reviews the various methods for remote

_sensing

of soil moisture from microwave radiometric _systems. Potential

_applications

from both airborne and

_spatial

observations are discussed in the

fields of agronomy,

hydrology

and

_{meteorology. Emphasis}

in this _paper is

_given

to

_relatively

new _aspects of microwave

techniques

and of

_temporal

soil moisture information

_analysis.

In

_particular,

the _aperture

_{synthesis technique}

allows us now to a address the soil moisture information needs on a

_global

_basis, from space instruments. _{(© Inra/Elsevier,}

_Paris.)

remote

_sensing

/ microwave

radiometry

/ soil moisture /

_vegetation

water status /

_{evapotranspiration}

Résumé - Utilisation des mesures de télédétection micro-onde

_passive

pour le suivi de l’humidité du sol.

Synthèse.

L’humidité de surface du sol est une variable clé _{pour décrire} les

_échanges

d’eau et

_d’énergie

à l’interface surface

ter-restre-atmosphère. Cependant,

l’humidité du sol varie fortement dans le _temps et

_l’espace.

Les données de télédétection micro-onde

_passive

ont un fort

_potentiel

_{pour fournir des estimations de l’humidité du sol} avec une bonne résolution

tem-porelle

(sur une base

_{journalière)}

et à une échelle

_régionale

_(∼ 10 _km). Cet article fait une revue des différentes méthodes

de suivi de l’humidité sol à

_partir

de radiomètres micro-ondes. Les

_{applications potentielles s’appuyant}

sur des mesures

aéroportées

et

_spatiales

sont

_analysées

dans les domaines de

_{l’agronomie, la}

_{météorologie et l’hydrologie.}

Nous insistons

sur des _aspects relativement nouveaux de la

_technique

de mesure micro-onde et de

_l’analyse

de séries

_temporelles

d’humidité du sol. En

_particulier,

la

_technique

de

_synthèse

d’ouverture _permet maintenant de

_répondre

aux besoins en

information sur l’humidité de surface à l’échelle de la

_biosphère,

à

_partir

d’instruments

_spatiaux.

_{(© Inra/Elsevier, Paris.)} télédétection / radiométrie micro-onde / humidité du sol / état

_hydrique

de la

_végétation

/

_{évapotranspiration}

Communicated _by Gérard _{Guyot (Avignon)} *

Correspondence and _reprints E-mail: _{wigneron@avignon.inra.fr}

1. INTRODUCTION

Previous research has shown that microwave remote

_sensing

sensors can be used to estimate

sur-face soil moisture. These estimates can

_provide

a

better

_knowledge

of the

_temporal

and

_spatial

varia-tions in soil

moisture,

which is

_important

in the fields of

_{hydrology, agriculture}

and

_meteorology.

Surface soil moisture

_(hereafter

referred to as _wg

and

_{corresponding roughly}

to the 0-5 cm

_top

soil

layer)

is a

_key

variable in the water _{and energy}

exchanges

at the land

_{surface/atmosphere}

interface. This variable is of crucial

_importance

for several

aspects.

In

_hydrology

and

_meteorology,

the water content

of the surface soil

_layer

is a

pertinent descriptor

of

the water

_exchanges

between the surface and the

atmosphere.

For instance

_Chanzy

and Bruckler

_[7]

showed that wg is a

_key

variable to estimate the

ratio between

_evaporation

and

_{potential evaporation}

over bare soils.

Also,

soil moisture is an

_important

variable to estimate the distribution of

_{precipitation}

between storm runoff and

_storage

and to

_compute

several

_key

_{variables of the land surface energy and} water

_budget

_(albedo,

_hydraulic

_{conductivity,}

_etc.).

In agronomy, surface soil moisture is

_important

for the

_development

_{of crops}

_during

the

_early

phas-es of the

growth

(germination,

development

of the

root

_system,

_etc.)

and also for cultural

_practices

(trafficability

in the

_fields).

_According

to Brisson and Perrier

_[2],

the interest in

_monitoring

the soil surface water status _{for crop simulation models is} threefold:

₁₎

it allows a better estimation of

transpi-ration processes

(the

maximum

_{transpiration}

depends

on the soil moisture status

_through

an

effect of microlocal advection:

_higher

_temperatures

and lower values of air moisture over

_dry

soils

increase the climatic demand at

_plant

_{level); 2)}

it makes it

_possible

to estimate the water status of

plants

at the

_beginning

_{of the crop}

_cycle;

₃₎

it

allows

_modelling

of the mulch

_(soil

_dry

surface

layer)

which is of interest in drastic

_dry

conditions

during

the emergence stage

(in

this case

competi-tion between

_rooting

and mulch

_deeping

_{may be}

observed).

At this

_point

of the Introduction it is

_important

to

distinguish

the surface soil moisture

_(∼

water

con-tent of the 0-5 cm

_top

soil

_layer)

which can be esti-mated

_using

remote

_sensing

_(R-S)

measurements, from the soil water content at

_depth.

_Actually,

the microwave R-S

_techniques

cannot

_provide

direct estimates of the water

_availability

for

_plants

which is in relation to the soil moisture in the root zone

(hereafter

referred to as

_w2)

and root

_depth.

However,

as discussed in this

_review,

_modelling

of

the heat and mass flow within soil can be used to

derive information about the soil water content at

depth

from

_temporal

surface soil moisture informa-tion. Water

_availability

for

_plants

is a

_key

variable to evaluate

_{vegetation transpiration}

which is a

_key

term _{in the energy and} water

_budgets

over

vegeta-tion covers.

In _{summary, the estimation of actual}

evapotran-spiration

ET for

_applications

in

_agriculture,

meteo-rology

and

_{hydrology, requires}

_repetitive

estimates of surface soil moisture

_(to

_{compute direct} evapora-tion from the

_soil)

and to derive information about the soil water content in the root zone

(to

_compute

water flows due to

_vegetation

_{transpiration).}

The estimation of ET over land surfaces is an

_important

issue for

_{meteorological modelling,}

since it is a

basic term of the land surface

_forcing

in mesocale

atmospheric

circulations

_[38].

Soil moisture is

_highly

variable both

_spatially

and

_temporally

in the natural

environment,

as the

results of the

_{inhomogeneity}

of soil

_properties,

topography,

land cover and the

_{nonuniformity}

of

rainfall and

_{evapotranspiration. Remotely}

sensed

data,

which can

_provide

_frequent

and

_spatially

com-prehensive

estimates of the land surface

character-istics,

arouse a

large

interest. The use of microwave

remote

_{sensing techniques}

to estimate surface soil moisture has been the

_object

of much research over

the

_past

two decades

_[46].

In the microwave

_region

of the

_{electromagnetic}

_spectrum,

soil

_reflectivity

is

mainly

driven

_by

the soil moisture content,

_owing

to

the

_large

contrast between the dielectric

_properties

of

_liquid

water and of

_dry

soil. Based on this

prop-erty, active

_(radar

_systems)

and

_passive

_(radiometric

systems)

microwave observations.can

_provide

esti-mates of soil moisture from

_space-borne

_platforms.

low-frequency

bands

_(f

< 15

_GHz)

are _very

attrac-tive since

_they

are insensitive to solar effects

_(as

a source of

_{illumination)}

and

_weakly

sensitive to

atmospheric

effects.

Therefore,

_regular

observa-tions can be made

_(the

microwave sensor can

observe the land surface characteristics

_through

clouds)

and it is

_usually

not _necessary to correct for

atmospheric

gaseous

absorption

and emission effects.

The two

_types

of microwave sensors

_(active

and

passive)

have distinct

_advantages,

which make them better suited for different fields of activities.

Active sensors offer

possibilities

of

high

spatial

resolution: about 10 m for the

_synthetic

_aperture radar

_(SAR)

installed aboard the satellites ERS

(Europe)

and Radarsat

_(Canada).

The measurement

is very sensitive to the

_geometry

of the surface

_(in

terms of soil

_{roughness, vegetation}

structure, row

effects due to _crop rows or

_tillage,

look

_angle,

etc.).

Usually,

in relation to

_{high spatial}

_sampling,

the

temporal repetition

of the SAR measurement is very low and

usually

exceeds 1 month. Over vege-tation covers, the active data have shown _very

_good

capabilities

for

_vegetation

_type

discrimination and forest biomass retrieval.

Passive sensors are limited to a rather coarse

res-olution of about 10-20 km at L-band

_{(1.4 GHz),}

which is an

_optimal

frequency

band for soil

mois-ture

_monitoring.

However,

this

_spatial

resolution is in

_agreement

with the

_grid

scales on the order of

10-100 km of mesoscale

_{meteorological}

and

cli-mate models.

Also,

in

_comparison

with

_radars,

pas-sive

_systems

have a

_greater

_sensitivity

to soil

mois-ture and are less sensitive to surface

_geometry.

Therefore,

simplified

algorithms

can be used to account for soil surface

_roughness

and

_vegetation

structure.

Thus,

the

_passive

data can

provide

global

monitoring

of the earth with a

_{daily temporal}

sam-pling

which is

_particularly

well-suited for mesoscale

_{meteorological applications.}

This paper reviews the current

_{understanding}

of

passive

microwave remote

_sensing

of soil moisture.

Algorithms

and methods

_developed

to retrieve

sur-face variables

_(surface

soil

moisture,

surface

tem-perature

and

_biomass)

from microwave data are

presented.

The discussion includes the

require-ments for sensor

_design,

current

_capabilities

for soil moisture

_monitoring

and recent advances in the

analysis

of

_temporal

soil moisture information.

2. MODELLING OF THE MICROWAVE EMISSION

The

_passive

microwave radiometer measures the

natural thermal emission of the land surface in the microwave

_region

_(frequency

_ranges

_roughly

from 0.3 to 300

_GHz).

The measurement of the

_spectral

brightness

B

f

(Wm

-2

sr

-1

Hz

-1

)

of the surface is

expressed

in terms of

_brightness

_temperature

_Tb

_P

(K),

where

_B

_f

₌ 2 k

_Tb

_P

_/&lambda;

₂

from the

Rayleigh-Jeans

_{approximation}

to Planck’s

_equation

(k

= Boltzmann’s

constant, &lambda;

=

wavelength

(m)).

The

_subscript

_{p denotes the}

_polarization

of the

mea-sured radiation

_(p

= v or _p =

h,

for vertical _or

hori-zontal

_{polarization,}

_{respectively).}

The surface

emis-sivity

e

P

is related to

Tb

P

using

a

_{simple equation:}

e

P

=

Tb

P

/

_T

_eff

_,

where

_T

_eff

is an effective microwave

surface _temperature. The value of

_T

_eff

is close to that derived from thermal infrared measurements.

In the

_following

sections,

the basic

_principles

of the

_modelling

of

_Tb

_P

are

presented.

The remote

sensing

models can be used to describe and

quanti-fy

the microwave measurement

_sensitivity

to the

land surface variables.

Also,

using

model inversion

procedures,

retrieval of the surface variables of interest can be carried out from the remote

sensing

measurements.

2.1. Soil emission

For a smooth soil

surface,

the soil microwave

emissivity

can be well

_approximated

from soil

reflectivity

&Gamma;

sP

:

The reflection coefficient

_R

_P

can be

_simply

cal-culated from the soil dielectric

_permittivity

_&epsiv;

_S

and from the view

_angle

_&thetas;,

_using

the Fresnel

_equations

(R

P

=

R

P

(&epsiv;

S

permittivi-ty

&epsiv;

_(&epsiv;

=

&epsiv;’+i&epsiv;")

is a

_physical

characteristic of the material which describes the

_propagation

of an

electromagnetic

wave in the material. For

_soils,

_&epsiv;

_S

is

_mainly

driven

_by

the soil moisture content and also

_by

the soil textural and structural

_properties.

Several models have been

_developed

in a low

fre-quency range

(1-20 GHz)

to relate the soil

permit-tivity

to soil _parameters

_(soil

_moisture,

bulk

densi-ty,

% of sand and

_clay,

_{etc. ) [15, 53].}

At

_higher

fre-quencies

(f >

20

_GHz),

more

_analyses

are

required

to better characterize the dielectric

_properties

of soils

_[4].

From

_equation

_(1),

the emission of a smooth soil can be

_simply

related to soil moisture

_(through

the

term

_&epsiv;

_S

₎

and to view

_angle

&thetas;.

_However,

_{in the} gen-eral case several factors should be also taken into

account.

First,

surface

_roughness

enhances soil emission and numerous theoretical works have

been and are

_{currently developed}

to relate the soil surface microwave

_{scattering properties}

to the soil

roughness

characteristics

_[18].

For most

applica-tions,

simple

and tractable

_approaches

based on

best fit

_parameters

can be used

_[52].

Also,

the microwave radiation _penetrates

_slightly

within the

_ground

and volume effects influence soil microwave emission. If there are

_strong

vertical

gradients

of moisture and

_temperature

within the

soil,

a

_question

arises: over which soil thickness

should we consider surface soil moisture _wg and

temperature

T

S

?

An

_{approximate approach}

is to

consider the mean value of these variables over a

fixed soil thickness. This thickness

_corresponds

to the so-called

_{’sampling depth’}

_d

_P

_,

and

_depends

mainly

on

_frequency

_(d

_P

&ap; 3 _{cm at} 1.4

GHz,

d

P

&ap;

1 cm at 5

_GHz,

_d

_P

_&ap;

0.5 cm if f > 5

_GHz)

_{[26, 51].}

It is the

_layer

whose dielectric

_properties

determine the

_emissivity.

To

_improve

characterization of the

gradient

effects,

theoretical and

_{semi-empirical}

approaches

can be

_applied

[43 ,61].

For most

_{applications,}

if the soil

_roughness

con-ditions do not

_change

much

_during

the

observa-tions,

the

_emissivity

_e

_SP

can be considered as

monotically decreasing

function of soil moisture wg and can be related to it

_by

the

_general

form

_[54]:

If sufficient

_ground

data are available to calibrate

the coefficient

_a

₀

and _a1, this

_simple

_relationship

proved

to _{be very}

_appropriate.

2.2.

_Vegetation

emission

At low

_frequencies

_{(f &sim;}

0.3-2

_GHz),

the emission of a

_vegetation

_canopy can be well

_{approximated by}

a

_simple

radiative transfer

_{(R.T.) model,}

hereafter referred to as the &tau;-&omega; model. This tractable model is a basic tool to retrieve the surface variables from remote sensed

_Tb

_P

data at low

_frequencies.

At

high-er

_frequencies

(f >

5-10

_GHz),

volume

_scattering

effects within the

_vegetation

_canopy

_increase,

and

more

_complex

R.T.

_approaches

are

required

to model the canopy microwave emission

[17, 49, 56].

Note that coherent effects in the volume

_scattering

cannot be accounted for

_by

the R.T.

_equations.

These effects can be

_neglected

in the

_high

frequen-cy domain when the

wavelength

is much lower than the scatterer sizes

_(leaves,

_{branches, trunks,}

_etc.),

whereas,

at low

_frequencies,

the coherent effects

are

probably significant,

but no tractable

_approach

is

_currently

available to take them into account

_[28].

Low

_frequency

radiations are better suited for

soil moisture

_monitoring

since

_they

can more

easi-ly

go

through

the

_{vegetation layer}

to sense mois-ture. Therefore we shall focus our

_analysis

on this

frequency

range.

Modelling

of

vegetation

effects at

higher

frequencies

was

_{investigated by}

_Choudhury

et al.

_[11]

and Calvet et al.

_[5].

Using

the &tau; - &omega;

model,

global

emission from the two

_layer

medium

_(soil

and

_vegetation)

is the sum

of three terms:

_(Tb*)

the direct

_vegetation

_emission,

(Tb

2

)

the

_vegetation

emission reflected

_by

the soil and attenuated

_by

_{the canopy}

_layer

and

_(Tb

₃

₎

soil emission attenuated

_by

_{the canopy}

_(figure

_1).

These different terms can be

_expressed

as:

where

_T

_V

is the

_vegetation

_temperature;

the

_single

scattering

albedo &omega;

_{parameterizes scattering}

within the canopy

layer

and _y is the attenuation factor. This last term is a function of the

_optical

thickness &tau; of the canopy and is

given by:

Note that the

_optical

_depth

&tau; is related to the extinction coefficient Ke

_according

to &tau; = Ke. Hc

(Hc

=

_height

of

_canopy)

where

_&Gamma;

_S

denotes the soil

_{reflectivity depending}

on

soil moisture

where

_{(1 -}

_&Gamma;

_S

₎

_corresponds

to soil

_emissivity

(e

S

=

(1 -

&Gamma;

S

)),

and

_T

_S

is the soil

_temperature.

In order to _compute the

_global

_{canopy emission} several

_{simplifications}

can be made:

Soil and

_vegetation

_temperatures

are

approxi-mately equal

(T

C

=

T

S

&ap;

T

V

).

Actually,

temperature

gradient

effects within the soil and

_vegetation

lay-ers should be considered.

_However,

_neglecting

these effects is relevant for most

_applications

to soil moisture

_study.

&omega; &ap; 0

(scattering

effects _are almost

negligible

_at

1.4

_GHz).

Note that this

_implies

that extinction effects are

equal

to emission effects

_(extinction

coefficient Ke &ap; emission coefficient

Ka).

The

_optical

thickness &tau; can be related to _the veg-etation biomass.

We shall

_{briefly analyse}

this last assertion. Several works found that &tau; could be

_linearly

related

to the total

_vegetation

water content

_W

_C

_(kg/m

₂

₎

using

the so-called b

_parameter

[24]:

The b

_parameter

can _{be calibrated for each crop}

type

or for

_{large categories}

of

_vegetation

_(leaf

dom-inated,

stem _{dominated and grasses.} At 1.4 GHz a

value of 0.12 +/-0.03 was found to be

representa-tive of most

_agricultural

_{crops. More} recent works showed that b also

_depends

on the

_gravimetric

water content of

_vegetation

_{[31, 58].}

For

instance,

b for a wheat crop decreased from the value b = 0.125

for green

vegetation

down to b = 0.04 for mature

wheat

_just

before harvest

_[58].

The b

_parameter

depends

on

_temperature,

_especially

at C-band because of the

_{temperature-dependent}

water relax-ation behaviour.

Also,

it was found that b

_strongly

depends

on

_polarization

and incidence

_angle,

espe-cially

for

_{vegetation canopies}

with a dominant

ver-tical structure

_(stem

_{dominated canopy} as cereal

crops)

[50, 57, 59].

In summary and

using

the above

_{simplifications,}

the

_global

_canopy

_emissivity

can be written as:

with

A

_priori

_knowledge

_{of the canopy}

_type

_and

pos-sibly

of its water status is

_required

to estimate the

canopy-dependent

parameters

of the function

_f()

_(b

_P

and

_possibly

_&omega;

_P

_,

_C

_pol

₎

in

_equation

_(8b).

A

_priori

knowledge

of the

soil

_type

is also

_required

to esti-mate or calibrate the function

_g()

in

_equation

_(8c).

From this information and

_{using equation}

_(8),

sim-ple modelling

of the _canopy

_brightness

_Tb

_P

can be

computed

from three land surface variables of inter-est: _{soil moisture wg,}

_vegetation

biomass and canopy

temperature

T

C

(T

C

&ap;

T

S

=

T

V

).

The

emis-sivity

is sensitive to other characteristics of the environment

_(soil

surface

_{roughness, topography,}

soil _texture,

_vegetation

_structure,

_etc.)

but these

dif-ferent terms can be considered as rather stable with

time,

and thus can be estimated or calibrated from

ancillary

information

_(soil

_maps,

_high

_spatial

reso-lution data in the

_optical

_domain,

_digital

elevation

model,

etc.).

3. MICROWAVE

_TECHNIQUES

The current status of the microwave

_techniques

and future

_developments

are

_presented

in this

sec-tion. Note that in this paper we shall focus on the

use of low

frequency

measurements for two main

reasons: at

_{higher frequencies}

_(f

> 15

_{GHz) 1)}

screening

effect of

_vegetation

is

_significant

and retrieval of soil and

_{vegetation hydrological}

charac-teristics is difficult in most

_{applications;}

₂₎

correc-tions of the

_atmospheric

effects are

required

and it

strongly

limits one of the

_key

interests of

microwave remote

_sensing.

_However,

several recent works

_investigated

the microwave

_signatures

of the land surfaces at

_{high frequencies}

_{[27, 41].}

In that

frequency

range, satellite data are

_currently

in

oper-ational use for

_atmospheric

_parameter

retrieval over oceans. For

_{atmospheric applications,}

if the land

surface contribution to microwave radiances could

be determined

_{accurately enough,}

then microwave retrievals of

_atmospheric

_quantities

_(cloud

_liquid

water and

_{precipitations),}

now

_{performed only}

over oceans,

might

be extended to land areas.

3.1.

_Requirements

for the

_{configuration}

system

As discussed in the

_preceding

_sections,

the microwave measurement is sensitive to three main land surface variables: wg,

_W

_C

and

_T

_C

_(assuming

that the other factors are _parameters that can be

cal-ibrated

_{using ancillary information).}

Therefore

sev-eral measurement data

_m

_i

_,

_(i

=

1,n),

with _a

signifi-cant low level of

correlation,

are

_required

to

dis-criminate between the effects of the surface data. These data can be obtained from measurements for several

_{configuration}

_systems of the sensor in terms of

_{polarization,}

view

_angle

and

_frequency.

As for

_polarization

effects,

the microwave

signa-tures of soil and

_vegetation

_{exhibit distinct}

respons-es. Emission from bare soil is

_strongly

_polarized

(Tb

H

<

_Tb

_V

₎

when view

_angle

&thetas; exceeds

30°,

while the emission from a dense

_vegetation

cover exhibits

almost no

_polarization

_pattern

_(Tb

_H

_{&ap; Tb}

_V

_).

This

property

has been often used to monitor the

vegeta-tion

_development

_[8,

_11,

_48].

Also,

multi-frequency

measurements can be

use-ful to

_distinguish

soil contribution from _{that of}

veg-etation. At low

_frequencies

_{(f &sim;}

1.4

_GHz),

soil

con-tribution is the dominant term in

_equation

₍₈₎

for most low

_vegetation

covers

_{(&gamma; &ap;}

_1).

As

_frequency

increases,

the

_screening

effect of

_vegetation,

i.e. the

intensity

of the attenuation effect of soil contribu-tion

_by

_{the canopy}

_layer,

increases

_{(&gamma; &ap;}

_0).

Thus at 5

GHz,

for a low

_vegetation

cover, soil and

vegeta-tion contribuvegeta-tions are close in

_magnitude,

while at

10 GHz the

_vegetation

effect becomes dominant

(figure

2).

Note that as attenuation effects of soil

contribution

increase,

vegetation

emission also increases

_(extinction

coefficient Ke &ap; emission

coefficient

_Ka).

Therefore,

the

_’screening

effect’ due to

_vegetation

combines two effects:

₁₎

the increase in the attenuation of the soil contribution

and

₂₎

the increase in the emission from the vegeta-tion.

Different levels in the

_magnitude

of this

vegeta-tion

_’screening

effect’ can be obtained from

multi-frequency

measurements. Similar results can be

obtained from

_{multi-angular}

measurements, since the attenuation effects increase as view

_angle

increases

_(y

=

exp(-

_&tau; / _cos

&thetas;)).

The data obtained

from a

_{multi-configuration}

_system

are

_appropriate

to

_distinguish

soil contribution from that of

vegeta-tion.

3.2. Sensor

_systems

The most well-known

_spaceborne

sensors are

SMMR

_(1978-1987)

and SSM/I. The latter has been in

_operation

on the DMSP satellite since 1987

[23].

The

_European

sensor MIMR

(6.8, 10.65,

_18.7,

23.8, 36.5

and 89

_GHz),

with a much

_improved

res-olution and calibration is scheduled for launch in the _year 2000

_[37].

_Using

a

_parabolic

reflector of

dimension 1.6m &times;

1.4m,

the

_ground

resolution of MIMR is about 50 km at C-band

_(&sim;5

_GHz).

The conical scan _geometry of the radiometer results in a

daily

coverage of 80 % of the earth surface with an

incidence

_angle

of 50°. Other

_spaceborne

exist-ing/near

future instruments have very similar char-acteristics: AMSR

_(for

_deployment

on the

_Japanese

satellite

_ADEOS-II),

PM-1 AMSR

_(USA),

Baskhara II

_(India).

_Operational

use of these sen-sors concern

_mostly

retrieval of

_atmospheric

char-acteristics

_{(temperature,}

water _{vapour, cloud}

_liquid,

etc.),

sea-surface

_temperature

and

wind,

sea-ice and

snow cover

[36].

Since all

_frequency

bands exceed

5

_GHz,

_screening

effect of

_vegetation

is

_significant,

and

_monitoring

of soil moisture is limited to _sparse

vegetation

of arid and semi-arid areas.

At satellite

altitudes,

an antenna size of at least

10 m is

_required

to obtain resolution in the 10-20 km range from a low

_frequency

_(L-band)

instru-ment

_(the

resolution is

_inversely

_proportional

to the

wavelength

&lambda; and

_proportional

to the antenna diam-eter

_(figure

_3a)).

_Therefore,

at _{the moment, L-band} data are available

_only

from

_ground

or airborne

instruments. Such instruments have been

_developed

in several countries

_(Russia,

_USA,

_India,

_Finland,

Switzerland,

Italy,

France)

for remote

_sensing

of the land surface features. In

France,

the

_{multifrequency}

PORTOS instrument

_(1.4,

5, 10.65, 23.8,

36.5 and

90

_GHz)

has been

_intensively

used in several air-borne and field

_campaigns.

In the

USA,

the Pushbroom Microwave Radiometer

_(PBMR)

and ESTAR 1.4 GHz instruments

_[45]

have been involved in most of the

_large

scale field

experi-ments:

FIFE, Monsoon-90, Washita’92,

_Hapex

Sahel,

Southern Great Plains 1997

_Hydrology

Experiment

(SGP97).

An

_analysis

of the results obtained from these three instruments is

_given

in the next section.

Besides conventional antenna

_technology,

an

alternative consists in

_{synthesizing large}

_aperture

using

an _array of small antenna elements. This

prin-ciple

is called

_aperture

_synthesis

or interferometric

radiometry

[20, 44].

The antenna consists of

ele-mentary

radiating

elements

_(ERE)

installed on

deployable

arms

_arranged

in T or U

_{shape (figure}

3b).

These

_depoyable

antennas can be

_easily

imple-mented _{in space and} can achieve the same

resolu-tion of very

large

conventional antennas, without the associated mass. Several one-dimensional

_(one

arm)

or two-dimensional

_synthetic

_aperture

L-band radiometer have been

_developed

and tested

_(ESTAR

in the USA

_[32],

MIRAS in France

_[20]).

In

for soil moisture

_{mapping applications}

in several

large

scale

_experiments

_[45].

In _1996, two L-band _space instruments were

pro-posed

to the Earth

_System

Science

_Program,

in response to an NASA Annoucement of

Opportunity. They

were: IRIS

(Inflatable

Radiometric

_{Imaging System)}

and

_Hydrostar

(based

on the

_heritage

of

_synthetic

_aperture

ESTAR

technology).

The

_{proposals proceeded}

to

_step

2 of the _{selection process. The} two US

_projects

_clearly

show that

₁₎

new

_technology

_approaches

have now

the

_potential

of

_providing

remote sensed L-band data with

_adequate

resolution and

_{being compatible}

with satellite

_platform

_limitations;

₂₎

soil moisture is one of the

_priority

_global

environment variables

for

_{regional/global}

weather forecast models

_(at

spa-tial resolutions &sim; 20-100

_km)

and

_long-range

cli-mate simulation models

_{(at &sim;}

150-200

_km).

4. LAND SURFACE MONITORING

The models described in the first section of this paper are _very

_appropriate

tools to

_quantify

the

sen-sitivity

of the remote

_{sensing signal}

to the surface variables. Also from model

_inversion,

retrieval methods are

_developed

to

_compute

estimates of the surface variables and to _{map soil moisture. Several}

measurement

data,

acquired

from several

configu-ration

_systems

of the sensor are often

_required

in

the retrieval process. The first

part

of this section reviews retrieval

_approaches

and the most

signifi-cant results.

Estimates of surface soil moisture

_resulting

from the inversion process can be used in the fields of

meteorology

and

_hydrology

in order to control model simulations and

_improve

water _{and energy} flow

_forecasting.

Also,

assimilation

_techniques

based on

_modelling

of the water and mass transfers

in the soil

column,

can be used to estimate the water

content down to

_{depths beyond}

the

_sampling

_depth

of the L-band microwave R-S observations. These

aspects

are

_presented

and discussed in the second

part

of this section.

4.1. Retrieval studies: methods and results

Very

significant examples

of the

_capability

of microwave

_radiometry

to

_produce

soil moisture database for

_hydrologic

studies can be illustrated

_by

the works of the USDA

_{Hydrology Laboratory}

(Beltsville)

in relation with the NASA Goddard

Space Flight

Center

_(Greenbelt)

_{during large}

scale

experiments.

For

instance,

_during

four intensive field

_campaigns

of FIFE in

_May-October

1987,

the PBMR was used to _{map surface soil moisture}

_[55].

It was also used to _{map surface soil moisture}

_during

the Monsoon 90

_experiment

in southern Arizona

[45].

Similarly,

the ESTAR instrument was used to

map soil moisture over the Washita’92

study

area

(46

by

19

_km)

with 200 m resolution in central Oklahoma

_[25].

In this

_region,

land cover is

domi-nated

_{by rangeland}

and

_pasture.

_During

the inten-sive observation

_period,

a consistent

_day-to-day

decrease in soil moisture

_{corresponding}

to a

_drying

phase

could be monitored. Predictions of soil

mois-ture were

_{analysed using}

several verification

sites,

where

_ground-based

0-5 cm surface soil moisture

samples

were collected. Distinct

_spatial

structures, in relation with

_topography,

soil texture and soil

hydraulic properties

could be

_{distinguished by}

tracking

soil moisture

_{changes during drying}

_[35].

Airborne microwave

_radiometry

was also used

over semi-arid areas, in the framework of the

Hapex-Sahel

Experiment using

both the PORTOS and PBMR instruments

_[8].

_Maps

of soil moisture

were

_computed

at several dates

_during

a month

period

in the middle of the

_rainy

season. Soil

mois-ture in the

_top

5 cm was calculated

using

a

single

linear

_relationship

between the

_brightness

tempera-ture measurements from

PBMR,

averaged

within

200 m

_grid

_square

_cells,

and soil moisture.

Large

contrasts in _{soil moisture maps} can be seen in

figure 4

between DoY 238

_{(observations}

_during

a

rainy

period)

and DoY 248

(5

days

after

_rain).

The

spatial

patterns

of soil moisture can be

_{explained by}

both the

_{heterogeneity}

of the rainfall amounts, which was shown to be a

_major

characteristic of the Sahelian rain

_fields,

and

_by

soil

_hydrodynamic

for the Washita’92

_experiment,

these

_drying

pat-terns were used to characterize soil

_properties

[22].

In these different

works,

the retrieval

_algorithm

used to

_produce

the soil moisture maps is based on

the &tau;-&omega;

model,

_{using ancillary}

data for surface

tem-perature

and

_vegetation

characteristics.

Tempera-ture estimates were

usually

obtained from thermal

infrared measurements

_{acquired concurrently}

with the microwave observations.

_Vegetation

data

_(type

and water

_content)

were estimated

using

land-use

database and SPOT

_images.

A

_{single vegetation}

water content

_value,

estimated from

_ground

obser-vations,

was

_assigned

to each

_vegetation

_category.

It seems that the way to account for the

_vegetation

characteristics limits the

_{applicability}

of this

approach

for

_operational

use of radiometric data in

global study.

For

instance,

Chen et al.

_[9]

of surface soil moisture from remote

_{sensing, using}

the PBMR

_during

the FIFE

_experiment.

Results indicated that more work is needed to

_improve

understanding

of a number of

factors,

such as

veg-etation,

thatch

_layer

and surface

_slope,

which affect the accuracy of the PBMR retrieved soil moisture.

In order to better account for

_vegetation

effects,

the

_possibility

of

_{simultaneously}

_retrieving

soil moisture and

_vegetation

water content

_W

_C

from

multi-configuration

passive

measurements has been

investigated by Wigneron

et al.

_{[57, 58]}

and

_Chanzy

et al.

_[8].

_Chanzy

et al.

_[8]

estimated

_W

_C

from indices at C-band based on

_polarization

and

_angular

differences over a semi-arid area in Sahel.

Similarly,

Teng

et al.

_[48]

used the

_polarization

dif-ference from satellite data to estimate the vegeta-tion effects and

_parameterize

the linear

_relationship

between soil moisture and

_brightness

_temperature.

The

_study

area included _part of the US corn and

wheat belts. Over

_agricultural

_crops

_(wheat

and

soybean) Wigneron

et al.

_{[57, 58]}

showed that

multi-angular

data at 1.4 GHz or

_bi-frequency

data

(at

1.4 and 5

_GHz)

can be used to retrieve

simulta-neously

soil and

_vegetation

characteristics

_(figure

5).

The

_principle

of these

_approaches

is to use

mea-surements

_{corresponding}

to two _{values of the} vege-tation attenuation

factor,

which

_{parameterizes}

the canopy

’screening

effect’. Note that similar results in discrimination can

_probably

also be obtained

from combined

_{passive/active}

data. The

_possibility

of

_making

simultaneous estimates of the soil and

vegetation

characteristics,

as demonstrated in these

works,

reinforces the _strong

_potential

interest for an

operational

use of the microwave radiometric data.

Though optimum frequency

for soil moisture

study

is 1.4

GHz,

interesting

results have been obtained at

_{higher frequencies}

from

_spaceborne

sensors.

Thus,

it is

_important

to mention the

_study

of

_Teng

et al.

_[48]

from SSM/I

data,

and those of

Choudhury

and Golus

_[10],

Owe et al.

_[40],

Ahmed

[1]

from

SMMR data. For

instance,

Ahmed

_[1]

obtained six levels of soil moisture in the mid-west and southern United States from 6.6 GHz SMMR observations.

As for surface

_temperature,

simultaneous retrievals of soil moisture and surface

_temperature

have been

_{attempted by}

Calvet et al.

_[5]

over _sparse

vegetation

covers. It seems that retrievals of surface

temperature should be limited to _{rather dense}

vege-tation covers, forests in

particular,

where the

microwave

_emissivity

remains

_relatively

constant with time

_[3,

21, 34,

60].

From satellite

data,

it appears that

temperature

retrieval

_requires

accurate

estimates of the fractional area of open water,

with-in each

_footprint.

In the Swiss Central

_Plains,

typi-cal RMS errors between measured and estimated

surface

_temperatures

are between 2 and 4 K from

SSM/I data

_[21].

_Conversely,

in nonforested

regions

surface emittance data show the

_potential

for

_{flood-monotoring}

_purposes

_[27].

4.2. Use of

_temporal

soil moisture information

Soil moisture is an

_important

variable in many

hydrologic, agricultural

and

_{meteorological}

appli-cations. As

_presented

in the

_{Introduction,}

the distri-bution of soil water within the 0-10 mm or

_(0-50

mm)

layer

has a

_strong

influence on _soil

evapora-tion.

_Taking

into account the

_evaporative

_demand,

reasonable estimates of cumulative

_evaporation

from soil can be obtained from

_simple

models based on a surface resistance

_r

_S

_.

The functional

relationships

between

_r

_S

and near-surface soil water

content _{has been studied in many works}

_{[7, 13, 33].}

For

instance,

working

with PBMR data from Monsoon

90,

Kustas et al.

_[30]

showed that the

evaporative

fraction,

i.e. the fraction of available energy which goes into

evaporation,

was

_strongly

correlated, r

2

=

0.7,

with the observed microwave

brightness

temperatures.

While we would

_expect

this to be the case for the

_{sparsely vegetated}

Walnut

Gulch

watershed,

the

_remotely

sensed soil moisture

values also appear to be

_important

in the more

humid

_grassland

site of FIFE. In an

analysis

of the

FIFE-89 data for the surface area

_averaged

_fluxes,

Sellers et al.

_[47]

found that the

_remotely

sensed surface soil moisture from the PBMR _{is very}

impor-tant for

_{parameterizing}

the soil resistance to evapo-ration and for

_estimating

the soil

_profile

moisture

content.

_Using

the

_spatial

variation of soil moisture from the PBMR in their

_{Simple Biosphere}

model

(SiB)

they

found that it accounted for about a third

of the variance observed in the latent heat flux.

The soil moisture content is also

_important

for

predicting

runoff

_following

a rain event. In Monsoon 90 the PBMR estimates of 0-5 cm soil

moisture were used for the definition of the

prestorm

initial soil moisture conditions for an

event based rainfall-runoff model

_[19].

The

find-ings

of this

_study

indicated that the

_remotely

sensed

measures of soil moisture were as

good

as several

ground

based methods in a small watershed for

runoff

_{predictions. Similarly,}

_cartography

of the

spatial

variations of soil moisture and

evapotranspi-ration at the river basin

scale,

were used to initial-ize

_hydrological

models and

_improve

outflow

fore-casting

[12, 62].

Thus,

it should be

_possible

to use

the

_remotely

sensed measures in areas without

extensive

_ground

measurements to

_improve

_the pre-diction of runoff.

In the field of

_{meteorology, potential}

interest of

repetitive

estimates of soil

_moisture,

on a

_1,

5

_day

schemes in mesoscale

_atmospheric

models,

has been

_{analysed by}

Noilhan and Calvet

_[38].

Several works

_investigated

the

_ability

to assess the

hydro-logical

conditions at

_depth

within soil

_(in

the

_top

1

or 2

m)

from

repetitive

estimates of the water

con-tent at the soil surface. In

_particular,

the water

con-tent in the root zone w2 is an

_important

_parameter,

which associated with the

_depth

of the root _zone,

can be used to assess the water status of

_vegetation

and water

_{availablility}

for

_{plant transpiration}

processes.

Promising

results of estimates of soil characteristics at

_depth

have been obtained from assimilation

_techniques

based on

_modelling

of the

water and mass transfers in the soil column.

For

instance,

the information content of the time variations of surface soil moisture

_during

a 5-30

day period

has been

_analysed

in several works

_[6,

16,

42].

A

_simple

water

_budget

_{for wg} can be

derived from the force-restore method

_applied

_by

[14]

to the

_ground

soil moisture:

The first term on the

_right

of

_equation

₍₉₎

repre-sents the influence of surface

_atmospheric

fluxes

(outputs

due to

_evaporation

_E

_S

and

_inputs

due to

precipitation

P).

The second term in

_equation

₍₉₎

characterizes the

_diffusivity

of water in the soil which tends to restore wg toward the bulk value

(w2).

The dimensionless coefficients

_C

₁

and

_C

₂

_are

highly dependent

upon soil moisture

(wg

and

_w2)

and soil texture.

_They

can be calibrated from

mea-surements or estimated

using parameterizations

derived from numerical

_experiments

_[39].

Equation

(9)

relates the time variations of surface soil moisture wg to the

_parameters

of

interest,

_E

_S

_,

P and w2. If correct estimations of P can be obtained

from the

_{ground meteorological}

network,

periodic

observations of wg will

provide

information on the root zone water content w2. On this

basis,

retrieval of the root zone soil moisture has been carried out

by

Calvet et al.

_[6]

_during

the Murex

_experiment.

The

_study

is based on a continuous series of

micrometeorological

data measured in

south-west-ern France on a fallow site in 1995-1996. A

mete-orological

surface scheme

_(ISBA)

_including

a

force-restore

_modelling

of water and heat transfers in

_soil,

was used to retrieve variables at

_depth

from

the

_ground

measurements _{of wg. It} was found that one measurement _{of wg every 3-4}

_{days during}

a

minimum 15

_{day period,}

was sufficient to obtain

In

_parallel,

the

_feasibility

of

_{using brightness}

temperature

measurements

_(microwave

and infrared

_channels)

to solve the inverse

_problem

associated with soil moisture and heat

_profile

was

demonstrated

_by

Entekhabi et al.

_[16].

This theoret-ical

_analysis

was based on a Kalman filter

_using

radiative transfer and

_coupled

moisture and heat diffusion

_equations.

Both assimilation

_approaches

from Calvet et al.

_[65]

and Entekhabi et al.

_{[ 16]}

showed that

_periodic

observations of the surface

characteristics,

such as those

_provided

_by

microwave remote

_sensing

_systems,

can be used to

retrieve information on the lower

_layers.

Additional

work in the future is necessary to test the conditions for an

_operational

use of such assimilation

tech-niques

in

_atmospheric

and

_hydrological

models at a

regional

scale.

However,

these works open

promis-ing

new fields of

_{investigation}

and

_strongly

empha-size the

_{large potential}

interest of

_repetitive

remote-ly

sensed microwave

_signatures,

on

_{a 1,}

3

_day

basis.

5. CONCLUSION

The

_previous

sections have discussed the basis of

passive

microwave remote

_sensing

for soil mois-ture, and indicated the

key

role of this surface vari-able in the water

_exchanges

between the surface and the

_atmosphere.

In the last

_section,

recent

assimilation

_techniques

of

_periodic

surface data are

presented

and

_proved

to _{be very}

_promising

for

deriving

important

geophysical

parameters,

such as

the root zone soil moisture.

As

_promising

as

_passive

microwave remote

sens-ing

for soil moisture appears to

_be,

the future for

using

this

_technique

is still uncertain.

_Existing

and

near future instruments do not have

_frequencies

low

enough

to be useful for soil moisture

retrieval,

except

over arid or semi-arid areas where the

screening

effect of

_vegetation

is low.

_Currently,

there is no

_{planned passive}

_spaceborne

_system

including

a low

_frequency

channel

_(L-band).

Furthermore,

it seems that

_{multispectral}

_(L-band

and C-band for

_instance)

or multi-incidence data are

_required

to discriminate between the effects of

soil moisture and

_vegetation

biomass.

_Large

interest

in near future instruments

_(e.g.

MIMR and

AMSR)

over the land surface should concern

_mostly

surface

temperature

retrieval.

However,

using

aperture

synthesis,

current

tech-nology

has now the

_potential

to

_{develop spaceborne}

L-band sensors with a

spatial

resolution on the

order of 10 km.

_Therefore,

_providing

much needed data for the

_{computational}

cells of

_{meteorological}

and climate models on the order of 10-200 km is

well within the

_capacity

of such future

_passive

sys-tems.

Also,

during

the last

_decade,

there has been

increasing

evidence of a

_{large atmospheric}

sensitiv-ity

to _{land surface processes. Short-term forecasts}

are

_{highly dependent}

on initial surface conditions

and the soil moisture status is a

_key

information to

predict

the near-surface

_atmospheric

fields. In

sum-mary,

passive

microwave

radiometry

appears to be

a

_promising

tool that addresses the fundamental

needs in

_atmospheric

and

_hydrological

models for land surface characterization at a

_{regional/global}

scale on a

_daily

basis.

REFERENCES

[1] Ahmed N.U.,

Estimating

soil moisture from 6.6

GHz dual

_{polarization,}

and/or satellite derived vegeta-tion index, Int. J. Remote Sens. 16

₍₁₉₉₅₎

687-708.

[2] Brisson N., Perrier A., A

_{semiempirical}

model of bare soil

_evaporation

_{for crop simulation} models, Water Resour. Res. 27 ₍₁₉₉₁₎ 719-727.

[3] Calvet J.-C.,

Wigneron

J.-P.,

Mougin

E., Kerr

Y.H., Brito _J.L., Plant water content and _temperature of the Amazon forest from satellite microwave

_radiometry,

IEEE Trans. Geosci. Remote Sens. 32 ₍₁₉₉₄₎ 397-408.

[4] Calvet _J.-C.,

_Wigneron

_J.-P.,

_Chanzy

_A.,

_Raju

_S.,

Laguerre

L., Microwave dielectric

_properties

of a

silt-loam at

_high

_frequencies,

IEEE Trans. Geosci.

Remote Sens. 33 ₍₁₉₉₅₎ 634-642.

[5] Calvet J.-C.,

Wigneron

J.-P.,

Chanzy

A.,

Haboudane D., Retrieval of surface _parameters from microwave

_radiometry

over _open

_canopies

at

_high

fre-quencies,

Remote Sens. Environ. 53 ₍₁₉₉₅₎ 46-60.

[6] Calvet _J.-C., Noilhan J., Bessemoulin _P.,

Retrieving

the root-zone soil moisture from surface soil moisture or _temperature estimates: a

_{feasibility study}