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Advances in Space Research 43 (2009) 1111–1117

Soil moisture mapping over the Chinese Loess Plateauusing ENVISAT/ASAR data

Tangtang Zhang a, Jun Wen a,*, Zhongbo Su b, Rogier van der Velde b,Joris Timmermans b, Rong Liu a, Yuanyong Liu a, Zhenchao Li a

a Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000,

Gansu, PR Chinab International Institute for Geo-Information Science and Earth Observation (ITC), Hengelsostraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands

Received 15 June 2008; received in revised form 28 October 2008; accepted 29 October 2008

Abstract

In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface processExperiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low inci-dence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during theLOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from themicrowave remote sensing and ground measurements is less than 0.02 cm3/cm3, with a RMS error of 2.0%, and a maximum less than0.04 cm3/cm3. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitablefor monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situationand the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep sloperegion, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soilmoisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.� 2009 Published by Elsevier Ltd on behalf of COSPAR.

Keywords: Soil moisture; Vegetation correction; ENVISAT–ASAR; Mesa of the Loess Plateau; LOPEX05

1. Introduction

Water in the soil takes an essential part in land–atmo-sphere–water cycle. This water is one of the most importantsubstances that affect agriculture yield and water resourcemanagement. It is also an interface of the earth surfaceand atmosphere that controls the interactions amonghydrosphere, biosphere and atmosphere systems. There-fore, many disciplines and scientists focus their studies orresearch topics on the estimation of soil moisture and itsvariation at various temporal and spatial scales (Baupet al., 2007; Bindlish and Barros, 2000, 2002; Calvetet al., 1996; Cosh et al., 2005; D’Urso and Minacapilli,

0273-1177/$36.00 � 2009 Published by Elsevier Ltd on behalf of COSPAR.

doi:10.1016/j.asr.2008.10.030

* Corresponding author. Tel./fax: +86 931 4967109.E-mail address: jwen@lzb.ac.cn (J. Wen).

2006; Famiglietti et al., 1999; Ghulam et al., 2007; Graysonand Western, 1998; Holah et al., 2005; Shi et al., 1997; Wenand Su, 2003a; Zribi et al., 2005).

Soil moisture is a key parameter in the application fieldof hydrology, meteorology, and agriculture. Soil moisturedetection at a large scale is essential for agriculture man-agement in particular for estimation of the arid crop yield.Furthermore, soil moisture information is an indispensableparameter in land process modelling at different scaleseffecting regional and global climate predictions. Regionalor large scale soil moisture ground measurements are timeand labor intensive. Using remote sensing to estimate thesoil moisture provides a fast way to map the soil moisturevalues and spatial variation. Therefore satellite microwaveremote sensing techniques on soil moisture retrieval andmapping are paid much more attention in recent years.

Table 1List of ASAR overpass data collected dining LOPEX05.

Date Mode Polarizations Incidence (�) Swath (km)

2005/07/22 IS2 HH/VV 19.2–26.7 1052005/7/28 IS5 HH/VV 35.8–39.4 652005/07/31 IS7 HH/VV 42.5–45.2 562005/03/07 IS1 HH/VV 15.0–22.9 1052005/03/11 IS4 HH/VV 31.0–36.3 882005/08/14 IS2 HH/VV 19.2–26.7 105

1112 T. Zhang et al. / Advances in Space Research 43 (2009) 1111–1117

The radar signal at C-band is sensitive to the surface soilmoisture (Paloscia et al., 2005). This C-band can be foundon satellites like ERS-2 (European Remote Sensing Satel-lite), RADARSAT and ENVISAT (Environment Satellite).The C-band measurements are not only effected by soilmoisture but they are also significantly affected by the veg-etation cover and ground surface roughness. Many statisti-cal or empirical methods have been proposed to retrievethe soil surface moisture using the ENVISAT/ASAR dataon vegetation or bare soil fields (Alexander et al., 2006;Baghdadi et al., 2006; Chen et al., 2007; Holah et al.,2005; Li, 2004). Because the terrain of Chinese Loess Pla-teau is very complex, we have integrated the available algo-rithms (Hallikainen et al., 1985; Chauhan et al., 1991; Wenand Su, 2003a,b) to estimate the surface layer soil moistureover the farm fields.

The ENVISAT/ASAR data is more sensitive to the sur-face roughness at a high incidence angle than that at alower incidence angle. The HH polarization signal is moresensitive to roughness than the VV signal. Furthermore,with the increasing incidence angle and surface roughness,the VV polarization data becomes very insensitive to soilmoisture.

A problem arises therefore to use the ENVISAT/ASARdata to estimate soil moisture over Chinese Loess Plateau,where roughness effect is a big issue. To circumvent thisproblem we selected the area of the Loess Plateau wherethe elevation is relatively flat as the main study area. Thisare is nearly 100 km2 and suitable for satellite remote sens-ing of surface soil moisture.

In this study, in order to keep the roughness effects onthe ENVISAT/ASAR signal as small as possible, that isthe ASAR data signal is mainly influenced by the soil sur-face dielectric constant. We used the available algorithm ofsoil moisture retrieval over agricultural fields which werebased at a low incidence angle of ENVISAT/ASAR APVV data. Concurrently ground measurements of soil mois-ture were collected during the Loess Plateau land surfaceprocess Experiment 2005 (LOPEX05). The comparisonbetween estimation and ground truth shows that the algo-rithm is suitable for the retrieval of soil moisture. The algo-rithm is applied to soil moisture mapping over the ChineseLoess Plateau. Further discussions and remarks are alsopresented in the last section.

2. Data and study area

2.1. ENVISAT/ASAR data

The European Environment satellite which waslaunched on March 1st, 2002, carries an active microwaveinstrument ASAR (Advanced Synthetic Aperture Radar).This instrument operates at a wavelength 5.6 cm (frequency5.3 GHz), and in different modes, namely as image mode,polarization mode, wide swath mode, global monitoringmode and wave mode. It can also provide multi-angle(the angle is from 15.0 to 45.2 corresponding to IS1 mode

to IS7 mode) and multi-polarization data to meet differentapplications on land and ocean. AP mode (Alternativepolarization mode) can provide HH/VV, HH/HV, andVV/HH data with ground resolution 30 m. The level 1Bproducts of ENVISAT/ASAR AP mode (ASAR Alternat-ing Polarization Ellipsoid Geocoded Image) were used inthis study (ASAR product hand book, 2002).

Six ENVISAT/ASAR overpass data (Table 1) wereacquired during the LOPEX05 field campaign which willbe introduced in the following section, but there were noground measurements on 22 July 2005, and it was a rainyday on 7 August 2005. So only one look (08/14) was avail-able for soil moisture retrieval and mapping, while otherthree acquisitions were taken as accessorial justificationsdue to their high incidence angles, the radar signal was sen-sitive to ground roughness at such incidence angles.

2.2. Study area and ground measurements

The Chinese Loess Plateau is located at about 80–120�Eand 30–45�N with average altitude about 1000 m above sealevel. The landscape of total plateau is much complicatedwith many of ridges, Loess hills, ditches and ravines. Mixedneedle and broadleaf forest distributes in the part of theLoess Plateau. The precipitation is concentrated amongJuly to September due to the effect of the East Asiamonsoon.

The experimental area is located in the east of Gansuprovince in China (Fig. 1). Here, the total area is nearly100 km2 and the average precipitation is about 500 mm.The experimental area belongs to the semi-arid and semi-humid regions where the wheat and corn are the mainplants, and represents typical regions of the Chinese LoessPlateau land surface types. The circles in Fig. 1 show thesample sites of the ground measurements locations. Atthese locations, measurements were performed of surfacesoil moisture, plants volume vegetation water content, leafarea index and plant biomass above the ground.

During the experimental period from July 20 to August26 in 2005, three eddy correlation systems (ECS) were setup, two at two different bare fields and the last one wasset up in millet field. At the same site as the ECS 4 setsof time domain reflectivity (TDR) probes were buried atthe depth 2.5, 5, 10 and 40 cm, respectively. An impedanceprobe (Hydra data reader) was used to measure the surfacesoil moisture in the 0–6 cm layer, soil salinity, soil temper-

Fig. 1. Geographic location (left), ENVISAT/ASAR image (right) and sample sites (circle) of the LOPEX05 study area.

T. Zhang et al. / Advances in Space Research 43 (2009) 1111–1117 1113

ature and electric conductivity at the selected sample sitesaround the satellite pass time (14-AUG-2005.03:04UTC,the time is 13-AGU-2005.07:04 Beijing time). Three ormore samples were taken in each point according to theactual land surface conditions. The values of measured soilmoisture ranged from 0.09 to 0.36 and an average value ofsoil moisture is taken to be use at each test point. Concur-rently, the gravimetric method was used to validate theTDR measurements (in detail see Table 2). The soil sam-ples were collected at the selected sample sites for analyzingthe soil textural components in the laboratory. The geo-physical locations of all the sample sites were recorded inthe Global Positioning System (GPS).

The experimental land surface mainly consists of manylong and narrow patch corn, bare soil surface, millet andalfalfa fields. Some of the fields are less than 30 m in width,though the length is far away than 2 or 3 times of width.Considering the deviation of the signal the satellite receivedfrom the land surface, in the fields, an average values of a5 � 5 pixels rectangle, surrounding the determined Univer-sal Transfer Mercator (UTM) coordinates, are comparedwith the ground measurements.

2.3. Data process

The ENVISAT/ASAR data were processed using a fourstep methodology. First, the geometric correction was con-ducted using the ground control point data. This data wascollected by the GPS during the experimental period. Sec-ond, the K (external calibration scaling factor), ad (inci-dence angle) and the far and near incidence angle

Table 2Ground measurements and more detailed information.

Sites Ground truth With veg. Without veg.

1 0.27 0.28 0.352 0.33 0.29 0.363 0.27 0.26 0.294 0.12 0.11 0.145 0.19 0.17 0.216 0.13 0.14 0.157 0.20 0.18 0.218 0.19 0.17 0.209 0.27 0.26 0.29

information were extracted using the BEST4.0.3 software.Third, each pixel’s incidence angle was calculated. Fourth,the DN (Digital Number) values were transferred to thebackscattering coefficients using Eq. (1).

The radar backscattering coefficients can be extractedfrom the ASAR data. The relationship relating DN tothe backscattering coefficient is given below (EuropeanSpace Agency):

r0 ¼ hA2i

KsinðadÞ ð1Þ

where r0 is the target backscatter coefficients (m2/m2); A isthe DN value, ad is the target incidence angle, K is externalcalibration scaling factor.

3. Theory and methodology

3.1. Inversion model for bare soil

In bare soil surface, the radar signal is mainly affected bythe surface roughness and soil water content besides theradar configuration (incidence angle, frequency and polar-ization). In the given configuration situations, the ASARsignal was mainly affected by the surface soil water contentand ground roughness, so the backscatter coefficients canbe described as follows:

r0 � A � r0soil þ B � roughnessþ C ð2Þ

where the r0 is the target total backscatter coefficients; andr0

soil is the surface soil contribution, and the A, B and C are

Instru. LAI VWC (kg/m2) Land use

TDR 4.52 9.01 CornTDR – – Bare soilTDR 4.38 6.91 CornTDR – – Bare soilTDR – – Bare soilTDR – – Bare soilCS616 – – Bare soilCS616 – – Bare soilCS616 3.65 2.12 Millet

Fig. 2. Flow chart of the algorithm used in soil moisture estimation.

1114 T. Zhang et al. / Advances in Space Research 43 (2009) 1111–1117

experiential coefficients depend on the wave length andincidence angle.

The ENVISAT/ASAR VV data backscattering coeffi-cients are much less sensitive to the roughness comparedto the HH data, which are almost constant when the inci-dence angle is changed (Holah et al., 2005; Li, 2004), thusthe Eq. (2) can be described as follows:

r0 � r0soil ð3Þ

The soil backscattering coefficients can be described bythe soil Fresnel reflectivity which is also described as Eq.(4) (Chauhan et al., 1991; Wen and Su, 2003b; Woodhouseand Hoekman, 2000):

r0soilðhÞ ¼

jCð0Þj2 expð� tan2 h2s2 Þ

2s2 cos4 hð4Þ

where r0soilðhÞ is target backscattering coefficient; C(0) is soil

Fresnel reflectivity at normal incidence of a half-space witha relative permittivity, it is an indicator of soil wetness thatcan be evaluated from the soil dielectric constant. s

(s ¼ffiffiffi2p

r=l) is RMS (root mean square) slope of surfaceheight, and the average value is 0.25 for a few measure-ments, r is standard deviation of the surface and l is hori-zontal distance between two different points on the surface,a Gaussian correlation function has been assumed. h is thetarget incidence angle, for C is also described as follows(Jackson et al., 2002; Ulaby et al., 1981):

Cðh; HÞ ¼ u2 cos h�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2e2 � sin2 h

p

u2 cos hþffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2e2 � sin2 h

p

�����

�����

2

ð5Þ

Cðh; V Þ ¼ e2 cos h�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2e2 � sin2 h

p

e2 cos hþffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2e2 � sin2 h

p

�����

�����

2

ð6Þ

where H and V are the polarizations, e2 is relative complexdielectric constant of the soil, expressed as a ratio of thecomplex dielectric constants of the lower (soil) and upper(air) medium; u2 is relative magnetic permeability of thesoil; h is the target incidence angle; and the �2 can be de-scribed as follows (Hallikainen et al., 1985):

e ¼ ða0 þ a1S þ a2CÞ þ ðb0 þ b1S þ b2CÞmv þ ðc0

þ c1S þ c2CÞm2m ð7Þ

where S and C are percent of the soil texture, the value is18% and 4%, respectively. mv is the volume surface soilwater content, and a, b, c are experiential coefficients(Hallikainen et al., 1985).

3.2. Inversion model for vegetation cover

In the vegetated areas, we assume that bare soil has thesame properties as have same property characterized bysurface roughness and soil moisture. Therefore the radia-tive transfer process can be described as follows (Wenand Su, 2003b):

r0ðhÞ ¼ ½ð1� FCÞð1� T 2ðhÞÞ�r0soil þ FCðr0

int þ r0vegÞ ð8Þ

where r0(h) is observed backscattering coefficient, FC isvegetation fractional coverage, T2(h) is canopy transmit-tance in two ways of incoming and outgoing path, r0

soil iscontribution of soil, rveg is vegetation volumetric contribu-tion and r0

int is the contribution from land surface–vegeta-tion interaction.

The reflection between interfaces of canopy layer andsoil surface or the double-bounced radiance contributioncannot be neglected, if the vegetation layer and the baresoil have a distinct boundary layer or if vegetation scattersare not continuously distributed, (Woodhouse and Hoek-man, 2000). However in this paper, the double-bouncedradiance contribution is neglected by considering the realvegetation fraction cover (Wen and Su, 2003b). ThereforeEq. (8) is also described as (Frison and Mougin, 1997):

r0 � r0veg þ s2r0

soil ð9Þ

where the s2 = exp(�2Bmvsech), mv is the vegetation watercontent, B is the empirical parameter. The r0

veg is also de-scribed as (10); the single scattering is a function of scatter-ing and absorption coefficient, which is affected by thescatter size and vegetation water content. If vegetationscatterers have the same size distribution, the single scatter-ing albedo will be a function of the vegetation water con-tent (Wen and Su, 2003b), as is illustrated by Eq. (10).

r0vegðhÞ ¼ Ks cos h

2Ke½1� expð�2Ke sec hÞ�

¼ 12xv cos h½1� T2ðhÞ�

ð10Þ

where Ks and Ke are scattering and extinction coefficient ofvegetation discrete elements. xv is the single scattering albe-do of the vegetation scatters. T2(h) is canopy transmittancein two ways of incoming and outgoing path. The T2(h) andxv are 0.16 and 0.085, respectively (van de Griend et al.,1998; Bindlish and Barros, 2001; Frison and Mougin,1997). The contributions of the vegetation canopy areparameterized by canopy fractional coverage at the givencanopy transmittance 0.16 and scattering albedo 0.085 inthe proposed soil moisture retrieval algorithm. This algo-rithm of C-Band ERS-1 wind scatterometer is also ableto be used for the C-band ENVISAT ASAR data, onlythe resolution of the output is different.

To better understand the computing processes of theproposed algorithm for the land surface soil moisture map-ping, a flow chart is given in Fig. 2.

T. Zhang et al. / Advances in Space Research 43 (2009) 1111–1117 1115

4. Case studies and validation

4.1. Soil moisture retrieval without vegetation cover

Suppose that the contributions of vegetation backscat-tering coefficients can be neglected, Eqs. (1), (4), (6), and(7) can then be applied to retrieve regional soil moistureover the experimental sites. Comparing the surface soilwater content retrieved by the algorithm with surface soilwater content of ground measurements we found estimatedvalues deviate very much from measured values (Fig. 3,left). The algorithm therefore is not successful without con-tribution of vegetation in this region. Fig. 3 also indicatesthat the maximum error between the estimated soil mois-ture and ground measurements is 0.08 (cm3/cm3), the aver-age error is 0.03 (cm3/cm3), and the RMS is 3.1%.

Fig. 4. Regional soil moisture distributions derived from ENVISAT/ASAR over the LOPEX05 study area (14 Aug 2005).

4.2. Soil moisture retrieval with vegetation cover

With consideration of the vegetation effects and the soilbeneath the vegetation, Eqs. (1), (4), (6), (7), and (10) areapplied to the imagery to retrieve the surface soil watercontent over the experimental area. The deviations betweenthe retrievals from the satellite imagery and ground mea-surements are shown in Fig. 3 (right). The results indicatethat the estimated soil moisture agree with the ground mea-surements when the ground measurements are less than0.20 cm3/cm3 independent of correcting for the vegetationeffects. However, the effect of vegetation in the retrievalsis observed when the ground measurements are around0.30 cm3/cm3. The ground measurements show the regionof soil moisture around 0.30 cm3/cm3 are farm cropsdomain. This vegetation layer scatter could cause largeerrors. Fig. 3 (right) also indicates that the average andRMS errors are somewhat lower 0.02 cm3/cm3 and 2.0%,respectively.

We used the above available algorithm to map the sur-face soil moisture with the low angle VV data of ENVI-SAT/ASAR over the study area (Fig. 4). In Fig. 4, thesoil moisture higher than 0.42 is indicated with the blackcolor and the soil moisture less than 0.03 is indicated with

Without vegetation

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

Ground measurements (cm3/cm3)

Est

imat

ed s

oil m

oist

ure

(cm

3/c

m3)

Fig. 3. Comparison of ENVISAT/ASAR estimated soil

the red color. The region where soil moisture is higher than0.42 cm3/cm3 or less than 0.03 cm3/cm3 is the steep inho-mogeneous slope of hill or the urban region. Due to radarforeshortening and layover effects, high soil moisture isestimated at slopes which face the radar direction. Theslopes which face away from radar look direction showlowest soil moisture. The steep inhomogeneous slope ofthe hill is covered with terrace or rather complicated ter-rain. We think the soil moisture estimated is invalid inthese regions. The proposed available algorithm has failedin the steep inhomogeneous slope of hill region and the soilmoisture over city region is not need in fact.

Fig. 4 indicates that the surface moisture map can reflectthe real surface situation except for the steep slope and theurban regions. The soil moisture range from 0.09 to0.36 cm3/cm3 is shown in different colors and agrees withmeasured soil moisture values.

To validate the soil moisture estimated from the ENVI-SAT/ASAR data, the other two ENVISAT/ASAR over-pass data available, on 22 July and 7 August, are alsoused to estimate the soil moisture though there are noground surface soil moisture measurements. The other

With vegetation

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

Ground measurements (cm3/cm3)

Est

imat

ed s

oil m

oist

ure

(cm

3/c

m3)

moisture with ground measurements (14 Aug 2005).

1116 T. Zhang et al. / Advances in Space Research 43 (2009) 1111–1117

three high incidence angle acquisitions data were also usedto further validate our algorithm for the retrieval of soilmoisture. The high incidence angles are normalized asIS2 incidence angle (Baghdadi et al., 2001).The comparisonbetween estimated soil moisture and local precipitation ispresented in Fig. 5. The comparison shows that the esti-mated higher soil moisture is observed corresponding tothe rainfall events on that day or around that day. Thoughthere were a few rainfall events occurred 7 days before on28 July, the soil moisture was still not high due to a consec-utive sunny days after that day.

5. Discussion and conclusion

The soil textural components of the experimental areaare nearly the same as the pixel resolution in our remotesensing study. So, we think the available algorithm is reli-able and suitable to surface layer soil moisture retrieval.The proposed algorithm in this study can be applied tomonitor drought or retrieve surface soil moisture over theChinese Loess plateau.

The algorithm is reasonably diminished the groundroughness effects by using the ENVISAT/ASAR AP modeVV polarization observation at a low incidence angle data,and take the vegetation layer into account to estimate thesurface soil moisture successfully. Later, the algorithmwas used to map the soil moisture over the Chinese LoessPlateau study area. The mapping results provide a simpleand available tool to monitor regional soil moisture spatialvariation over the study area.

The presented results have shown the proposed algo-rithm is suitable and available for soil moisture retrievalover the study area. More importantly, this is the first timefor the ENVISAT/ASAR data applied to the complicatedsmall agricultural fields in the Chinese Loess Plateau. Inmost soil moisture investigations, errors and variabilityare unavoidable. However, the results are acceptable con-sidering the specific application. In the mixed small agricul-tural fields, reducing the roughness effects of the radarsignal, a method was proposed to map the surface soilmoisture over the Chinese Loess Plateau, which allows thatthe RMS error is 2.0% and the average errors are lowerthan 0.02 cm3/cm3 over the regions. We point out that

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25-J

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31-J

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m)

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0.05

0.10

0.15

0.20

0.25

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oil m

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Precipitation Estimated soil moisture

Fig. 5. Comparison between the ENVISAT/ASAR estimated soil mois-ture and local precipitation.

the errors over the steep inhomogeneous slope of hill arenot accepted. So, if the available DEM data are input,we think that the results could be better. More detailedand further work will be completed in future. Meanwhile,we hope more scenes ASAR data will be processed to jus-tify the proposed algorithm in the future.

Acknowledgments

This work was supported by the Centurial Programsponsored by the Chinese Academy of Sciences (2004406)and the Field Station Foundation of the Chinese Academyof Sciences.

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