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Apr 2, 2014 - The knowledge of its special and temporal variation has importance with ... For example, surface temperature property is used in thermal infrared band of .... tension. But tensiometers have been found to be unsuitable for measurements in dry ..... Jaeger, E.B., Lehner, I., Orlowsky, B. And Teuling, A.J.,. 2010.
 

Journal of Indian Water Resources Society, Vol 34, No.2, April, 2014

ASSESSMENT OF SURFACE SOIL MOISTURE THROUGH CLASSICAL METHOD AND OPTICAL, THERMAL REMOTE SENSING TECHNIQUES G. Shukla1 and P. K. Garg2 ABSTRACT Surface soil moisture is one of the crucial variables in hydrological and atmospheric processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous hydrological and environmental studies. Advance in technology shown that soil moisture can be measured by a variety of ground-based and remote sensing techniques, each with its own strengths and weaknesses. This work presents a comprehensive review of the progress of surface soil moisture retrieval approaches. Approaches for surface soil moisture estimation from ground-based point measurements to space-based optical, thermal, measurements are presented in this review study. In ground-based approaches, the physical principles and comparative study on three basic scales; cost, accuracy and response time, have been discussed. Measured parameters, limitations and drawbacks of different techniques have also been summarized. Spacebased approaches review study has been divided in two classes depending on data types; optical, thermal. Limitations existing in current soil moisture estimation methods have been also discussed. This review focused only on advanced optical and thermal remote sensing methods for soil moisture retrieval. Keywords: Surface soil moisture, Techniques of measurement. 

INTRODUCTION Surface soil moisture is the water present in the upper 10 cm of soil, whereas root zone soil moisture is the water that is available to plants, which is generally considered to be in the upper 200 cm of soil. Although only a tiny proportion of total water stored in the soil, approximatelly 0.0012% (Vander and Casey, 2010), surface soil moisture is one of the crucial variables in hydrological process, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface (Wangle et al., 2009). The knowledge of its special and temporal variation has importance with respect to study the different processes on Earth surface. Therefore, the estimation of soil moisture by means of remotely sensed observations is very attractive to domains, like hydrology, agronomy or meteorology. However, the large heterogeneity in the spatial and temporal distribution of soil moisture at the landscape level and the lack of standard methods to estimate this property limit its quantification and use in extensive research (Mittelbach et al., 2012). Tradition approaches are basically ground-based techniques for soil moisture retrieval. Different methods ranging from gravimetric method to neutron probe technology, time domain reflectometry (TDR), frequency domain reflectometry (FDR), tensiometers and more accurate and latest ground penetrating radar method (GPR), gamma ray attenuation. Ground-based techniques however provide more precise data. The instruments can be accurately calibrated to give depth wise measurements of soil moisture. Only point measurements are taken by ground-based equipment, and therefore this makes spatial interpretation a difficult task. 1.

Research scholar, Geomatics Engineering Group, Department of Civil Engineering, Indian Institute of Technology, Roorkee, INDIA. Email:[email protected]

2.

Professor, Geomatics Engineering Group, Department of Civil Engineering, Indian Institute of Technology, Roorkee, PIN 247667, INDIA.

Different studies on soil moisture have been carried out with advancement in remote sensing techniques. Remote sensing measurements of the soil record the amount of radiation in a given wavelength reflected off of or emitted from the surface to the sensor. The basic difference among different approaches are the wavelength bands of electro-magnetic spectrum used, source of electro-magnetic energy, response measured by the sensor and model to relate signal response to soil moisture content. For example, surface temperature property is used in thermal infrared band of electro-magnetic spectrum to retrieve surface soil moisture. Beside those common to all optical techniques, such as shallow soil penetration, cloud contamination, rain effect, utility of TM band is limited by meteorological condition and vegetation. They are often empirical and depend on local meteorological conditions, such as wind speed, air temperature, and humidity (Mallick et al., 2009).

VARIOUS APPROACHES At present, there are several means of surface soil moisture monitoring according to the spectrum range: microwave, thermal, shortwave, and red-infrared band. Two broad approaches according to data type used are discussed. 2.1 Traditional Approaches (Classical Approaches) 2.2 Optical and Thermal Remote Sensing Approaches

Traditional approaches Tradition approaches are basically ground-based techniques. In these techniques of soil moisture estimation, the instrument is in direct contact with soil particles. Such techniques provide more precise data. Gravimetric method involves oven drying a soil sample of known volume at 105 for 24 h. The water content is calculated by subtracting the oven dry weight from the initial field soil weight (Lunt et al., 2005). It seems that this temperature range has been based on water boiling temperature and does not consider the soil physical and chemical characteristics.

Manuscript No. 1369

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J. Indian Water Resour. Soc., Vol. 34, No. 2, April, 2014   Time Domain Reflectometry (TDR) and Frequency Domain Reflectometry (FDR) are based on interaction between electromagnetic wave and soil moisture. In TDR, the travel time T and the length of the probe L, which has been travelled along twice, the propagation velocity V = 2L/T is calculated (Seneviratne et al., 2010; Richardson et al., 1992). The velocity of the pulse changes with changes in soil moisture content due to the relatively large dielectric value of water (Munoz-Carpena, 2012), is the basic principle behind soil moisture measurement using TDR. The limitation of this method is the minimum system rise time. The total rise time consists of the combined rise time of the driving pulse and that of the oscilloscope which monitors the reflections. FDR is similar to TDR, but FDR provides an estimate of the soil moisture content on the basis of a variation in the frequency of a signal due to the dielectric properties of the soil (Trenberth et al., 2009; Evett, 2003). Sensitivity to air gaps, soil salinity, temperature, bulk density and clay content limit the use of this method (Erlingsson et al., 2009). Methods using radioactive sources for pulse generation are also used for soil moisture estimation, but due to environment aspect these methods have limited applications (Neutron Probe Technology, Gamma Ray Attenuation).In Neutron Probe

moisture content (Chanasyk and Naeth, 1996). The Gamma Ray Attenuation is also a radioactive method capable of determining the moisture content in the upper soil layers (up to 1–2 cm). The gamma transmission gives a measure of the change in wet density (Pires et al., 2005), which can give a direct estimate of the soil moisture content (Seneviratne et al., 2010). Another instrument, tensiometer measures the capillary tension. But tensiometers have been found to be unsuitable for measurements in dry soil. The high maintenance requirements limit the use of this method in research (Dukes et al., 2010). The ground penetrating radar (GPR) measurements are based on the transmission and reflection of an electro-magnetic wave in the soil. This method bridges the gap between point measurements and remote sensing (Dobriyal et al., 2012). Steep and rocky slopes limit its use due to the large sizes of the antennas. The use of GPR in forests is difficult because trees behave as reflectors generating erroneous data (Schrott and Sass, 2008). Comparison of traditional ground-based techniques has been presented in the Table 1; on three basic scales cost, accuracy and response time. Measured parameters, limitations and drawbacks of these methods have been also listed.

Table 1: Comparison of traditional ground-based techniques Methods

Technique

Scales Cost

Accurr . High

Response Time 24 H

Measured Parameters

Suitability Limitations

Mass water content

Rocky soils

Drawbacks

References

Labor and time intensive, destructive Radioactive source hazardous to health and envi. Probe length influence accuracy

Trenberth et al., 2009

Gravimetric

Oven drying and weighing

Econo mical

Neutron Probe

Neutrons hydrogen collision

Expen sive

High

1-2 min

Volumetric soil moisture content

Soil specific calibration,

TDR

Change in EM wave velocity with dielectric constant Change in EM wave frequency with dielectric constant Suction fource

Econo mical

High

Instantaneous

Volumetric soil moisture content

High saline soils

Expen sive

Low

Instantaneous

Volumetric soil moisture content

Sensitive to air gap, Temperature

Evett, 2003;Erling sson et al, 2009

Econo mical

High

Instantaneous

Soil water potential

Soil specific calibration, Soil salnity, Clay Content, Dry soil

High maintanance

Dukes et al., 2010.

Change in reflected signature Gamma ray absorption relation with wet density

Expen sive

High

Instantaneous

Schrott and Sass, 2008;

High

Instantaneous

Foreste area, steep and rocky slope Highly stratified soil

Soil dependency

Expen sive

Volumetric soil moisture content Volumetric soil moisture content

Difficulty of use

Pires et al., 2005

FDR

Tensiomete rs GPR

Gamma ray attenuation

0.01

Technology, the neutrons are slowed down by collisions with the nuclei of hydrogen atoms present in the molecules of water in the soil. Since water contains two atoms of hydrogen per molecule, this therefore gives a measure of soil moisture. The output from the neutron probe can be directly related to the soil

Dobriyal et al, 2012;Muno -Carpena, 2012 Richardson et al., 1992.

Advanced in Optical and Thermal Remote Sensing Different approaches to retrieve soil moisture have been used from wide range of satellite data. The basic difference among these approaches are the wavelength band of electro-magnetic

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J. Indian Water Resour. Soc., Vol. 34, No. 2, April, 2014   spectrum used, source of electro-magnetic energy, response measured by the sensor and model to relate signal response to soil moisture content. Different wavelength bands have been used for soil moisture study which varies from optical, thermal, to microwave region. Our review focused on only advance in optical and thermal remote sensing method for soil moisture retrieval. Space-based approaches can be categorized in four categories according to broad electro-magnetic spectrum range. Short wave infrared (SWIR) band is sensitive to the water content of the plant. It is an important band for drought monitoring by remote sensing data. The Normalized difference water index (NDWI) is a typical model used for drought monitoring using the SWIR band. SASI (Shortwave Angle Slope Index) has been widely used to estimate soil and vegetation moisture based on NIR. Table 2 is summarizing remote sensing techniques for near-surface soil moisture estimation.

different landuse/land covers (LULC) on the temporal and spatial variation of soil moisture. In highly diverse landscapes, the uncertainty of the soil moisture estimate from remote sensors is likely to increase by using sensors at coarse spatial resolution. This analysis indicates that regardless of the spatial proximity between two homogeneous fields, LULC is a factor affecting the soil moisture behaviour. Analysis also showed high homogeneity in the soil moisture behaviour within them.   Mallick et al. (2009) demonstrated a technique to estimate volumetric surface (0.05m) soil moisture content Øv using LST-NDVI 2D space in cropped soils at field (0.00090 ) and landscape (0.010 ) scales over selected agricultural regions of India.. Figure 1 shows a conceptual diagram of LST-NDVI triangle for determining the soil wetness index.

Table 2: Summary of remote sensing techniques for near-surface soil moisture estimation Spectrum Domain

Spectrum Range

Properties Observed

Suitability

Advantages

Limitations

Optical

0.4µm-0.9µm

Soil reflection

Vegetated Area

• Fine spatial resolution • Broad Coverage

• Limited surface penetration • Cloud contamination • Smoke, Fog, Rain Effect • Weather dependency

Thermal

3µm-5µm and 8µm-14µm

Radiant Temperature

Bare Land

• Fine spatial resolution • Broad Coverage

• Limited surface penetration • Cloud contamination • Rain effect • Perturbed by meteorological conditions and vegetation

Both

• Weather free operation • Moderate Surface Penetration • Weather free operation • Moderate to high surface penetration (1cm to 1km) • High spatial resolution

• Coarse resolution • Sensitive to roughness and moisture in vegetation

Microwave a)Passive

0.15cm-30cm

b)Active

1.0cm-100cm

• Brightness temperature • Dielectric constant •Backscattering coefficient •Polarizatio •Dielectric constant

Both

The two angle indexes, Shortwave Angle Slope Index (SASI) and Angle at NIR (ANIR) were presented by Khanna et al. (2007). This study, based on the previous research, was carried out by Palacios- Orueta et al. (2006) to develop shortwave angle normalised index (SANI) with a combination of NIR, SWIR1 and SWIR2 MODIS bands. The second angle index, ANIR, was a combination of reflectance values in the Red, NIR and SWIR1 bands. Soil moisture samples regressed on soil moisture content had an average R2 of 0.95. Thus the strong correlations of SASI and Slope across all sample sites indicate that the index could be effectively applied at a regional scale, although it may not be accurate at a global scale. Giraldo et al. ( 2008) have examined the effect of

No Evaporation Land Surface Temperature

(858 nm) and SWIR (1240 and 1640 nm) MODIS bands (Khanna et al., 2007). However, the method is not suitable for bare soil areas and sparsely vegetated areas.

• Varying resolution from near to far range. • Sensitive to roughness and moisture in vegetation • Speckles

Dry edge, SWI =0 No Transpiration Tsmax

Tsmin

MaximumTranspiration

Maximum Evaporation Wet edge, SWI =1 Normalized Difference Vegetation Index

Fig 1: LST-NDVI triangle space

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J. Indiian Water Ressour. Soc., Vol. V 34, No. 2, April, 2014 Triaangular scatterr space from laand surface tem mperature (LS ST) andd normalized difference d veg getation index was utilized to obtaain a soil wetnness index (SW WI). SWI on a given day and a timee (t), is definedd as equation 1: SW WI (t) =

Tsm max(i)-Txx(i)

(1)

Tsm max(i)-Tsmin Whhere Ts(i) is the land surface teemperature of i-th i pixel. Tsminn is the minimum LST T in the trianglle that defines the wet edge and a Tsmax(i) is the max imum LST cor rresponding to i-th NDVI alo ong m dry edge.By know wing the upper and lower lim mits of volumettric soill moisture at thhe surface, the soil wetness inndex on the givven dayy or time cann be converted d to an absolute estimate of voluumetric surfacce soil moistu ure using thee relationship in equuation 2 (Wagnner et al.,1999).. Øv = Ømin + SWI (Ø ( max - Ømin)

(2)

Whhere, Ømin is voolumetric soil moisture conteent at permaneent wiltting point andd Ømax is arithm metic mean off volumetric soil s moiisture content at a saturation an nd field capaciity. Validation of fieldd scale Øv esstimates from ASTER produuce a RMSE of 0.0339. This study showed that the t validity of surface moistuure estimates largely depends on dynamic rangges of LST and a ND DVI, which offten may not be sufficientt as a result of resttricted samplinng window sizze due to low swath in case of fineer resolution sensors. The LST–NDVI triangle methhod showed less errorr for the interm mediate NDVI range 0.35–0.665. This revealed a faairly good corrrelation (0.75) and low RMS SD of 0.027 0 for fractiional vegetatio on cover (Fv) below b 0.5. Figuure 2 shhows special distribution d off volumetric sooil moisture ovver Punnjab state on daay of year (DO OY), 331 and 611. Traansfer coefficieent (ha) for esstimation of sooil moisture: The T aim m of Zhao et all., 2011 work was w to investiggate the potenttial

motely measurrable soil evapporation transffer coefficient of a rem (ha) forr estimation off soil moisture. Relationship has shown in equatioon 3. Mv = a x In (ha) + b

(3) 

Where Mv is volumeetric soil moistture, a and b are a regression coefficiients, respecttively. The soil evaporattion transfer coefficiient (ha), a coeefficient propossed through thee equation 4. Ts - Ta (4) ha =  

Tsd - Ta

Where Ts, Tsd, and Ta T are the land surface tempeerature (LST), dry soiil (without evvaporation) suurface temperaature, and air temperature respectivvely. The lessons drawn from f the studdy can be chaaracterized as followss: (i) “ha” cann serve as  a usseful and easilyy measurable indicatoor for evaluaating the soill moisture sttatus, and it generallly increases as a soil moisturee decreases (iii) the method of landd surface energgy balance wass superior to thhe method of maximuum temperatuure in estimatting ha. But the latter is simplerr in calculationn procedures, with reliable accuracy, a and (iii) wiith increasing soil depth, thee accuracy of “ha” for soil moisturre estimation dropped duee to MODIS’’s increasing inabilitty to measure itt at deeper soill layers. Linear decomposition of mixture pixels: Gao et e al., (2011) suggestted a method of estimating soil moisture based on the of mixture piixels. In this sttudy, mixture linear decomposition d pixel reflectance r w was consider as linear com mbination of vegetattion and soil reeflectance. Figure 3 is showing Red-NIR R featuure space. Wheere Vr and Vn are thhe vegetation canopy Reed and NIR reflectance, respecttively, Sr and Sn S are the direcct soil reflectannce at the Red

  Fig. 2: (a) Spaatial distributiion of surface soil moisturee from MODIIS AQUA and F d AMSR-E ovver Punjab staate on DOY 3331 (26 Novem mber 2004) with w low vegettation cover condition; c (b) Spatial distrribution of surface soil mooisture from M MODIS AQUA A and AMSR R-E over Pun njab state on DOY 61 (2 March M 2005) with high veegetation coveer condition (M Mallick et al., 2009).

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J. Indiian Water Ressour. Soc., Vol. V 34, No. 2, April, 2014    

A

Figure 4. shows resuult of classificaation based on decision tree N band in thhe Shunyi and and soiil moisture inversion by the NIR Tongzhhou area. A coomparison of measures m soil moisture and computted soil moistuure is shown in i Table 3. It is concluded that thee precision off surface soil moisture calcuulated by the methodd proposed in this study cann satisfy appliication needs. Howevver, there weree some limitaations on the soil moisture inversioon model in thhis study. Thee first is that the t minimum and maaximum NDV VI values weree determined by empirical methodds which lead dependency on o regional chharacteristics. The seecond limitatioon is that miixture pixel reflectance r is consideered as the linnear combinattion of vegetaation and soil reflectaance, however it is a combinaation of severaal factors, and third iss that the deteermination of soil s line equattion is on the assumpption that the all a soil pixels locate on the constant soil line, wiith the differennce of soil reflectance only caused c by soil moisturre. But in reality, the soil red r and near infrared i band

NIR Reflectance

 B(V Vr,Vn)

C

                        Red Reflectance R

 

Fig. 3: Red-N Near infrared feature spacee (Gao et al., 11) 201

Fig. 4: Resultt of classificattion based on decision tree and soil moisture inversioon by the NIR F R band in thee Shunyi and T Tongzhou areea (Gao et al., 2011) 2 Table 3: Comparison C between b measu ured soil moistture (MSM) and a computed soil moisture (CSM) in Shu unyi and Tongzhou area (Gao et al, 2011). IID S SY1 S SY2 S SY3 S SY4 S SY5 S SY6 S SY7 S SY8 T TZ1

MSM(%) 29.4 24.8 23.1 25.2 24.9 21.3 27.2 27.8 29.3

CSM(%) 29.9 30.3 32.8 32.3 30.4 30.2 30.8 30.2 30.6

Rel. error 0.016 0.223 0.420 0.283 0.220 0.420 0.132 0.087 0.045

ID TZ2 TZ3 TZ4 TZ5 TZ6 TZ7 TZ8 TZ9 TZ10

andd NIR band resspectively; Rr and Rn are thhe reflectance of mixxture pixels at the t red and nir band, respectiively.

MSM(%) 32.8 25.5 43.5 55.3 53.8 39.4 39.9 33.5 33.4

CSM(%) 30.5 30.3 33.6 34.6 34.6 32.4 33.5 31.5 33.4

Rel. error 0.070 0.190 0.227 0.374 0.357 0.177 0.160 0.058 0.001

reflectaance are alsoo influenced by soil textture, surface roughnness etc .

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J. Indian Water Resour. Soc., Vol. 34, No. 2, April, 2014   Trapezoid Ts – Fv (surface temperature –vegetation fraction cover) space: Trapezoid Ts – Fv (surface temperature – vegetation fraction cover) space was introduced by Zhang et al. (2008) in their model, considering the dry edge and wet edge form two physical boundary of the 2D Ts-Fv space (Sun et al., 2012). Figure 5, shows conceptual diagram of Trapezoid Ts – Fv (surface temperature –vegetation fraction cover) space.

 

is found that the observed dry line, which is directly determined by the scatter plots from the remote sensing data, is usually lower than the theoretical edge (Stisen et al., 2008). Ts - Tmin

TVDI = 1 – SWI =

(5)

Tmax – Tmin

Tsd   Tsd

A (T max)

Theoretical Dry Edge

Ts (K)

Tsd Observed Dry Edge

B (T min) Ts w 0

Tvw Fv

1  

Fig. 5: Ts-Fv space (Zhang et al., 2005, 2008)  An advanced temperature vegetation dryness index (ATVDI) is used to monitor soil moisture status by Sun et al., (2012). Assuming that the vegetation index is linearly related to vegetation fractional cover (Fv), Zhang et al. (2008) proposed a method to determine the theoretical dry edge and wet edge. It

The TVDI calculated from the theoretical dry edge is called the advanced temperature vegetation dryness index (ATVDI) and that calculated from the observed dry edge is called the simple temperature vegetation dryness index (STVDI). A comparison of values is shown in Table 4.

Table 4: Comparison between ATVDI and STVDI (Sun et al., 2012) Site

ATVDI

EF1 EF2 EF3 EF4 EF5 EF6 EF7 EF8 EF9 EF10 EF11 EF12 EF13 EF15 EF19 EF20

R2 0.13 0.43 0.47 0.42 0.74 0.33 0.41 0.22 0.75 0.15 0.53 0 0.65 0.43 0.42 0.09

STVDI Value Range 0.00-0.67 0.04-0.54 0.00-0.35 0.13-0.61 0.02-0.65 0.16-0.45 0.18-0.71 0.14-0.56 0.17-0.52 0.25-0.67 0.23-0.49 0.14-0.63 0.26-0.79 0.26-0.79 0.29-0.87 0.31-0.64

R2 0.07 0.3 0.43 0.12 0.68 0.06 0.01 0.11 0.62 0.00 0.25 0 0.48 0.36 0.19 0

Value Range 0.00-0.85 0.06-0.69 0.00-0.59 0.17-0.72 0.03-0.81 0.27-0.53 0.29-0.57 0.33-0.79 0.25-0.74 0.26-0.61 0.36-0.78 0.36-0.74 0.25-0.78 0.34-0.82 0.51-0.85 0.41-0.90

In Situ Soil Moisture Value Range 0.25-0.47 0.31-0.40 0.40-0.51 0.08-0.16 0.30-0.39 0.31-0.35 0.28-0.37 0.10-0.20 0.28-0.35 0.31-0.36 0.23-0.31 0.27-0.36 0.25-0.35 0.09-0.10 0.25-0.32 0.25-0.35

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J. Indian Water Resour. Soc., Vol. 34, No. 2, April, 2014   The results indicated that the theoretical dry edge forms a trapezoid shape during the period of data coverage whereas the observed dry edge does not. Across all points, the R2 of the ATVDI (0.35) is greater than that for the STVDI (0.28). Comparative study of optical-thermal remote sensing approaches for surface soil moisture has been summarised in Table 5; on three basic scales; techniques, parameter used, land type suitability and limitations of approach. Table 5. Comparative study of optical-thermal remote sensing approaches for surface soil moisture.

SUMMARY AND CONCLUSION Attempts have been made by several researcher to review the soil moisture measuring techniques (Robinson et al., 2008; Erlingsson et al., 2009; Panet et al., 2010, Mulder et al.,2011, Dobriyal et al., 2012) the best suited for different landscape types. The choice of a method for soil moisture estimation would depend on the application and the resource availability. While choosing a method one should consider characteristics, such as the accuracy of the results, calibration requirements, spatial resolution, cost and ease in using the methods. Different ground-based methods have been discussed here with their advantages and limitations. These include gravimetric method to neutron probe technology, time domain reflectometry (TDR), frequency domain reflectometry (FDR), tensiometers and more accurate and latest ground penetrating radar method (GPR), gamma ray attenuation. Although, GPR method bridges the gap between point measurements and remote sensing (Dobriyal et al., 2012). Steep and rocky slopes limit its use due to the large sizes of the antennas. The use of GPR in forests is difficult because trees behave as reflectors generating erroneous data (Schrott and Sass, 2008). Groundbased techniques provide more precise data. The instruments can be accurately calibrated, give depth wise measurements of soil moisture. Only point measurements are taken, and this makes spatial interpretation a difficult task (Trenberth et al., 2009).The use of ground-based methods is limited by factor labour and time intensive, while in other methods (Gamma ray, Neutron probe) radioactive source is hazardous to health and environment. On the basis of the strengths and weaknesses of each of the methods reviewed, it can be conclude that the ground-based method is accurate and inexpensive but is destructive, slow and time consuming and does not allow replications in the same location, thereby having limited spatial coverage. Different wavelength bands has been used for soil moisture study. These bands vary from optical, TM, to microwave region. Our review only focused on optical and thermal remote sensing techniques.Various studies have been carried out using optical band (Khanna et al., 2007; Giraldo et al., 2008;Zhao et al., 2010; Gao et al., 2011) for retrieving the soil moisture but these are limited by shallow soil penetration, cloud contamination, smoke, fog and rain effect and dependency of spectral characteristic on various soil contaminants. Methods using optical bands are not suitable for bare soil areas and sparsely vegetated areas. In addition to water content, spectral characteristics of soil’s also depend on numerous other factors, such as mineral composition, organic matter, soil texture, and surface roughness.

Surface temperature property is used in thermal infrared band of electro-magnetic spectrum to retrieve surface soil moisture in different studies (Mallick et al., 2009; Sun et al., 2012). Thermal inertia method, Crop water stress index (CWSI) Water Deficiency Index (WDI),Thermal Inertia Method (only for bare soil and sparsely vegetation regions), temperature vegetation dryness index (TVDI), LST-NDVI 2D space are used in thermal remote sensing of soil moisture. Beside those common to all optical techniques such as shallow soil penetration, cloud contamination, rain effect, utility of TM band is limited by meteorological condition and vegetation. They are often empirical and depend on local meteorological conditions, such as wind speed, air temperature, and humidity (Mallick et al., 2009). The methods reviewed here will be of use to generate information for decision making for suitability and reliability of different approaches to retrieve surface soil moisture. Although, the review has examined a number of literature sources, it seems to be practically impossible to include all the publications on the topic. Besides, it is possible that some commonly used methods are only briefly referred. To justify our work, review only consider recent advanced in soil moisture retrieval methods using optical and thermal space based data.These gaps could be filled by subsequent contributions, and there is a scope for further discussions about the current status of available knowledge on methods to estimate soil moisture and its relationship with other meteorological and landscape parameters.

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