Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation

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1 20 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No.5 Geeratig high spatiotemporal resolutio LAI based o MODIS/GF- data ad combied Krigig-Cressma iterpolatio Liu Zhehua,2,3,4,5, Huag Ruge,2,3,4,5,6, Hu Yuemig,2,3,4,5*, Fa Shudi,2,3,4,5, Feg Peihua,2,3,4,5 (. College of Natural Resources ad Eviromet, South Chia Agricultural Uiversity, Guagzhou 50642, Chia; 2. Guagdog Provicial Key Laboratory of Lad Use ad Cosolidatio, Guagzhou 50642, Chia; 3. Guagdog Provice Egieerig Research Ceter for Lad Iformatio Techology, Guagzhou 50642, Chia; 4. Key Laboratory of the Miistry of Lad ad Resources for Costructio Lad Trasformatio, Guagzhou 50642, Chia; 5. Guagzhou Surveyig ad Mappig Geographic Iformatio Idustry Egieerig Techology Research Ceter, Guagzhou 50642, Chia; 6. Pearl River Comprehesive Techology Ceter (Iformatio Ceter) of Pearl Water Resources Commissio, Guagzhou, 506, Chia) Abstract: Geeratio of high spatial ad temporal resolutio LAI (leaf area idex) products is challegig because higher spatial resolutio remotely sesed data usually have coarse temporal resolutios ad vice versa. I this study, a ovel method that combiig Krigig iterpolatio ad Cressma iterpolatio was proposed to geerate high spatial ad temporal resolutio LAI products by fusig Moderate Resolutio Imagig SpectroRadiometer (MODIS) characterized by coarse spatial resolutio ad high temporal resolutio ad Gaofe- (GF-) with fie spatial resolutio ad coarse temporal resolutio. This method was applied to the Huagpu district of Guagzhou, Guagdog, Chia. The results showed that compared to field observatio, the predicted values of LAI had a acceptable accuracy of 73.2%. Usig Mora s I idex ad Kolmogorov-Smirov tests, it was foud that the MODIS data were spatially auto-correlated ad characterized by ormal distributios. Scalig dow the km km spatial resolutio MODIS products to a spatial resolutio of 30 m 30 m usig poit-krigig resulted i a precisio of 79.38% compared to the results at the same spatial resolutio derived from a 8 m 8 m spatial resolutio GF- image by scalig up usig block-krigig. Moreover, the regressio models that accouts for the relatioship betwee NDVI (Normalized Differece Vegetatio Idex) ad LAI based o MODIS data obtaied the determiatio coefficiets ragig from to Fially, the data fusio ad iterpolatio of MODIS ad GF- data usig Cressma method geerated high spatial ad temporal resolutio LAI maps, which showed reasoably spatial ad temporal variability. The results imply that the proposed method is a powerful tool to create high spatial ad temporal resolutio LAI products. Keywords: data fusio, MODIS, GF-, LAI, spatiotemporal resolutio, spatial iterpolatio, remote sesig DOI: /j.ijabe Citatio: Liu Z H, Huag R G, Hu Y M, Fa S D, Feg P H. Geeratig high spatiotemporal resolutio LAI based o MODIS/GF- data ad combied Krigig-Cressma iterpolatio. It J Agric & Biol Eg, 206; 9(5): Itroductio LAI (leaf area idex) is oe of the extremely importat Received date: Accepted date: Biographies: Liu Zhehua, Associate Professor, research iterests: quatitative remote sesig, greemoutai@63.com; Huag Ruge, Master, research iterests: quatitative remote sesig, oscarge@63.com; Fa Shudi, Master, research iterests: lad use egieerig, fsd_990@26.com; Feg Peihua, Master, research iterests: quatitative remote sesig, @qq.com. *Correspodig author: Hu Yuemig, Professor, College of Natural Resources ad Eviromet, Guagzhou 50642, Chia. Tel: , ymhu@63.com. parameters i process-based models that idirectly quatify the material ad eergy exchage of lad surface with plat boudary layer [,2]. Accurate measuremet of LAI is critical for assessig vegetatio growth ad carbo sequestratio that maitais ecological balace. As a traditioal method, field poit measuremet caot practically satisfy the diverse eeds for retrievig LAI at various spatial ad temporal scales. Moreover, the field measuremet based method is labor itesive ad expesive. I cotrast, remote sesig techiques are used to rapidly moitor the dyamics of LAI at regioal scales because of their spatially explicit measuremets

2 September, 206 Liu Z H, et al. Retrievig for high spatiotemporal resolutio LAI Vol. 9 No.5 2 ad almost real-time dyamics moitorig as well as a relatively low cost. Global LAI products have bee provided by usig satellite observatios o a routie basis, such as Medium Resolutio Imagig Spectrometer (MERIS), Multi-agle Imagig SpectroRadiometer (MISR), Polarizatio ad Directioality of Earth Reflectace (POLDER) ad widely used Moderate Resolutio Imagig SpectroRadiometer (MODIS). Most of these products have medium or coarse spatial resolutios ad limited estimatio precisio of crops ad vegetatio parameters such as vegetatio structure due to the existece of mixed pixels. Though high spatial resolutio images ca be acquired ad utilized to reduce the effects of mixed pixels, it is ofte foud that such data do ot fully meet the requiremets of derivig fie spatial resolutio LAI products because of their coarse temporal resolutios compared to the moderate spatial resolutio products such as MODIS LAI. O the other had, such high spatial resolutio LAI products ca also be obtaied by scalig dow moderate spatial resolutio MODIS LAI data ad the combiig them with the iformatio provided by fie spatial resolutio images such as those from Gaofe-(GF-) satellite with a spatial resolutio of 8 m. So far, various dowscalig algorithms have bee developed ad ca be grouped ito pixel-based methods ad object-orieted methods [3-6]. The object-orieted methods are the procedures of texture extractio ad image segmetatio, ad are complicated. Relatively, the pixel-based methods are simple ad they itegrate the image iformatio based o a widow of pixels. The implemetatio ca be achieved usig commoly used algorithms such as local average, ceter poit, earest eighbor, biliear iterpolatio ad cubic covolutio [6,7]. But, these methods rely solely o spectral data of images, ad do ot take ito accout of image texture ad structure, which limit the precisio of the dowscalig trasformatio [8]. I compariso with the aforemetioed methods, Krigig is a spatial iterpolatio or geostatistical method that ca produce predictios of uobserved locatios usig observed values at earby locatios. Krigig weights the observed values for each ukow locatio accordig to their distaces from the ukow locatio ad spatial auto-correlatio of a iterest variable. Krigig estimator is ubiased ad its Krigig Variace is miimized. May studies have idicated that the geostatistical method is promisig to study the spatial structure of cotiuous radom variables [9-6]. The predictios obtaied by Krigig are more accurate tha those by traditioal spatial iterpolatio methods [7]. This study aimed at developig ad accessig a ovel method that could be used to geerate high spatial resolutio LAI products by fusig the relevat MODIS products ad GF- images. I this way, MODIS products should have fie temporal resolutios ad GF- images should have fie spatial resolutio. This method was developed based o the idea of data fusio ad scale trasformatio by combiig Krigig ad Cressma spatial iterpolatio methods. 2 Materials ad methods 2. Study area ad data This study was coducted i the cetral part of Huagpu district suburb, Guagzhou, Guagdog Provice, Chia, ad show with a 8 m 8 m spatial resolutio GF- true color composite image (Figure ). The area is about 930 km 2 with a average altitude of 20 m (ceter logitude 23 5'4"N, latitude 3 26'55"E). The climate is humid subtropical mosoo climate, characterized by warm witers, hot summers, little frost ad sow, sufficiet rai ad sushie with a aual average temperature of 22ºC. The mothly average precipitatio is mm. The mai vegetatios i the area are subtropical evergree broadleaf forests ad commo crops. Figure The 8 m spatial resolutio GF- true color composite image of the study area ad the spatial distributio of 50 sample plots

3 22 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No.5 A 8 m spatial resolutio GF- image with three visible bads ( μm, μm, μm) ad oe ear-ifrared bad ( μm) was acquired o October, 203. The GF- image does ot cotai LAI data, but the image bads ca be used to calculate NDVI. GF- is the first oe of high-resolutio optical earth observatio satellites series of Chia Natioal Space Admiistratio (CNSA) ad provides data for service ad decisio makig of lad plaig ad maagemet ad agricultural disaster moitorig, resource ad evirometal safety ad so o. Geometric correctio of GF- was coducted usig a total of 20 groud cotrol poits ad least-squares trasformatio with a root mea square error (RMSE) of less tha 0.5 pixel. The km spatial resolutio MODIS LAI product (MOD5A2) ad reflectace product (MOD3A2) dated September 30, 203, were also collected. The products from MODIS Terra ad Aqua satellites have a high temporal resolutio ad are a importat data source of LAI ad other parameters of terrestrial ecosystems [8]. To improve the image quality, the MODIS products were geometrically corrected usig MODIS Tool software. Atmospheric correctios of both GF- ad MODIS data were carried out usig FLAASH model [9]. Moreover, the MODIS products were geometrically co-registered to the GF- image usig the coordiates of the corer poits of GF- image. I order to obtai a high spatial resolutio GF- LAI product dated October, 203, we carried out a LAI sychroous observatio experimet whe GF- satellite overpassed the study area. I the experimet, a total of 50 sample plots i farm lads, grass lads ad broadleaf forests were selected to estimate LAI. The sample plots were show with several vegetatio types, icludig rice, cor ad other crops, broadleaf forests such as eucalyptus ad fruit trees. Each of the sample plots had a area of 30 m 30 m. The plat caopy aalyzer (LAI-2000) was selected to collect field observatios of LAI for the vegetatio caopy types at systematically sampled five poits alog a diagoal lie withi each sample plot. 2.2 Methods Before this study, various experimets were coducted to fid a optimal spatial resolutio of LAI product for the study area ad the obtaied results idicated that the optimal spatial resolutio was 30 m. I this study, MODIS LAI products were thus scaled dow from the spatial resolutio of km to the spatial resolutio of 30 m 30 m usig a poit-krigig iterpolatio, while a block-krigig was applied to scale up the GF- data from the spatial resolutio of 8 m 8 m to the spatial resolutio of 30 m. Moreover, regressio models were developed for various vegetatio caopy types to explai the relatioships betwee LAI ad NDVI based o MODIS products ad the utilized to derive GF- LAI data. Fially, the data fusio of MODIS ad GF- products was coducted to geerate high resolutio ad temporally ad spatially cotiuous LAI products usig Cressma iterpolatio Poit-Krigig for scalig dow MODIS LAI data Krigig is a spatial iterpolatio method that ca be used to produce predictios of uobserved locatios usig the observatios results at earby locatios. The predicted values were calculated as the weighted meas of the values from the earby sampled poits. The ubiased estimator for ordiary poit-krigig is [20] : Z 0 = λ Z () i= where, Z, Z 2,..., Z is the observed values of MODIS LAI data; Z 0 deotes the predicted value of LAI for a locatio; λ i (i =, 2,..., ) is the weight for the i th pixel ad ca be derived usig followig equatio: γ L γ λ γ0 M O M M M = (2) γ L γ λ γ 0 L 0 μ where, γ ij (i, j =, 2,..., ) is the semivariace betwee pixels i ad j from semivariogram derived usig MODIS data; μ is Lagragria coefficiet. A empirical semivariogram γ(h) is defied as [2] : N( h) γ ( h) = Z( x ) Z( x + h) 2 Nh ( ) i i 2 [ i i ] (3) i= where, N(h) deotes the umber of data pairs give a directio ad distace h betwee pixels x i ad x i + h; Z(x i ) ad Z(x i + h) are the observatios at locatios x i ad x i + h, respectively.

4 September, 206 Liu Z H, et al. Retrievig for high spatiotemporal resolutio LAI Vol. 9 No.5 23 With Krigig, it is assumed that sample poit data are spatially auto-correlated. The spatial auto-correlatio explais the spatial variability of data. It is also assumed that the data have a ormal distributio. There are several methods that ca be used to test the spatial auto-correlatio of data, icludig Mora s I idex, Geary Ratio, Cliff-Ord statistic ad so o [25]. I this study, Mora s I was used to test the spatial auto-correlatio of MOIDS data. Moreover, Kolmogorov-Smirov test was applied to test the MODIS data for the ormality at a sigificace level of Block-Krigig for scalig up GF- data I cotract with poit-krigig, block-krigig refers to the case where both the predictio ad measuremet supports are blocks rather tha poits [22]. Based o the research of Goovaerts [20], the ubiased estimator for a uobserved block is: Z = λ Z (4) B i i i= where, λ i (i =, 2,..., ) is the weight derived usig the followig equatio: γ L γ λ γx M O M M M = γ L γ λ γ x L 0 μ where, γ x,..., γ x are the withi-block semivariace ad are defied as: γ ix γ ij A x i=, j x (5) = (6) where, A x represets the area of the block. 2.3 Cressma method for retrievig high spatial ad temporal resolutio LAI Cressma iterpolatio is a computatioally fast method, ad it liearly combies the residuals betwee predicted values ad observed values or referece values. The residuals are weighted depedig o the distace betwee predicted ad observed locatios. This method is appropriate for geeratig predictios. It is stable whe the spatial resolutio of image data used is higher tha the model grid resolutio. The predictios of Cressma aalysis ca be calculated accordig to followig equatio [23] : ϕ( r, r ) x ( r ) x ( r ) i j 0 j b j j= a( i) = b( i) + x r x r j= ϕ( r, r ) where, is the umber of image pixels for differet kids of vegetatio; r i ad r j represet the model ad image data grid poits, respectively; x b is the backgroud value cosidered whe the MODIS LAI is iterpolated i this study; x 0 is the observatio data from MODIS LAI whe a GF- LAI value x a is predicted. The weight fuctio φ(r i, r j ) is writte as: 2 2 R d ij ϕ( ri, rj) = max 0, 2 2 (8) R + d ij where, d ij is the distace betwee poits r i ad r j ; R is the ifluece radius. With Cressma method, the ifluece radius R ad the shape of weight fuctio φ(r i, r j ) have to be selected for differet kids of vegetatio. The shape of the weight fuctio determies how the image data ifluece the model. To improve the precisio of Cressma aalysis, a ladscape idex was itroduced to acquire the ifluece radius R i this study based o the followig equatio: Ai Ri = Di ( Fi) (9) π where, represets the mea patch area for a vegetatio type; F i is the fragmet idex; D i is the ladscape domiace idex based o Equatio (0) [24] : i 2 i 2 i i= i j (7) m D = log ( m) + ( p )log p (0) where, m is the total umber of lad cover categories i the study area; p i is the proportio of the grid cells for a lad cover type i the ladscape. The ladscape domiace idex measures the deviatio betwee the ladscape diversity ad the greatest diversity. Because oly oe GF- image was used i this study, LAI i o the ith day for each pixel must be adjusted usig the followig equatio: 46 Wi LAIi = L () 46 where, W i represets the date differece; L is the LAI differece betwee the GF- LAI value o October ad the 34th MODIS LAI. Based o the MODIS data used i

5 24 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No.5 this study, the costat i Equatio () was set up as 46, which equals to the total umber of MODIS images withi a year. 3 Results 3. Spatial dowscalig of MODIS products The results of Mora s I idex from MODIS data are show i Figure 2. The values of Mora s I idex for red bad, ear-ifrared bad, NDVI ad LAI were 0.722, 0.703, ad 0.655, respectively. The values of z-score for red bad, ear-ifrared bad, NDVI ad LAI were 32.69, 3.84, ad 29.65, respectively. These idicated that the MODIS data were spatially clustered or auto-correlated ad there was a probability of less tha % that the spatially clustered patters could be the results of radom chace. a. Mora s I for red bad b. Mora s I for ear-ifrared bad c. Mora s I for NDVI d. Mora s I for LAI Figure 2 Results of Mora s I idex for MODIS data The results of Kolmogorov-Smirov test for the ormality of MODIS data are show i Table. All the probabilities were greater tha 0.05 [26], which implied that the hypotheses of ormal distributio for red bad, ear-ifrared bad ad LAI were acceptable. NDVI had a log ormal distributio. Thus, the MODIS products

6 September, 206 Liu Z H, et al. Retrievig for high spatiotemporal resolutio LAI Vol. 9 No.5 25 could be scaled dow from a coarser spatial resolutio to a fier oe usig Krigig. Table Kolmogorov-Smirov test for MODIS data Normal distributio test Red bad Near-ifrared bad NDVI LAI Probability Distributio ormal ormal Logarithmic ormal ormal Accordig to the spatial resolutio of MODIS LAI product, set h=920 m, the experimetal semivariograms were computed for all directios usig MODIS LAI image ad the fitted usig spherical, expoetial, Gaussia ad void hole effect model. As a example, the results for LAI were show i Figure 3. The calculatios ad model fittigs were also coducted for MODIS red bad, ear-ifrared bad ad NDVI. The part results of the semivariogram model parameters, icludig ugget, sill ad rage, are listed i Table 2. a. Spherical model b. Expoetial model c. Gaussia model d. Void hole effect model Figure 3 Omidirectioal experimetal semivariograms ad their models of MODIS LAI product Table 2 Parameters of semivariogram models fitted to MODIS NDVI ad LAI data Data Model Nugget (C 0 ) Sill (C 0 +C) C 0 /(C 0 +C)/% Rage/km Average of residuals RMSE NDVI LAI Spherical Expoetial Gaussia Void hole effect Spherical Expoetial Gaussia Void hole effect The spatial auto-correlatio of MODIS products was raked accordig to the ugget-to-sill ratio, that is, C 0 /(C 0 +C)) [27]. Whe the ratio<25%, 25%<it<75%, ad it>75%, the variables showed a strog, moderate ad weak spatial auto-correlatio, respectively [25]. Moreover, the smaller the RMSE, the more accurate the model fitted. Thus, the expoetial model could obtai the greatest accuracies for all the MODIS products, expressed as: h ( ) ( 4 ) γ h e red = + (3) h ( ) ( ) γ ir h = + e (4) h ( ) ( 4 ) γ NDVI h = + e (5)

7 26 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No.5 h γ LAI ( h) = ( e ) (6) I Figure 4, the km km spatial resolutio MODIS products were scaled dow to a spatial resolutio of 30 m 30 m usig Krigig based o the semivariograms Equatios (3)-(6) for red bad (Figure 4a vs. 4b), ear-ifrared bad (Figure 4c vs. 4d), NDVI (Figure 4e vs. 4f) ad LAI (Figure 4g vs. 4h). The dowscaled images had the spatial distributios similar to those at the km km spatial resolutio, but they looked more smoothed ad detailed. a. Red bad ( km) b. Red bad (30 m) c. Near-ifrared ( km) d. Near-ifrared (30 m) e. NDVI ( km) f. NDVI (30 m) g. LAI ( km) h. LAI (30 m) Figure 4 Spatial resolutio MODIS products ( km, left) vs. dowscaled spatial resolutio images usig Krigig (30 m, right)

8 September, 206 Liu Z H, et al. Retrievig for high spatiotemporal resolutio LAI Vol. 9 No Accuracy assessmet 3.2. Idirect assessmet Numerous studies have bee reported o the strog liear relatioship betwee NDVI ad LAI [6,28,29]. The precisio of NDVI predictios ca be thus used as a idirect idicator for the precisio of LAI predictio. Block-Krigig was first applied to scale up the 8 m 8 m spatial resolutio GF- NDVI data to a spatial resolutio of 30 m 30 m i Figure 5. I this study, the precisio of NDVI predictios were obtaied by calculatig ad usig the stadard error of predicted NDVI values from the dowscaled MODIS product as a percetage of estimated NDVI from the up-scaled GF- data [30]. I Figure 6, the average precisio was more tha 79.38% with a average error coefficiet of 20.62% for NDVI. Figure 6 Error coefficiet of NDVI calculated as a absolute value of predictio mius referece divided by the referece based o sampled poits Accuracy assessmet of LAI based o field observatios The LAI estimates from the dowscaled MODIS LAI data were validated usig field observatios i Figure 7. A accuracy of 73.2% was obtaied for the predictios of LAI, implyig that usig Krigig, dowscalig MODIS LAI from km km spatial resolutio to 30 m 30 m was appropriate. a. Red bad a. Farmlad b. Near-ifrared bad b. Grasslad c. NDVI Figure 5 Spatial resolutio GF- products (8 m) c. Broadleaf forest Figure 7 Error coefficiet of LAI for differet vegetatio types

9 28 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No Retrieval of high spatial ad temporal resolutio LAI product by fusig GF- ad MODIS data LAI varies with differet vegetatio types ad growth seasos. I order to obtai the relatioship betwee NDVI ad LAI, it is ecessary to carry out a lad use ad lad cover (LULC) classificatio for the study area. I this study, the classificatio was coducted usig a maximum likelihood supervised classificatio ad the GF- image. The obtaied LULC classes icluded farmlad, broadleaf forest, grasslad, built up area, water body, ad bare soil (Figure 8). The LAI values were plotted agaist the NDVI values from MODIS products ad the models that accouted for the relatioships were developed for each of the LULC types i Figure 9. Figure 8 Lad use ad lad cover classificatio map usig supervised classificatio ad GF- image a. Farmlad b. Grasslad c. Broadleaf forest Figure 9 The regressio models of LAI agaist NDVI for differet vegetatio types The results showed that the LAI teded to expoetially icrease with NDVI whe the determiatio coefficiets varyig from to The regressio models ca be used to estimate LAI values for differet vegetatio types based o the GF- image derived NDVI data. The obtaied spatial distributio of LAI is show i Figure 0. Figure 0 The spatial distributio of vegetatio caopy LAI usig the regressio models ad LULC classificatio map based o GF- image derived NDVI data 3.4 High spatial ad temporal resolutio LAI from GF- ad MODIS LAI data usig Cressma method The experimet showed that broadleaf forest had a larger ladscape domiace idex, but relatively smaller ladscape fragmet idex tha the other vegetatio types. Therefore, its ifluece radius was the largest amog three vegetatio types. Whe the LAI was retrieved from GF- LAI with the method used to adapt the broadleaf forest LAI from the iterpolated MODIS LAI product, the effect of adjustmet was icreased with the expadig of ifluece radius i the weightig fuctio. I this study, the calculated ifluece radius of farmlad, grasslad ad broadleaf forest were respectively 5.62 m, 3.44 m ad 2.58 m. The high spatial resolutio LAI images of the study area throughout year 203 were obtaied usig Cressma method. The results ad the dyamic chage iformatio for the MODIS LAI ad the spatial iformatio of GF- high resolutio remote sesig observatios are show i Figure.

10 September, 206 Liu Z H, et al. Retrievig for high spatiotemporal resolutio LAI Vol. 9 No.5 29 Figure High spatial ad temporal resolutio LAI from the GF- ad MODIS LAI data of the study area for year 203 Figure exhibits the differet characteristics of dyamic chages. The smallest LAI values occurred i the late witer ad sprig period (February, March, April ad May), while the largest LAI values appeared i Jauary, September, October, November ad December. The study area is located i the low latitude area characterized by a subtropical mosoo climate. The vegetatio is evergree ad there is o sigificat differece betwee sprig ad autum. Thus, the time series of LAI did ot completely chage with seasos defied by the climatology. Because the study area is domiated by broadleaf forests (more tha 40%), it exhibited much greater effect o the tred of LAI i time series. 4 Coclusios ad discussio I this study, a ovel method that combiig Krigig ad Cressma iterpolatio was proposed to geerate fier spatial ad temporal resolutio LAI products based o MODIS ad GF- images. This method was applied to a suburb area of Huagpu district, Guagzhou, Guagdog Provice of Chia ad led to a acceptable accuracy of 73.2% for the predicted values of LAI compared to its field observatios. This implies that this method is a powerful tool to create fier spatial ad temporal resolutio LAI products. The Mora s I idex was used to test the spatial autocorrelatio of MOIDS products, while Kolmogorov-Smirov was used to test the ormal distributio of MODIS products. The results show that the MODIS data are spatially auto-correlated ad characterized by ormal distributios ad thus meet the assumptios of Krigig. The km km spatial resolutio MODIS products were the scaled dow to a spatial resolutio of 30 m 30 m usig poit-krigig, which geerated a precisio of 79.38% compared to the results at the same spatial resolutio derived from a

11 30 September, 206 It J Agric & Biol Eg Ope Access at Vol. 9 No.5 8 m 8 m spatial resolutio GF- image by scalig up usig block-krigig. The study area was classified ito farmlad, broadleaf forest, grasslad, buildig site, water body ad bare soil usig a GF- image ad a supervised classificatio procedure. For each vegetatio type, a regressio model that accouts for the relatioship betwee NDVI ad LAI based o MODIS data was built ad the used to retrieve a GF- based LAI product. The regressio models had acceptable accuracies with the coefficiets of determiatio ragig from to I this study, whe Cressma method was used to combie MODIS ad GF- data for geeratig fie spatial ad temporal resolutio LAI products, oly oe GF- image was used, which ievitably led to accumulative ucertaities that were ukow. I order to reduce the ucertaities, more GF- images should be used i the further study. Ackowledgmets This research was supported by Sciece ad Techology Program of Guagzhou, Chia (204A ). [Refereces] [] Myei R B, Hoffma S, Kyazikhi Y, Privette J L, Glassy J, Tia Y, et al. Global products of vegetatio leaf area ad fractio absorbed PAR from year oe of MODIS data. Remote Sesig of Eviromet, 2002; 83(): DOI: 0.06/s (02) [2] Veroustraete F, Paty J, Myei R B. Estimatig et ecosystem exchage of carbo usig the ormalized differece vegetatio idex ad a ecosystem model. Remote Sesig of Eviromet, 996; 58(): DOI: 0.06/ (95) [3] Huag H P. Scale issues i object-orieted image aalysis. Beijig: Graduate Uiversity of Chiese Academy of Scieces, (i Chiese with Eglish abstract) [4] Zhou M, Zhag J L. Review o scale trasformatio for remote sesig image ad selectio of optimal spatial resolutio. World Nuclear Geosciece, 20; 28(2): (i Chiese with Eglish abstract) [5] Wag Q L. Study o object-orieted remote sesig image classificatio ad its applicatio-takig urba vegetatio extractio i Futia, Shezhe city for example. Najig: Najig Forestry Uiversity, (i Chiese with Eglish abstract) [6] He M. Lad use iformatio extractio by object-orieted techology based o remote sesig image. Sichua: Southeast Uiversity of Sciece ad Techology, (i Chiese with Eglish abstract) [7] Guo L M. A aalysis o scale trasformatio i remote sesig-based o fractal theory. Shaaxi: Southwest Uiversity, (i Chiese with Eglish abstract) [8] Hay G J, Niema K O. Spatial thresholds, image-objects, ad up scalig: a multi-scale evaluatio. Remote Sesig of Eviromet, 997; 62(): 9. DOI: 0.06/s (97) [9] Atkiso P M, Tate N J. Spatial scale problems ad geostatistical solutios: A review. The Professioal Geographer, 2000; 52(4): DOI: 0./ [0] Crow W T, Ryu D, Famiglietti J S. Upscalig of field-scale soil moisture measuremets usig distributed lad surface modelig. Advaces i Water Resources, 2005; 28(): 4. DOI: 0.06/j.advwatres [] Famiglietti J S, Devereaux J A, Laymo C A, Tsegaye T, Houser P R, Jackso T J, et al. Groud-based ivestigatio of soil moisture variability withi remote sesig footprits durig the Souther Great Plais 997 (SGP97) Hydrology Experimet. Water Resources Research, 999; 35(6): DOI: 0.029/999wr [2] Mohaty B P, Skaggs T H. Spatio-temporal evolutio ad time-stable characteristics of soil moisture withi remote sesig footprits with varyig soil, slope, ad vegetatio. Advaces i Water Resources, 200; 24(9): DOI: 0.06/s (0) [3] Cosh M H, Jackso T J, Bidlish R, Prueger J H. Watershed scale temporal ad spatial stability of soil moisture ad its role i validatig satellite estimates. Remote Sesig of Eviromet, 2004; 92(4): DOI: 0.06/j.rse [4] Li H B, Li Z H, Liu S X. Applicatio of Krigig techique i estimatig soil moisture i Chia. Geographical Research, 200; 22(5): (i Chiese with Eglish abstract) [5] Zhag J G, Che H S, Su Y R, Zhag W, Kog X L. Spatial variability of soil moisture i surface layer i depressed karst regio ad its scale effect. Acta Pedologica Siica, 2008; 45(3): (i Chiese with Eglish abstract) [6] Grayso R B, Wester A W. Towards areal estimatio of soil water cotet from poit measuremets: time ad space stability of mea respose. Joural of Hydrology, 998; 207(): DOI: 0.06/s (98) [7] Woodcock C E, Strahler A H. The factor of scale i remote

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