Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tres20

Download by: [Capital Normal University] Date: 20 October 2017, At: 01:07

International Journal of Remote Sensing

ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20

Downscaling of passive microwave soil moistureretrievals based on spectral analysis

Aifen Zhong, Anqi Wang, Jiwei Li, Tingbao Xu, Dan Meng, Yinghai Ke,Xiaojuan Li & Yun Chen

To cite this article: Aifen Zhong, Anqi Wang, Jiwei Li, Tingbao Xu, Dan Meng, Yinghai Ke,Xiaojuan Li & Yun Chen (2018) Downscaling of passive microwave soil moisture retrievalsbased on spectral analysis, International Journal of Remote Sensing, 39:1, 50-67, DOI:10.1080/01431161.2017.1378456

To link to this article: http://dx.doi.org/10.1080/01431161.2017.1378456

Published online: 19 Sep 2017.

Submit your article to this journal

Article views: 20

View related articles

View Crossmark data

Downscaling of passive microwave soil moisture retrievalsbased on spectral analysisAifen Zhonga, Anqi Wangb, Jiwei Lic, Tingbao Xud, Dan Menga, Yinghai Kea,Xiaojuan Lia and Yun Chene

aBeijing Laboratory of Water Resource Security, Base of the State Key Laboratory of Urban EnvironmentProcess & Digital Modeling, Capital Normal University, Beijing, China; bNorth China University ofTechnology, Beijing, China; cDepartment of Geosciences, University of Massachusetts, Amherst, USA;dFenner School of Environment and Society, Australian National University, Canberra, Australia; eCSIROLand and Water, Canberra, Australia

ABSTRACTThe retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods formonitoring soil moisture. However, the spatial resolution of pas-sive microwave soil moisture products is generally low; thus,existing soil moisture products should be downscaled in order toobtain more accurate soil moisture data. In this study, we explorethe theoretical feasibility of applying the spectral downscalingmethod to the soil moisture in order to generate high spatialresolution soil moisture based on both Moderate ResolutionImaging Spectroradiometer and Fengyun-3B (FY3B) data. We ana-lyse the spectral characteristics of soil moisture images coveringthe east-central of the Tibetan Plateau which have different spatialresolutions. The spectral analysis reveals that the spectral down-scaling method is reliable in theory for downscaling soil moisture.So, we developed one spectral downscaling method for derivingthe high spatial resolution (1 km) soil moister data from the FY3Bdata (25 km). Our results were compared with the ground truthmeasurements from 15 selected experimental days in 16 differentsites. The average coefficient of determination (R2) of the spectraldownscaling increased nearly doubled than that of the originalFY3B soil moisture product. The spectral downscaled soil moisterdata were successfully applied to examine the water exchangebetween the land and atmosphere in the study regions. Thespectral downscaling approach could be an efficient and effectivemethod to improve the spatial resolution of current microwavesoil moisture images.

ARTICLE HISTORYReceived 9 June 2017Accepted 3 September 2017

1. Introduction

Soil moisture is a principal variable for controlling the water cycle between the terrestrialenvironment and atmosphere (Dobriyal et al. 2012). It affects the water cycle in manyaspects, such as infiltration of rainfall to the land surface, surface evaporation, and theexchange of water. These effects of the soil moisture would future contribute to the

CONTACT Anqi Wang wanganqi2015@ncut.edu.cn North China University of Technology, Beijing 100000, China

INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018VOL. 39, NO. 1, 50–67https://doi.org/10.1080/01431161.2017.1378456

© 2017 Informa UK Limited, trading as Taylor & Francis Group

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

terrestrial environment change and global climate change (Ko, Mascaro, and Vivoni2016; Malbéteau et al. 2016). It was often applied in the remote-sensing studies forthe drought monitoring, crop yield estimation, and related research because it couldrepresent condition for plant growth and development (Seneviratne et al. 2010; Zhaoand Li 2015; Im et al. 2016). Therefore, the accuracy of soil moisture remote-sensingestimation method has significant meanings to the agriculture yield prediction, terres-trial environment observation, and global change studies (Lu et al. 2012; Milly, Dunne,and Vecchia 2005).

Different remote-sensing sensors can provide soil moister information with differenttemporal and spatial resolutions. The microwave bands had advantage than the opticalbands because they were less affected by weather conditions (Choi and Hur 2012; Wanget al. 2016). Thus, the microwave bands have been widely used for the monitoring of thesoil moisture content. Generally, passive microwave sensors detect the soil moisturecontent over larger areas. And, it also had a higher temporal resolution than activemicrowave sensors (Mladenova et al. 2014). Recently, the soil moisture products derivedfrom the passive microwave have been widely applied to the monitoring of droughtsand landslides (Bolten et al. 2010; Ray, Jacobs, and Cosh 2010). However, the currentpassive microwave soil moisture products all have low spatial resolutions (~25 km). Forinstance, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) onboard theNational Aeronautics and Space Administration’s (NASA’s) EOS Aqua spacecraft, whichoperated from 2002 to 2011; the European Space Agency’s Soil Moisture Ocean Salinity(SMOS) spacecraft (launched in November 2009); the Microwave Radiation Imageronboard the National Satellite Meteorological Centre’s (NSMC’s) Fengyun-3B (FY3B)spacecraft (launched in November 2010); and the Advanced Microwave ScanningRadiometer-2 onboard the Japan Aerospace Exploration Agency’s Global ChangeObservation Mission 1st-Water spacecraft (launched in May 2012). Only the NASA’s SoilMoisture Active Passive (SMAP) satellite (January 2015) was designed to provide highresolution soil moisture products. However, this active sensor stopped working in July2015. Therefore, the spatial resolution of the SMAP soil moisture product is still at 36 km(Song and Jia 2016). The low spatial resolution of current passive microwave soilmoisture products limits the application in fine scale applications. So, downscaling thecoarse soil moisture products to a higher spatial resolution had significant meanings tothe fine agriculture monitoring, water cycle observation at the regional scale, and floodsand drought controls (Choi and Hur 2012).

The current methods for downscaling passive microwave soil moisture productsinclude semi-physical algorithm and physical model method. The semi-physicalmethod is applied based on the triangular/trapezoidal eigenspace theories relatedwith the normalized difference vegetation index (NDVI) and the land surface tem-perature (LST) (Carlson 2007; Choi and Hur 2012). This method generally assesses theLST, NDVI, albedo products of Moderate Resolution Imaging Spectroradiometer(MODIS) to build a positive correlation function with the soil moisture for down-scaling the passive microwave soil moisture products (Chauhan, Miller, and Ardanuy2003; Kim and Hogue 2012; Piles et al. 2011; Song, Jia, and Menenti 2014). The semi-physical method is most commonly used for the soil moisture downscaling. However,the semi-physical method employs statistical regression only for the downscaling. So,it lacks the physical underpinnings. Moreover, it also hasn’t included the regression

INTERNATIONAL JOURNAL OF REMOTE SENSING 51

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

scale effect. Because the optical products cannot be effectively applied in cloudyconditions, the weather conditions generally obscured the semi-physical method. Thephysical model-based method has clear physical underpinnings because it is devel-oped based on physical models (land surface–atmosphere interaction models) (Merlinet al. 2005; Merlin et al. 2008; Merlin et al. 2013). But the application of the physicalmodel-based method requires data for describing the terrain, soil texture, and manyother auxiliary parameters. So, it cannot be applied to large areas with high effec-tiveness. So, the effective downscaling method for the passive microwave soil moist-ure products is needed.

The spectral downscaling method has been used for integrating the water vapourand rainfall fields (Montopoli, Pierdicca, and Marzano 2012; Rebora et al. 2006). It offersthe new promise for soil moisture image downscaling. It can be correlated to thephysical model-based method. Moreover, it utilizes the spatial characteristics of variablesand considers physical mechanisms of the semi-physical method in the process ofdownscaling. In this method, an originated image in the spatial domain is convertedto the image in the frequency domain by using the Fourier transformation. The trans-formation of the frequency domain image aims to include more spatial details for thedownscaled spatial image by using the inverse Fourier transformation. Wang et al.(2015) used the spectral downscaling method to downscale the AMSR-E soil moistureproducts from a spatial resolution of 25 km to a spatial resolution of 5 km. In thatprevious study, the amplitude information of the soil moisture image with a low spatialresolution was preserved as that of the unresolved high spatial resolution soil moistureimage. Then, the high resolution optical factors were introduced to obtain the phaseinformation of the unresolved high spatial resolution soil moisture image. This processwas made by using the multivariate linear statistical regression method. The studyresults showed that this method is reliable based on a comparison between theAMSR-E soil moisture products and ground measured soil moisture data. However, theprevious studies only directly employed the spectral analysis results of the integratedwater vapour fields. This means a spectral analysis of the soil moisture image is lacking.Therefore, the relationship between power spectral density (PSD) and spatial frequencyof soil moisture is still unknown. Thus, the theoretical basis of the spectral downscalingmethod is still needed to be exploring. It limits the application of this method in soilmoisture image downscaling.

This article developed spectral downscaling of soil moisture data based on thespectral characteristics of soil moisture image. A geographically weighted regression(GWR)-based approach was developed to set the phase information of the unresolvedhigh spatial resolution soil moisture image to the spectral downscaling method. Weselected the east-central of the Tibetan Plateau as study regions. We analyse the spatialand spectral characteristics of soil moisture images from different sensors with differentspatial resolutions. Finally, we used the FY3B soil moisture products as the experimentaldata source and the optical land products of MODIS as auxiliary data. We selected15 days data between 1 May 2012 and 30 September 2012 to downscale the FY3B soilmoisture products. We aim to downscale from a spatial resolution of 25 km to a spatialresolution of 1 km. The spectral downscaled results were evaluated by using groundtruth soil moisture measurements.

52 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

2. Method

The FY3B soil moisture products are developed for the application of the China based onthe parameters calibration in China. The FY3B soil moisture product has better perfor-mance than AMSR-E, SMOS, and the other satellite soil moisture products when applyingin the central Tibetan Plateau. The FY3B soil moisture product could be reliably appliedin drought monitoring, studying hydrology, and climate changes (Cui et al. 2016a,2016b). In this article, we introduced an example of downscaling the FY3B soil moistureproduct from a spatial resolution of 25 km to 1 km by using the spectral downscalingmethod. The method is based on the spectral characteristics of soil moisture image.

2.1. Amplitude information

A frequency domain image can be transferred from the spatial image using the Fouriertransformation. Frequency domain images can be expressed in terms of amplitude andphase information as

FvðsÞ ¼ AvðsÞ � e�j�Ψv sð Þ (1)

where Fv(s) is the frequency domain image, s is the spatial frequency, Av(s) is theamplitude, and ΨvðsÞ is the phase. The inverse Fourier transformation is following,

VðrÞ ¼ FFT�1 FvðsÞf g ¼ FFT�1 AvðsÞ � e�j�ΨvðsÞn o

(2)

where VðrÞ is the spatial domain image obtained using the inverse Fourier transforma-tion and r is the 2D position vector. The key idea for obtaining the unresolved highspatial resolution soil moisture image is to determine its amplitude and phase informa-tion. In the frequency domain, the relationship between the PSD and the amplitude isfollowing,

ϕvðsÞ ¼�

FvðsÞj j2�

¼�jAvðsÞj2

�(3)

where ϕv(s) is the PSD, ⟨∙⟩ is the expectation operator, and |∙| is the modulus of a complexnumber.

According to the method from Montopoli, Pierdicca, and Marzano (2012) which isused to estimate the amplitude information of an unresolved high spatial resolutionwater vapour map, we assume that the same power law can describe the relationshipbetween the PSD and the spatial frequency of the soil moisture images. The power lawrelationship can be calculated from the low spatial resolution soil moisture image. Then,the amplitude information of the unresolved high spatial resolution image can beestimated by using Equation (3).

We analyse the spatial and spectral characteristics of soil moisture image to verify theabove assumption for improving the spectral downscaling method in soil moistureimage. Geostatistics focuses on the spatial distribution and spatial autocorrelation ofvariable factors, which is one of the most effective methods for analysing the spatialcharacteristics of soil moisture. Semi-variance function, also known as the semi-vario-gram, can be used to analyse the spatial variability and spatial autocorrelation of

INTERNATIONAL JOURNAL OF REMOTE SENSING 53

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

variable factors and is a key function of studying soil variability in geostatistics. Thus, weanalyse the spatial characteristics of soil moisture using the semi-variance function fromgeostatistics. If the spatial analysis is reasonable, we will derive the relation betweenpower spectra and spatial frequency from the semi-variance function using the methodfrom Montopoli, Pierdicca, and Marzano (2012).

2.2. Phase information

The phase information of the unresolved high spatial resolution image must be obtainedto conduct spectral downscaling after the amplitude information has been estimated.These phase information for covering large study areas can be obtained by downscalingthe low spatial resolution soil moisture map to the desired fine spatial resolutionthrough semi-physical method. The semi-physical approach incorporates informationfrom the high spatial resolution remote-sensing data (Wang et al. 2015). This methodpreserves the information from the soil moisture data which has a low spatial resolution.It also introduces remote-sensing data with a high spatial resolution. Thus, the down-scaled map could have additional information in both spatial and frequency domains. Inaddition, if we use the phase information from the downscaled map in the spectraldownscaling, we will obtain spectral downscaling results with an increased level ofspatial details.

The GWR approach within the semi-physical method considers both influence of thespatial locations of the variables in the regression relationship and effect of scale on theregression relationship. So, it can obtain better downscaling results than the normallinear regression (Yu, Di, and Yang 2008). Therefore, this article uses the GWR method todownscale the FY3B soil moisture products from a spatial resolution of 25 km to that of1 km. We selected the MODIS land products, including the daily surface temperatureproduct (MOD11A1) with a spatial resolution of 1 km, the 16 days NDVI product(MOD13A2), and the 8 days albedo product (MCD43B3) for combining the data fromthe Aqua and Terra satellites as auxiliary data. Moreover, the phase information of thedownscaled (GWR) soil moisture images is employed as the phase information of theunresolved high spatial resolution soil moisture image.

Figure 1 shows the schematic diagram for performing the spectral downscaling ofFY3B soil moisture products from 25 km resolution to 1 km resolution. The spectraldownscaled results were obtained by using the inverse Fourier transformation with thepreviously estimated amplitude and phase information.

3. Study area and data set

3.1. Study area

The east-central of the Tibetan Plateau is an important agricultural region where theprimary agriculture activities were animal husbandry of Tibet Autonomous Region ofChina. The acquisition of soil moisture content is very important for drought monitoring.The remote-sensing technology is an efficient way to obtain soil moisture informationwithin a large area because of the remoteness of this area and the limited numbers ofmonitoring stations. In Figure 2, we selected the east-central of the Tibetan Plateau as

54 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

the study area. The study area is located between 91.1°–93.0°E and 30.8°–32.8°N. It has atotal area of 49,297 km2. The elevations range from 4110 to 6543 m. The climate is semi-arid with the average annual precipitation of 430 mm. The soil type of this area is mostlysandy with a rough texture. The major land-cover type is grassland (83%) with smallportions of bare land (13%), waterbodies, glaciers, and developed regions.

3.2. Ground-based measurements

To validate the downscaled results, we downloaded ground truth measurements of soilmoisture from the National Earth System Science Data Sharing Infrastructure (http://www.geodata.cn/). These data cover the east-central of the Tibetan Plateau from 1 May2012 to 30 September 2012. The data include three spatial resolutions (1°, 0.3°, and 0.1°)in four observation depths (5, 10, 20, and 40 cm). A total of 56 validation sites wereassessed in this study. Because of the large study area and the depth of microwave soilmoisture products (1 cm), the coarse (1°) ground truth measurements of soil moisturecontent at depths of 5 cm were used for validation. In summer, thawing of frozen soil inthe seasonally frozen areas occurs. Therefore, some study areas were marshland andlakes where the soil moisture content exceeds the maximum data of the FY3B soilmoisture products (Song and Jia 2016). So, we exam the validation sites for removingthe abnormal values. A total of 16 effective validation sites (named as L03, L05, L06, L08,L09, L11, L13, L15, L16, L19, L27, L29, L33, L34, L37, and L38) were used in this study.Besides, we assessed the daily precipitation data from the Tibetan Plateau Scientific DataCentre (http://www.tpedatabase.cn/) to compare the precipitation with ground truth soilmoisture, FY3B soil moisture, and our downscaled soil moisture. The Naqu precipitationsite was the available one in the study area. And, the L19 among all validation sites of

Relation Analysis between PSD and spatial frequency

for FY3B soil moisture image

FY3B soil moisture image (25 km)

MODIS products: NDVILST Albedo (1 km)

FY3B soil moisture image (1 km)

The amplitude information of the unresolved soil moisture

image (1 km)

The phase information of the unresolved soil moisture

image (1 km)

Spectral downscaled soil moisture image (1 km)

FFT-1

FFT

FFT

Result analysis and evaluation

Spatial characterization of soil moisture images

Soil moisture images with different spatial resolution

obtained from different sensors

Spectral characterization of soil moisture images

Spatial and Spectral analysis of soil moisture

The process of spectral downscaling

GWR

Figure 1. Schematic diagram for performing the spectral downscaling of FY3B soil moistureproducts.

INTERNATIONAL JOURNAL OF REMOTE SENSING 55

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

soil moisture is the closest one to the Naqu precipitation site which indicates they havesimilar meteorological conditions. The locations of the validation sites of soil moistureand Naqu precipitation site are shown in Figure 2.

3.3. Data set for satellites

To analyse the spatial and spectral characteristics of soil moisture, we obtained soilmoisture products from different satellites, including the FY3B soil moisture products(NSMC, http://satellite.nsmc.org.cn) and the AMSR-E soil moisture products (NationalSnow and Ice Data Center, http://nsidc.org/). FY3B soil moisture product is retrievedby using 10.7 GHz (X band) and 18.7 GHz (K band) brightness temperature based on theQp model (Parinussa et al. 2014). AMSR-E soil moisture product is retrieved by using6.925 GHz (C band) and 10.65 GHz (X band) brightness temperature following thealgorithm by Njoku et al. (2003). The local solar time of ascending and descendingorbits of both Aqua and FY3B cross the equator is about 1:30 p.m. and 1:30 a.m.,respectively. The retrieval accuracy of both AMSR-E and FY3B soil moisture product isless than or equal to the root mean square error (RMSE) of 0.06 m3 m−3. The AMSR-E soilmoisture products have been widely used in the soil moisture estimation and

Figure 2. The study regions in Tibetan Plateau. The validation sites of soil moisture, Naqu precipita-tion site, and the digital elevation model (DEM) were showed in the map.

56 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

downscaled research in recent years. The FY3B soil moisture product is being graduallyapplied to soil moisture estimation in China. As shown in Table 1, we used the AMSR-Esoil moisture products of the 2011 images only, because the instruments stoppedworking in October 2011. The SMOS soil moisture products also available in the studyperiod, but the precision of the SMOS product is low in China due to the effects of thesatellite’s altitude. So, we didn’t use the SMOS data in this study.

The downscaling of the present microwave soil moisture products to a finer spatialresolution is necessary to analyse the spatial and spectral characteristics of soil moistureat different spatial resolutions. Therefore, this article uses the GWR method and MODISland products to downscale the FY3B soil moisture products (from 25 to 1 km). Theground truth measurements were also compared to those of soil moisture maps fordifferent satellites of the spatial and spectral analysis.

We normalized the soil moisture data in Table 1 by using the minimum–maximummethod to reduce the influence of the different sensors, calculation errors, and noise inthe spatial analysis. We performed a normal distribution test at the 0.05 significancelevel using the single-sample K–S method. The results showed that the soil moisturedata (Table 1) are normally distributed. We then used the Geostatistical Analyst toolboxin the ArcGIS 10.1 Software to calculate the semi-variance function, which shows thatthe variation pattern of soil moisture was under the hypothesis of second-order spatialstationarity and spatial isotropy.

4. Results and discussion

4.1. Spatial and spectral characterization of soil moisture

We used ArcGIS 10.1 Software to calculate the semi-variance function of the normalizedsoil moisture data in Table 1. The results are consistent with those obtained by perform-ing the analysis in the GS+ Version 9 Software.

The curves in Figure 3 describe the semi-variance functions of the soil moisture data.The results show that power law models (represented by the black fitted curves) providegood descriptions of the variation patterns of soil moisture as:

γ hð Þ ¼ αhβ (4)

where γ(h) indicates the semi-variogram of the soil moisture, h indicates the scalar lagdistance, and α and β are regression parameters.

The power law model (α = 0.000025 and β = 0.64) is most consistent with thecurves describing the semi-variance functions of the normalized soil moisture datasources in Table 1. This agreement indicates that the same variation pattern existsbetween the low spatial resolution (25 km) and the high spatial resolution (1 km) soil

Table 1. List of data sources for analysing the spatial and spectral characteristics of soil moisture.Soil moisture Year Month and day Temporal resolution Spatial resolution (km)

FY3B 2011 29 August; 14 September 1 day 252012 3 August; 14 September 1 day 25

AMSR-E 2011 29 August; 14 September 1 day 25GWR 2012 3 August; 14 September 1 day 1Measured data 2012 3 August; 14 September 0.5 h –

INTERNATIONAL JOURNAL OF REMOTE SENSING 57

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

moisture data in the same study region and time period. We then resampled theMODIS products from a spatial resolution of 1 km to 500 m, 2.5 km, and 5 kmseparately by using the bilinear interpolation method. We also downscaled theFY3B soil moisture products from a spatial resolution of 25 km to 500 m, 2.5 km,and 5 km by using the GWR downscaling method. The results show that the variationpatterns of the soil moisture data with a spatial resolution of 2.5 km are close to thatof the FY3B, AMSR-E products and the ground truth measurements of soil moisture.There are relatively large deviations between the variation patterns associated withthe soil moisture data of the spatial resolutions of 500 m or 5 km with the FY3B,AMSR-E products, and the ground truth measurements. This difference may be due tothe characteristics of soil moisture itself and/or errors caused by resampling theMODIS data.

The semi-variance function of soil moisture describes the variations of spatial hetero-geneity with changing distance in the spatial domain, whereas the PSD describes thevariations of soil moisture with spatial frequency in the frequency domain. The semi-variance function and the PSD describe generally the same. They are just different in thedescription of the domain. The relationship between PSD of soil moisture images andthe spatial frequency can be established in the frequency domain if the variationpatterns of soil moisture could be transferred from the spatial domain to the frequencydomain. The method from Montopoli, Pierdicca, and Marzano (2012) has been appliedfor deriving the power spectra of the semi-variance function in the atmospheric inte-grated water vapour fields. That inspires us to interpolate the algorithm to build the

Figure 3. Normalized semi-variogram of soil moisture data listed in Table 1. The black fitted curvesrepresent power law models with the form γ = αhβ. The middle black curve provides the best fit tothe observations, and the parameter values associated with this curve are α = 0.000025 andβ = 0.64.

58 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

relationship between the PSD and the spatial frequency of the soil moisture images. Thepower law is expressed as

ϕðsÞ � s�v (5)

where ∅(s) indicates the PSD, s indicates the spatial frequency, and v is a coefficient thatis associated with α and β. The detailed derivation can be found in Montopoli, Pierdicca,and Marzano (2012).

We used MATLAB 2012 Software to calculate the PSD and spatial frequency of theexperimental soil moisture images to validate the power law relations between PSD andspatial frequency at different scales. A variety of models, such as exponential model,power law model, logarithmic model, and linear model, were fitted to the PSD andspatial frequency of the FY3B and GWR soil moisture images. The results showed thatthe power law model yields the best coefficient of determination (R2) values.Considering the effects of noise and the calculation errors involved in the downscalingprocess, the results (Figure 4) are consistent with the previous discussion. In thefrequency domain, the power law could effectively describe the relationships betweenthe PSD and the spatial frequency for both low spatial resolution (25 km) and highspatial resolution (1 km) soil moisture images. According to the analysis presentedabove, the amplitude information of the unresolved high spatial resolution image canbe estimated using the low spatial resolution soil moisture image.

4.2. Spectral downscaled results

We selected the FY3B soil moisture products and optical MODIS products to downscalethe FY3B soil moisture (from 25 to 1 km). Figure 5 shows the spectral downscaled soilmoisture results on 29 May 2012 (before the rainy season; Figure 5(c)) and 14 September

Figure 4. The power laws describing the relationships between the PSD and the spatial frequency ofthe FY3B and GWR soil moisture data on 3 August 2012 and 14 September 2012.

INTERNATIONAL JOURNAL OF REMOTE SENSING 59

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

2012 (after the rainy season; Figure 5(d)). The original FY3B soil moisture products arealso shown in Figures 5(a) and (b). The soil moisture content on 14 September wassignificantly higher than that on 29 May, which is agreeing with the actual response ofsoil moisture to seasonal effects. The trend of the spatial distribution of the soil moisturecontent in the spectral downscaled soil moisture map is consistent with the originalFY3B soil moisture data. This is represented by the central regions which is higher thanthe other regions of the study area. The spectral downscaled results have a much higherspatial variability than the original data.

The FY3B soil moisture products clearly reflect the changes of soil moisture contenton the Tibetan Plateau. Table 2 summarizes the R2 and RMSE of the FY3B soil moistureproduct and the spectral downscaled soil moisture data. The results were calculatedthrough the comparisons between satellite results with the ground truth measurementson the 15 selected days. The coefficient of determination of FY3B soil moisture rangesfrom 0.070 to 0.480, and the average R2 is 0.263. The RMSE ranges from 0.048 to

Figure 5. The 25 km FY3B soil moisture maps (a,b) and 1 km spectral downscaled soil moisturemaps (c,d) in the study area and corresponding to 29 May 2012 and 14 September 2012,respectively.

60 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

0.130 m3 m−3, and the average RMSE is 0.074 m3 m−3, which is slightly higher than thesuggested accuracy goal (less than or equal to 0.06 m3 m−3) for the RMSE of FY3B soilmoisture products. The coefficient of determination of spectral downscaled data rangesfrom 0.291 to 0.780, and the average R2 is 0.510. The RMSE ranges from 0.040 to0.090 m3 m−3, and the average RMSE is 0.061 m3 m−3. The average R2 of the spectraldownscaled data improved nearly doubled than the FY3B soil moisture data. And, theaverage RMSE was decreased by 17.6% than the originated one. The accuracy of thespectral downscaled results illustrated the significant improvement in both accuracy andspatial resolutions.

Figure 6 shows the relationships between the average satellite results and the groundtruth measurements at the 16 validation sites. The average values of each site in 15

Table 2. Summary of the R2 and RMSE of the FY3B soil moisture product and the spectral down-scaled soil moisture data relative to ground-based measurements of soil moisture on the 15 selecteddays.

FY3B SMa (25 km) Downscaled SM (1 km)

Date R2 RMSE (m3 m−3) R2 RMSE (m3 m−3)

29 May 2012 0.170 0.061 0.291 0.05430 May 2012 0.110 0.049 0.480 0.04431 May 2012 0.379 0.048 0.379 0.04813 June 2012 0.250 0.048 0.300 0.04023 July 2012 0.120 0.130 0.460 0.0803 August 2012 0.280 0.068 0.551 0.0577 August 2012 0.440 0.079 0.601 0.09019 August 2012 0.480 0.088 0.570 0.0773 September 2012 0.240 0.081 0.500 0.0644 September 2012 0.200 0.078 0.640 0.0588 September 2012 0.460 0.068 0.490 0.0639 September 2012 0.070 0.086 0.510 0.06914 September 2012 0.140 0.082 0.780 0.05020 September 2012 0.291 0.085 0.520 0.07230 September 2012 0.310 0.055 0.581 0.047Average 0.263 0.074 0.510 0.061

aSM represents soil moisture.

Figure 6. Relationships of the 15 days average FY3B soil moisture product, the 15 days averagespectral downscaled soil moisture data, and the ground truth measurements of soil moisture (a,b) atthe 16 validation sites.

INTERNATIONAL JOURNAL OF REMOTE SENSING 61

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

selected days were assessed in the comparisons. The R2 and the RMSE of the averagespectral downscaled soil moisture data are 0.710 and 0.038 m3 m−3, respectively,whereas the R2 and the RMSE of the average values of FY3B soil moisture product are0.464 and 0.049 m3 m−3, respectively. The spectral downscaled results alleviate themismatches between the FY3B soil moisture product and the ground truthmeasurements.

Table 3 summarizes the average values and the standard deviation at the 16 valida-tion sites for the 15 selected days for the FY3B soil moisture product, the spectraldownscaled soil moisture data, and the ground truth measurements. The ground truthmeasurements of soil moisture range from 0.133 to 0.320 m3 m−3. The FY3B-based soilmoisture values range from 0.104 to 0.418 m3 m−3. The spectral downscaled soilmoisture data range from 0.104 to 0.368 m3 m−3, which is closer to the range of theground measurements. The improvement of the downscaled results may due to thehigh spatial resolution optical data for reducing the extreme values of soil moisturecontent to some extent. Meanwhile, both downscaled results and variations of the FY3Bsoil moisture content are more obvious than that of ground measurements during thestudy period. The measurement depth of the ground measurements is 5 cm, whereasthe measurement depth of satellite soil moisture products is approximately 1 cm. Thesoil at shallower depth is highly sensitive to evaporation, rainfall, and other weatherconditions. So, the temporal dynamics between the dry and wet seasons are moreobvious in the microwave soil moisture products.

The satellite soil moisture content is smaller (drier) than the ground measurementsduring the selected days in May. This phenomenon may because of the melting of thesnow, ice, and frozen soil in the Tibetan Plateau area during April and May in each year.The snowmelt has a substantial impact on soil moisture remote-sensing estimation. Thesatellite soil moisture content is larger (wetter) than the ground measurements on mostof the selected days from June to September. This may be caused by the plants flourishin the study area. The water content of the plants influences the surface temperatures

Table 3. Summary of the average values and the standard deviation at 16 validation sites for the 15selected days for the FY3B soil moisture product (m3 m−3), the spectral downscaled soil moisturedata, and the ground measurements of soil moisture.

Ground SM FY3B SM (25 km) Downscaled SM (1 km)

Date Average St. dev. Average St. dev. Average St. dev.

29 May 2012 0.154 0.052 0.125 0.006 0.125 0.01430 May 2012 0.144 0.049 0.132 0.005 0.125 0.01531 May 2012 0.133 0.048 0.104 0.025 0.104 0.02113 June 2012 0.149 0.047 0.165 0.004 0.155 0.02923 July 2012 0.320 0.067 0.418 0.082 0.368 0.0863 August 2012 0.290 0.072 0.274 0.064 0.296 0.0827 August 2012 0.290 0.073 0.327 0.093 0.366 0.07519 August 2012 0.285 0.081 0.344 0.036 0.338 0.0633 September 2012 0.280 0.086 0.309 0.032 0.297 0.0734 September 2012 0.274 0.081 0.300 0.023 0.303 0.0508 September 2012 0.227 0.082 0.202 0.075 0.247 0.0519 September 2012 0.224 0.085 0.241 0.043 0.258 0.05314 September 2012 0.247 0.089 0.242 0.032 0.255 0.05320 September 2012 0.311 0.062 0.357 0.082 0.343 0.09230 September 2012 0.235 0.062 0.232 0.017 0.211 0.054Average 0.237 0.069 0.251 0.041 0.253 0.054

62 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

which introduces systematic errors into the retrieval of the satellite soil moistureproducts (Parinussa et al. 2014). The standard deviation of the ground measurementsis larger than that of the satellite soil moisture product on most of the selected days.This represents the larger spatial variability of the ground measurements of soil moist-ure. This difference in variability occurs because the ground measurements of soilmoisture represent the soil moisture content at the point locations of the monitoringsites, whereas the satellite soil moisture product represents the average soil moisturecontent in the area. The remote-sensing derivation of the soil moisture leads to datasmoothing. Moreover, the standard deviation of the spectral downscaled soil moisturedata is higher than that of the original FY3B soil moisture product. This result shows thatthe spatial variations in spectral downscaled soil moisture become clearer with theincreased spatial resolution.

In order to further prove the effectiveness of the experimental results, we comparedthe daily precipitation at Naqu precipitation site with FY3B soil moisture product,spectral downscaled soil moisture, and ground-based measurements at L19 validationsite (Figure 7). The results show the soil moisture variations with the precipitation data.The soil moisture increased after the precipitation events. The soil moister alsodecreased before next precipitation event. Overall, the downscaled results were morerelated to the precipitation. This phenomenon indicated that our method improved themonitoring accuracy compare with the originated FY3B soil moister products.

The precipitation event would affect the soil moisture content in the following days.We compared the precipitation data with the FY3B soil moisture product, spectraldownscaled soil moisture, and ground measurements. The accumulative values of theprecipitation were assessed in the comparisons. The accumulative 10 days precipitationfitted best with the soil moisture data including FY3B, spectral downscaled, and groundmeasurements. As shown in Figure 8, the fitted curves show that the soil moisture datawere highly correlated to the accumulative precipitation data. Meanwhile, the down-scaled moister data were more correlated to the precipitation data than originated FY3Bdata. These results further proved that our methods could be applied to generate thehigh spatial resolution soil moister data with high accuracy.

In general, the satellite soil moisture products could well reflect the temporaldynamics of the soil moisture content on the east-central of the Tibetan Plateau. The

Figure 7. FY3B soil moisture product, spectral downscaled soil moisture, and ground-based mea-surements at L19 validation site of soil moisture in 15 experimental days were compared with thedaily precipitation recorded at Naqu precipitation site.

INTERNATIONAL JOURNAL OF REMOTE SENSING 63

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

performances of spectral downscaling were much better (R2, RMSE) than the FY3B soilmoisture products. The satellite-derived soil moister could be applied to explain thewater cycle in the terrestrial environment, such as precipitation in our study regions.

5. Summary and conclusions

We developed a spectral downscaling method for the soil moisture data. We analyse thespectral characteristics of soil moisture images to prove our assumption that spectral char-acterizes of soil moister had different scales. The spectral analysis reveals that the power lawdescribes the relationship between the PSD and the spatial frequency well in multiple spatialresolutions. The results of the spectral analysis are consistentwith the assumption for verifyingthe theoretical basis of a spectral downscaling method for soil moisture image.

The key idea for obtaining an unresolved high spatial resolution soil moisture map byusing the spectral downscaling method is to obtain its amplitude and phase informationin the frequency domain. To get this information, we first preserved the amplitudeinformation of the low spatial resolution soil moisture map and used it to represent theamplitude information of the unresolved finer spatial resolution soil moisture map. Then,we used the GWR method to downscale the FY3B soil moisture product to the desiredspatial resolution for spectral downscaling.

After applying the spectral downscaling to the FY3B soil moisture products, theresults are as follows. (1) Spectral downscaling performs better than the FY3B soilmoisture product in coefficient of determination and RMSE. (2) The spectral downscaledresults have more clearly and detailed spatial variations than the original data, due to itshigh spatial resolutions. (3) The satellite soil moisture data could be applied to explain

Figure 8. Comparison between the FY3B soil moisture products, spectral downscaled soil moisturedata, and ground measurements at L19 validation site of soil moisture with the accumulative10 days precipitation at Naqu precipitation site.

64 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

the water exchange between the land and atmosphere through the comparisons withthe precipitation records.

The spectral downscaling method is user-friendly with the consideration of thephysical mechanisms in the process of downscaling. The spectral downscaling approachused in this study is an efficient and effective method to improve the spatial resolutionof current microwave soil moisture images.

Acknowledgement

The authors would like to thank Dr M. Montopoli for explaining the concepts of PSD and thetheories of Fourier transformation to us.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC): [GrantNumber 41401426].

References

Bolten, J. D., W. T. Crow, X. Zhan, T. J. Jackson, and C. A. Reynolds. 2010. “Evaluating the Utility ofRemotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring.” IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing 3: 57–66.doi:10.1109/JSTARS.2009.2037163.

Carlson, T. 2007. “An Overview of the “Triangle Method” for Estimating Surface Evapotranspirationand Soil Moisture from Satellite Imagery.” Sensors 7: 1612–1629. doi:10.3390/s7081612.

Chauhan, N. S., S. Miller, and P. Ardanuy. 2003. “Spaceborne Soil Moisture Estimation at HighResolution: A microwave-optical/IR Synergistic Approach.” International Journal of RemoteSensing 24: 4599–4622. doi:10.1080/0143116031000156837.

Choi, M., and Y. Hur. 2012. “A Microwave-Optical/Infrared Disaggregation for Improving SpatialRepresentation of Soil Moisture Using AMSR-E and MODIS Products.” Remote Sensing ofEnvironment 124: 259–269. doi:10.1016/j.rse.2012.05.009.

Cui, Y., D. Long, Y. Hong, Z. Han, C. Zeng, and X. Hou. 2016a. “Evaluation of the FY-3B/MWRI SoilMoisture Product on the Central Tibetan Plateau.” Proceedings of the IEEE 2016: 1655–1658.doi:10.1109/IGARSS.2016.7729423.

Cui, Y., D. Long, Y. Hong, C. Zeng, J. Zhou, Z. Han, R. Liu, and W. Wan. 2016b. “Validation andReconstruction of FY-3B/MWRI Soil Moisture Using an Artificial Neural Network Based onReconstructed MODIS Optical Products over the Tibetan Plateau.” Journal of Hydrology 543:242–254. doi:10.1016/j.jhydrol.2016.10.005.

Dobriyal, P., A. Qureshi, R. Badola, and S. A. Hussain. 2012. “A Review of the Methods Available forEstimating Soil Moisture and Its Implications for Water Resource Management.” Journal ofHydrology 458: 110–117. doi:10.1016/j.jhydrol.2012.06.021.

Im, J., S. Park, J. Rhee, J. Baik, and M. Choi. 2016. “Downscaling of AMSR-E Soil Moisture with MODISProducts Using Machine Learning Approaches.” Environmental Earth Sciences 75: 1120–1139.doi:10.1007/s12665-016-5917-6.

INTERNATIONAL JOURNAL OF REMOTE SENSING 65

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

Kim, J., and T. S. Hogue. 2012. “Improving Spatial Soil Moisture Representation though Integrationof AMSR-E and MODIS Products.” IEEE Transactions on Geoscience and Remote Sensing 50: 446–460. doi:10.1109/TGRS.2011.2161318.

Ko, A., G. Mascaro, and E. R. Vivoni. 2016. “Irrigation Impacts on Scaling Properties of Soil Moistureand the Calibration of a Multifractal Downscaling Model.” IEEE Transactions on Geoscience andRemote Sensing 54: 3128–3142. doi:10.1109/TGRS.2015.2511628.

Lu, H., T. Koike, K. Yang, Z. Y. Hu, X. D. Xu, M. Rasmy, D. Kuria, and K. Tamagawa. 2012. “ImprovingLand Surface Soil Moisture and Energy Flux Simulations over the Tibetan Plateau by theAssimilation of the Microwave Remote Sensing Data and the GCM Output into a LandSurface Model.” International Journal of Applied Earth Observation and Geoinformation 17: 43–54. doi:10.1016/j.jag.2011.09.006.

Malbéteau, Y., O. Merlin, B. Molero, C. Rüdiger, and S. Bacon. 2016. “DisPATCh as a Tool to EvaluateCoarse-Scale Remotely Sensed Soil Moisture Using Localized in Situ Measurements: Applicationto SMOS and AMSR-E Data in Southeastern Australia.” International Journal of Applied EarthObservation and Geoinformation 45: 221–234. doi:10.1016/j.jag.2015.10.002.

Merlin, O., A. G. Chehbouni, Y. H. Kerr, E. G. Njoku, and D. Entekhabi. 2005. “A Combined Modelingand Multispectral/Multiresolution Remote Sensing Approach for Disaggregation of Surface SoilMoisture: Application to SMOS Configuration.” IEEE Transactions on Geoscience and RemoteSensing 43: 2036–2050. doi:10.1109/TGRS.2005.853192.

Merlin, O., M. J. Escorihuela, M. A. Mayoral, O. Hagolle, A. A. Bitar, and Y. Kerr. 2013. “Self-CalibratedEvaporation-Based Disaggregation of SMOS Soil Moisture: An Evaluation Study at 3 Km and 100M Resolution in Catalunya, Spain.” Remote Sensing of Environment 130: 25–38. doi:10.1016/j.se.2008.06.012.

Merlin, O., J. P. Walker, A. Chehbouni, and Y. Kerr. 2008. “Towards Deterministic Downscaling ofSMOS Soil Moisture Using MODIS Derived Soil Evaporative Efficiency.” Remote Sensing ofEnvironment 112: 3935–3946. doi:10.1016/j.se.2008.06.012.

Milly, P. C. D., K. A. Dunne, and A. V. Vecchia. 2005. “Global Pattern of Trends in Streamflow andWater Availability in a Changing Climate.” Nature 438: 347–350. doi:10.1038/nature04312.

Mladenova, I. E., T. J. Jackson, E. Njoku, R. Bindlish, S. Chan, M. H. Cosh, T. R. H. Holmes, et al. 2014.“Remote Monitoring of Soil Moisture Using Passive Microwave-Based techniques—TheoreticalBasis and Overview of Selected Algorithms for AMSR-E.” Remote Sensing of Environment 144:197–213. doi:10.1016/j.rse.2014.01.013.

Montopoli, M., N. Pierdicca, and F. S. Marzano. 2012. “Spectral Downscaling of Integrated WaterVapor Fields from Satellite Infrared Observations.” IEEE Transactions on Geoscience and RemoteSensing 50: 415–428. doi:10.1109/TGRS.2011.2161996.

Njoku, E. G., T. J. Jackson, V. Lakshmi, T. K. Chan, and S. V. Nghiem. 2003. “Soil Moisture Retrievalfrom AMSR-E.” IEEE Transactions on Geoscience and Remote Sensing 41: 215–229. doi:10.1109/TGRS.2002.808243.

Parinussa, R. M., G. Wang, T. R. H. Holmes, Y. Y. Liu, A. J. Dolman, R. A. M. De Jeu, T. Jiang, P. Zhang,and J. Shi. 2014. “Global Surface Soil Moisture from the Microwave Radiation Imager Onboardthe Fengyun-3B Satellite.” International Journal of Remote Sensing 35: 7007–7029. doi:10.1080/01431161.2014.960622.

Piles, M., A. Camps, M. Vall-Llossera, I. Corbella, R. Panciera, C. Rudiger, Y. H. Kerr, and J. Walker.2011. “Downscaling SMOS-derived Soil Moisture Using MODIS Visible/Infrared Data.” IEEETransactions on Geoscience and Remote Sensing 49: 3156–3166. doi:10.1109/TGRS.2011.2120615.

Ray, R. L., J. M. Jacobs, and M. H. Cosh. 2010. “Landslide Susceptibility Mapping Using DownscaledAMSR-E Soil Moisture: A Case Study from Cleveland Corral, California, US.” Remote Sensing ofEnvironment 114: 2624–2636. doi:10.1016/j.rse.2010.05.033.

Rebora, N., L. Ferraris, J. Von Hardenberg, and A. Provenzale. 2006. “RainFARM: RainfallDownscaling by a Filtered Autoregressive Model.” Journal of Hydrometeorology 7: 724–738.doi:10.1175/JHM517.1.

Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J.Teuling. 2010. “Investigating Soil Moisture-Climate Interactions in a Changing Climate: AReview.” Earth-Science Review 99: 125–161. doi:10.1016/j.earscirev.2010.02.004.

66 A. ZHONG ET AL.

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017

Song, C., and L. Jia. 2016. “A Method for Downscaling FengYun-3B Soil Moisture Based onApparent Thermal Inertia.” Remote Sensing 8: 703. doi:10.3390/rs8090703.

Song, C., L. Jia, and M. Menenti. 2014. “Retrieving High-Resolution Surface Soil Moisture byDownscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data.” IEEE Journalof Selected Topics in Applied Earth Observations and Remote Sensing 7: 935–942. doi:10.1109/JSTARS.2013.2272053.

Wang, A. Q., C. Xie, P. Liu, and L. L. Xu. 2015. “Unmixing Low-Resolution Soil Moisture UsingSpectrum Downscaling and Evapotranspiration.” Proceedings of the IEEE 2015: 1976–1979.doi:10.1109/IGARSS.2015.7326184.

Wang, J., Z. Ling, Y. Wang, and H. Zeng. 2016. “Improving Spatial Representation of Soil Moistureby Integration of Microwave Observations and the Temperature–Vegetation–Drought IndexDerived from MODIS Products.” ISPRS Journal of Photogrammetry and Remote Sensing 113:144–154. doi:10.1016/j.isprsjprs.2016.01.009.

Yu, G., L. Di, and W. Yang. 2008. “Downscaling of Global Soil Moisture Using Auxiliary Data.”Proceeding of the IEEE 3: 230–233. doi:10.1109/IGARSS.2008.4779325.

Zhao, W., and A. Li. 2015. “A Comparison Study on Empirical Microwave Soil Moisture DownscalingMethods Based on the Integration of Microwave-optical/IR Data on the Tibetan Plateau.”International Journal of Remote Sensing 36: 4986–5002. doi:10.1080/01431161.2015.1041178.

INTERNATIONAL JOURNAL OF REMOTE SENSING 67

Dow

nloa

ded

by [

Cap

ital N

orm

al U

nive

rsity

] at

01:

07 2

0 O

ctob

er 2

017