Estimating Fractional Vegetation Cover From Landsat-7 ETM+

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Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model Xiaoxia Wang, Kun Jia, Shunlin Liang, Fellow, IEEE, Qiangzi Li, Xiangqin Wei, Yunjun Yao, Xiaotong Zhang, and Yixuan Tu

Abstract— Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (∼1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R 2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R 2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale. Index Terms— Crop growth model, dimidiate pixel model, finer spatial resolution satellite data, fractional vegetation cover, Manuscript received March 9, 2017; revised May 22, 2017; accepted May 25, 2017. This work was supported by the National Natural Science Foundation of China under Grant 41671332, Grant 41571422, and Grant 41331173 and in part by the National Key Research and Development Program of China under Grant 2016YFA0600103. (Corresponding author: Kun Jia.) X. Wang, K. Jia, Y. Yao, X. Zhang, and Y. Tu are with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China (e-mail: [email protected]). S. Liang is with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China, and also with the Department of Geographical Sciences, University of Maryland, College Park, MD 20742 USA (e-mail: [email protected]). Q. Li and X. Wei are with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2017.2709803

Global LAnd Surface Satellite (GLASS) fractional vegetation cover (FVC) product, radiative transfer model.

I. I NTRODUCTION RACTIONAL vegetation cover (FVC) is defined as the percentage of the vertical projected area of green vegetation to the total statistical area [1], [2]. It is an important parameter for describing surface vegetation conditions and reflecting ecosystem changes [2], [3]. FVC also plays a vital role in the exchange of carbon, water, and energy at the land surface [4]. Thus, FVC is required for many earth surface process models, climate modeling, and global change studies [2], [5]. Therefore, the estimation of FVC with high quality at the regional and global scales is of great significance. Currently, several FVC products have been generated using remote sensing data, such as Geoland2/BioPar version 1 (GEOV1) [5], Medium-Resolution Imaging Spectrometer [6], Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) [7], POlarization and Directionality of the Earth’s Reflectances [8], Spinning Enhanced Visible and Infrared Imager [9], and Global LAnd Surface Satellite (GLASS) [10], [11] products. The spatial resolutions of these FVC products are coarse ranging from 500 to 6000 m. However, these coarse resolutions are generally far larger than the typical length scales of most landscapes, which limits the applications of these FVC products in many fields [12], [13]. Therefore, finer spatial resolution FVC products are needed to address the applications that more precisely monitor vegetation coverage at a regional scale, and can simulate earth surface processes more precisely to study global change [1], [2]. Therefore, developing a finer spatial resolution FVC estimation method for applications at regional and global scales is of great significance. Three basic FVC estimation methods have been adopted to generate finer spatial resolution FVC using remote sensing data, including empirical methods, pixel unmixing models, and physical model-based methods [2], [3], [5], [10], [14]. The empirical method establishes the statistical relationship between FVC and vegetation indices or the reflectance of specific wavebands [3], [4], [15]. Empirical models can generate good FVC estimation results at a regional scale but are not suitable for application at a large scale because of their high dependence on the specific vegetation types in specific regions.

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TABLE I M AIN D ATA U SED IN T HIS PAPER , I NCLUDING THE L ANDSAT-7 ETM+ R EFLECTANCE D ATA , GLASS FVC D ATA , AND F IELD M EASURED FVC D ATA

Pixel unmixing models estimate FVC at the subpixel level based on the assumption that each pixel is composed of several components, and the proportion of the vegetation component is considered as the FVC of the pixel [3], [4]. Among the various pixel unmixing models, the dimidiate pixel model is the simplest and most widely used model at a regional scale and assumes that pixels only consist of two components: vegetation and nonvegetation [3], [10], [16]. However, the great challenge existing in pixel unmixing models is determining the endmembers of the vegetation and nonvegetation, especially for large scale FVC estimations. Physical model-based methods are based on the inversion of canopy radiative transfer models. These methods can simulate the physical relationships between FVC and vegetation canopy spectral reflectance over a wide range of situations, thus they have good applicability theoretically [10]. However, these methods are complex and direct inversion of radiative transfer models is very difficult. Thus, lookup table (LUT) methods or neural networks are usually used to simplify the inversion process [14]. However, the reflectance data at finer spatial resolution used in FVC estimation are easily affected by atmospheric conditions which can result in the inaccurate FVC estimates. To overcome this problem, the authors previously proposed an FVC estimation method that incorporated crop growth characteristics from a crop growth model into the FVC estimation using coarse resolution remote sensing data [17]. The proposed method combined the crop growth model built from the ground measured FVC data and the radiative transfer model using a dynamic Bayesian network (DBN). The performance of the proposed method indicated that incorporating the crop growth model could effectively improve the FVC estimation accuracy using the radiative transfer model. This provided a feasible strategy for improving finer spatial resolution FVC estimation accuracy. However, the crop growth model was determined from ground measured FVC data in a small region, it is practically impossible to collect sufficient ground measured data to build a crop growth model for estimating FVC at a larger scale. Thus, building a crop growth model would be difficult for finer spatial resolution FVC estimation at a regional scale using this previous method. The current time-series coarse resolution FVC product contains vegetation growth information. Incorporating such information to construct a crop growth model for finer spatial resolution FVC estimation may improve the accuracy. Therefore, a finer spatial resolution FVC estimation method that integrates finer spatial resolution reflectance data, a coarse

Fig. 1. (Left) Location of the study area. (Right) Distribution of the sampling sites.

resolution FVC product based on the coupled radiative transfer model and a statistical crop growth model is proposed in this paper. The proposed method is investigated using the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) reflectance data and the GLASS FVC product, which is a product extended from the GLASS product suite [11]. The performance of the proposed method is evaluated using a direct comparison of the estimated and ground measured FVC data, and an intercomparison with the widely used dimidiate pixel model. II. S TUDY A REA AND DATA A. Study Area The selected study area is part of an oasis in the Heihe River Basin of China, a typical arid region in China (see Fig. 1). The selected study area is located between 38° 50 N and 38° 54 N latitudes, and 100° 20 E and 100° 25 E longitudes. The annual average precipitation is approximately 140 mm and the annual average temperature is approximately 7 °C–10 °C. The study area has been used for farmland, with maize as the dominant crop. The growing season of maize is from May to September. B. Field Measured FVC Data The field FVC data were quantitatively measured at the study area from May to August in 2012 (Table I), the main crop growing season. Thirteen sampling sites (noted as EC1–EC13), 10 m × 10 m in size, were selected based on the preliminary investigation of the study area. The FVC of each sampling site was computed from nine photographs taken from the nadir. Generally, one photograph was taken at the

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center of the sample site, and the other eight photographs were taken at spots distributed on a diagonal line at an even interval [18], [19]. The FVC value of a photograph was extracted using an automatic shadow-resistant algorithm in the Internationale de L’Eclairage L∗a∗b∗ color space SHAdowResistant LABFVC, which was a reliable method through visual interpretation [20]. Finally, the measured FVC data were used to directly evaluate the performance of the proposed method. C. Landsat-7 ETM+ Reflectance Data Landsat-7 ETM+ images (Path 133'CRow 033) from day of year (DOY) 160–272 in 2012 were obtained to estimate FVC at finer spatial resolution. The Landsat-7 ETM+ images contained six reflective bands (30-m resolution), one thermal band and one panchromatic band (15-m resolution) with a time interval of 16 days. The red and near-infrared wavebands of the Landsat-7 ETM+ data were used in this paper. The images had wedge-shaped gaps because of the failure of the scan-line corrector for the ETM+ sensor on board Landsat7 in 2003. The Landsat-7 ETM+ images were preprocessed using radiometric calibration and atmospheric correction in the Landsat Ecosystem Disturbance Adaptive Processing System software. D. GLASS FVC Product The GLASS FVC data from DOY 137 to DOY 273 in 2012, which contained the dynamic vegetation change information were used to build the crop growth model. The GLASS FVC product was generated from MODIS data with spatial and temporal resolutions of 500 m and eight days, respectively [10]. The GLASS FVC product had been validated with satisfactory accuracy and spatial and temporal continuities [10]. The data were reprojected from the sinusoidal projection to Universal Transverse Mercator projection for consistency with the Landsat-7 ETM+ reflectance data. III. M ETHODS A method combining the radiative transfer model and a crop growth model was proposed to estimate FVC with medium spatial resolution in a previous study [17]. The finer spatial resolution FVC estimation method was developed based on the previous method. The process for estimating finer spatial resolution FVC from the GLASS FVC and the Landsat7 ETM+ reflectance data was based on the coupled radiative transfer and crop growth models, as shown in Fig. 2. The time-series GLASS FVC data, which contained the FVC dynamic information, were incorporated into the process for estimating FVC by building the crop growth model. The incorporation of the GLASS FVC data overcame the problem of the lack of sufficient field measured FVC when estimating finer spatial resolution FVC at a larger scale and improved the generality of the proposed method. The PROSAIL model was applied to compute the LUTs, denoting the relationship between FVC and canopy reflectance. The detailed principle for the FVC estimation method, combining the radiative

Fig. 2. Flowchart of the proposed method for estimating finer spatial resolution FVC.

transfer and crop growth models using a DBN, is provided in the previous study [17]. The improvement are described in Sections III-A–III-C. In addition, to generally evaluate the proposed method, the results of the direct comparison with the field measured FVC and the indirect comparison with the dimidiate pixel model are presented and discussed. A. Crop Growth Model The crop growth model describes the crop growth process and offers information on the temporal FVC variations as the complementary information for estimating FVC. The modified Verhulst logistic equation was selected to construct the crop growth model [21] FVC =

d 1+

exp(a ∗ t 2

+ b ∗ t + c)

(1)

where a, b, c, and d are the model coefficients, and t is DOY. In the previous study, the crop growth model was built using the field measured FVC through a fitting software based on the Levenberg–Marquardt (LM) and the Universal Global Optimization algorithms [22]. When estimating finer spatial resolution FVC at a larger scale, the estimation method met the challenge of the lack of sufficient field measured FVC data, which could not satisfy the need in building the crop growth model for finer spatial resolution remote sensing data. The time-series GLASS FVC data contain the FVC dynamic information and thus have the potential to build the crop growth model for finer spatial resolution remote sensing data. In this paper, the dynamic change in a GLASS FVC pixel was regarded as a general change in the ETM+ pixels of 500 m × 500 m. Each GLASS FVC pixel corresponded to approximately 15 ×15 ETM+ pixels, which meant the 15 × 15 ETM+ pixels were considered as having a similar crop growth model. Moreover, the LM algorithm and simulated annealing method were combined to automatically build reliable crop growth models for each pixel of Landsat-7 ETM+ data based on the time-series GLASS FVC data [23]. The LM algorithm, a combination of the gradient descent and Gauss–Newton methods, is a fine method for fitting nonlinear models and is thus widely used for nonlinear fitting [22]. The simulated annealing method is a global optimization technique based



TABLE II I NPUT VARIABLES FOR THE P ROSAIL M ODEL

on the Monte Carlo iteration and has many applications [24]. The combination of the two methods could utilize their advantages to build a satisfactory crop growth model for the Landsat-7 ETM+ data. The simulated annealing method was used to automatically provide suitable initial values of the model coefficients. Then, based on the suitable initial values, the LM algorithm obtained optimal values of the model coefficients. The coefficient of determination (R 2 ) and the root mean squared error (RMSE) were chosen to evaluate the fitting results. B. PROSAIL Model The radiative transfer model PROSAIL was adopted to simulate the canopy reflectance due to its simplicity, accuracy, and availability [25]. The PROSAIL model is a combination of the popular leaf optical properties model PROSPECT [26] and canopy reflectance model Scattering by Arbitrary Inclined Leaves (SAIL) [27], [28]. The PROSPECT-5 model can simulate the leaf hemispherical reflectance and transmittance using the leaf chlorophyll a + b concentration (Cab ), dry matter content (Cm ), water content (Cw ), carotenoid content (Car ), brown pigment content (Cbrown ), and leaf structure parameter (N) as input parameters. The SAIL model, which assumes the plant canopy is a turbid medium, was run to simulate the bidirectional reflectance factor of plant canopies. The canopy structure was characterized using the leaf area index (LAI), average leaf angle inclination (ALA), assuming an ellipsoidal distribution, and hot-spot parameter (Hot) [7]. To compute FVC under the assumption of a turbid medium, the classical gap fraction relationship with LAI and ALA was used, and thus the initial input variable FVC defined from nadir was converted to LAI for input to the SAIL model. The classical gap fraction relationship was expressed as G(θ, LIDF) ∗ LAI (2) Po(θ ) = exp − cos(θ ) FVC = 1 − Po(0°) (3) where Po(θ ) is the gap fraction in the direction θ . G(θ , LIDF) is the projection function, representing the projected area in the direction θ of a unit LAI. At the nadir, θ is 0; LIDF is the P(refT |FVCT ) P(FVC T |RefT ) =

FVCT −1

FVCT

leaf inclination distribution function, which is characterized by the ellipsoidal distribution and ALA. The input variables for the SAIL model consisted of leaf reflectance, leaf transmittance, FVC, soil reflectance (SR), ALA, solar zenith angle, viewing zenith angle, Hot, and relative azimuth angle. The leaf reflectance and leaf transmittance were provided by the PROSPECT model. The input variables for the PROSAIL model (Table II) were fixed or given reasonable ranges based on previous studies, such as the Leaf Optical Properties Experiment 93 database and algorithm for producing CYCLOPES FVC [7], [29], [30]. The SR was calculated from the PRICE soil-reflectance model, which simulated soil spectra using four high spectral resolution basis vectors [31]. The solar and observation geometries were determined by referring to the Landsat-7 ETM+ image information. C. Retrieving Finer Spatial Resolution FVC The proposed method for finer spatial resolution FVC estimation was based on the coupled radiative transfer model and crop growth model using a DBN [17]. The core of the proposed method was using the crop growth model, LUT established through the PROSAIL model, and the Landsat7 ETM+ reflectance data to obtain the posterior probability of the FVC based on (4), shown at the bottom of the page, where FVCT is the FVC estimate at time T, RefT = (ref1 , ref2 , ref3 , . . . , refT−1 , refT ) are the time-series Landsat-7 ETM+ reflectance data; and refT is the Landsat-7 ETM+ reflectance at the time T . The probability P(refT |FVCT ) denotes the conditional relationship between reflectance and FVC, which is obtained from the Landsat-7 ETM+ reflectance data and LUT established through the PROSAIL model. The state transition probability P(FVC| FVCT−1 ) is obtained from the crop growth model which is built using the time-series GLASS FVC data. The state estimation of FVC at the time T P(FVCT |RefT ) is obtained through integrating the probability P(refT |FVCT ), P(FVC| FVCT−1 ), and the state estimation of FVC at time T − 1. The optimal FVC value at the current time is computed through the minimum mean square error estimation method. More detailed information of the FVC estimation method, which combines the radiative transfer and P(FVC T |FVCT −1 )P(FVCT −1 |RefT −1 )

P(refT |FVCT )P(FVC T |RefT −1 )

(4)


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crop growth models using a DBN, is provided in the previous study [17]. D. Retrieving Finer Spatial Resolution FVC Based on the Dimidiate Pixel Model Intercomparison with an existing FVC estimation method is a suitable way to confirm the reliability of the proposed method. The dimidiate pixel model, which has been widely used to estimate FVC at regional scales [3], [16], was chosen as the reference method to retrieve FVC to further test the performance of the proposed method. FVC estimation using normalized difference vegetation index (NDVI) as the independent variable based on the dimidiate pixel model is expressed in NDVI − NDVIs (5) FVC = NDVIv − NDVIs where NDVI is the normalized difference vegetation index value of the mixed pixel, and NDVIv and NDVIs are the NDVI values of the “pure” vegetation and bare soil pixels, respectively. To retrieve FVC using the dimidiate pixel model, NDVIv and NDVIs should be determined first. Theoretically, the two parameters should not vary with time and space, but they vary with time and space under the influence of many factors, such as vegetation type, soil type, and soil moisture. Therefore, based on the assumption that the “pure” vegetation pixel and bare soil pixel can be found in space and time, the two parameters are determined using the statistical analysis of spatial and temporal NDVI [10]. In this paper, NDVIs and NDVIv were determined as the lower and upper 5% NDVI for the study area using the statistical analysis method. IV. R ESULTS The finer spatial resolution FVC estimates in the study area were generated from the Landsat-7 ETM+ and GLASS FVC data using the proposed method. Fig. 3 shows the finer spatial resolution FVC images at DOY 192 and DOY 240 and reveals the spatial distribution of FVC and change in FVC from DOY 192 to DOY 240. The information revealed from the images proves to be consistent with the actual situations of land cover type distributions through visual observation. This result preliminarily indicates the reliability of the proposed method. To evaluate the agreement between the trend in the FVC estimated from the proposed method in time series and actual crop growth characteristics, one pixel was randomly selected to compare the FVC estimates with the field measured FVC (see Fig. 4). The interpolated FVC estimates were obtained from the estimated FVC using the cubic spline interpolation method for consistency with the dates of the field measured FVC. The field measured FVC data in time series show a curve reflecting the crop growth characteristics in the growing season. However, there are certain fluctuations in the crop growth season, which may have been caused by the influence of human factors during field measurement. The fluctuation is in a reasonable range for the field measured FVC as the reference FVC. The interpolated FVC estimates show a clearly

Fig. 3. Finer spatial resolution FVC estimates at DOY 192 and DOY 240 generated from the proposed method.

Fig. 4. Comparison between the field measured FVC in time series, interpolated FVC estimates, and FVC estimated from the proposed method at one randomly selected pixel.

smooth curve in accordance with the trend shown in the field measured FVC in time series, which is consistent with the crop growth characteristics. These results indicate that the FVC estimates from the proposed method are reasonable. To quantitatively evaluate the performance of the proposed method for finer spatial resolution FVC estimation, 161 field measured FVC values from 13 sample sites at approximately 12 times were used to directly validate the FVC estimation accuracy. Due to the time inconsistency between the estimated and field measured FVC, the estimated FVC was interpolated through the cubic spline interpolation method to obtain the FVC values at given times corresponding to those of the field measurements. The results of the comparison between the interpolated FVC estimates from the proposed method and all field measured FVC are shown in Fig. 5(a). All points in Fig. 5(a) are distributed around the reference line y = x, and no systematic bias is observed. The R 2 and RMSE are 0.6942 and 0.0884, respectively. The result indicates that the proposed method is efficient and reliable for estimating FVC at finer spatial resolution. In addition, to further assess the performance of the proposed method, the performance of the dimidiate pixel model was compared with that of the proposed method [Fig. 5(b)]. The NDVIs and NDVIv values for the dimidiate pixel model are 0.0678 and 0.9411, respectively. The FVC estimates from


Fig. 5.


Comparisons of the field measured FVC and the FVC estimated from (a) proposed method and (b) dimidiate pixel model.

the dimidiate pixel model deviate from the line y = x, which indicates that the FVC estimates have a systematic over-estimation. The over-estimation is particularly evident at high FVC. The R 2 between the FVC estimated from the dimidiate pixel model and the field measurements is 0.7034, and the RMSE is 0.1575. Comparing the results from Fig. 5, the FVC estimation results obtained from the proposed method are more consistent with the field measurements than those of the dimidiate pixel model. All the evaluation results indicate that the proposed method can effectively utilize the vegetation growth information from the coarse resolution FVC product and acquire high-quality FVC estimates with finer spatial resolution. V. D ISCUSSION This paper proposed a reliable method for estimating finer spatial resolution FVC from the current coarse resolution FVC product and finer spatial resolution remote sensing reflectance data. The proposed method successfully integrated the vegetation growth information from the time-series GLASS FVC product by building a statistical crop growth model and Landsat-7 ETM+ reflectance data, and achieved a satisfactory FVC estimation result (R 2 = 0.6942, RMSE = 0.0884). The proposed method effectively incorporated the vegetation growth information which weakened the negative influence on FVC estimation from atmospheric conditions, terrain, and shadow. Therefore, the proposed method could be efficient for improving the FVC estimation accuracy with finer spatial resolution and achieve satisfactory performance. Furthermore, the proposed method was automatically operated without any prior knowledge and human interaction. Therefore, it could overcome the problems in determining the parameters in the empirical method and pixel unmixing models for FVC estimation. In addition, the crop growth model has usually been determined using field measured data in previous

studies [17], [29], [32]. However, the field measured FVC data have been always insufficient to build a crop growth model at a large scale. The coarse resolution FVC product in time series contained vegetation growth information and were used to build the crop growth model in the process for estimating finer spatial resolution FVC at a regional scale. The application of the coarse resolution FVC product successfully addressed the problem of building the crop growth model for finer spatial resolution FVC estimation. This success indicates that the proposed method has potential for regional scale finer spatial resolution FVC estimation and may be feasible for estimating finer spatial resolution FVC for other vegetation types. Here, the study area was used for farming. Most of the study area could be regarded as homogeneous landscapes. The general crop growth characteristics of the pixels were nearly similar, and thus, it was suitable to consider 15 × 15 pixel finer spatial resolution data as having a similar crop growth model. However, when referring to heterogeneous landscapes, the existence of exceptional pixels creates errors in the crop growth model and influences the FVC estimation accuracy. Therefore, the key to overcome this problem is making each pixel of the finer spatial resolution reflectance data have its own crop growth model, using the coarse resolution FVC product. Some fusion methods [33], [34] have the potential to serve as reference methods to address this problem, which is the natural extension of this paper for future study. In addition, the FVC estimation accuracy could be affected by the performance of the coarse resolution FVC products, which would determine the uncertainty of the crop growth model. Therefore, choosing a suitable coarse resolution FVC product is a critical part of the proposed method. Two or more FVC products could be integrated to obtain better FVC data and provide more accurate vegetation growth information. In this paper, the proposed method was developed for cropland and achieved satisfactory results. The proposed


method also has the potential to be applied at a large scale with different vegetation types. When estimating finer spatial resolution FVC at a large scale, suitable vegetation growth models for different vegetation types should be explored to provide accurate vegetation growth information. This paper should be investigated in future work. Moreover, the GLASS FVC product and Landsat-7 ETM+ reflectance data were used to evaluate the proposed method. Other coarse resolution FVC products and finer spatial resolution remote sensing data also have the potential to be used for finer spatial resolution FVC estimation using the proposed method, such as the GOEV1 FVC product, Advanced Spaceborne Thermal Emission and Reflection Radiometer data, and Systeme Probatoire d’Observation dela Tarre data. VI. C ONCLUSION A finer spatial resolution FVC estimation method from a coarse resolution FVC product and finer spatial resolution reflectance data, based on the coupled radiative transfer model and a crop growth model, was proposed in this paper. The proposed method was evaluated using the GLASS FVC product for building the statistical crop growth model and Landsat-7 ETM+ reflectance data. The validation results indicated that the proposed method successfully integrated the crop dynamic change information from the coarse resolution FVC product and achieved satisfactory FVC estimates. The proposed method is a practicable and reliable method for finer spatial resolution FVC estimation, and has the potential to be applied at large scale with various vegetation types, which will be the focus of future work. ACKNOWLEDGMENT The authors would like to thank X. Mu from Beijing Normal University for providing the ground reference data for the Heihe region. R EFERENCES [1] X. Jing, W.-Q. Yao, J.-H. Wang, and X.-Y. Song, “A study on the relationship between dynamic change of vegetation coverage and precipitation in Beijing’s mountainous areas during the last 20 years,” Math. Comput. Model., vol. 54, nos. 3–4, pp. 1079–1085, 2011. [2] X. Zhang, C. Liao, J. Li, and Q. Sun, “Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data,” Int. J. Appl. Earth Observat. Geoinf., vol. 21, pp. 506–512, Apr. 2013. [3] G. Jiapaer, X. Chen, and A. Bao, “A comparison of methods for estimating fractional vegetation cover in arid regions,” Agricult. Forest Meteorol., vol. 151, no. 12, pp. 1698–1710, 2011. [4] J. Xiao and A. Moody, “A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA,” Remote Sens. Environ., vol. 98, nos. 2–3, pp. 237–250, 2005. [5] F. Baret et al., “GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production,” Remote Sens. Environ., vol. 137, pp. 299–309, Oct. 2013. [6] F. Baret, K. Pavageau, D. Béal, M. Weiss, B. Berthelot, and P. Regner, “Algorithm theoretical basis document for MERIS top of atmosphere land products (TOA_VEG),” INRA-CSE, Avignon, France, Tech. Rep., 2006. [7] F. Baret et al., “LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm,” Remote Sens. Environ., vol. 110, no. 3, pp. 275–286, 2007.

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Xiaoxia Wang received the B.S. degree in surveying and mapping engineering from Central South University, Changsha, China, in 2014. She is currently pursuing the M.S. degree with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China. Her research interests include the estimation of fractional vegetation cover using remote sensing.

Kun Jia received the B.S. degree in surveying and mapping engineering from Central South University, Changsha, China, in 2006, and the Ph.D. degree in cartography and geographic information system from the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China, in 2011. He is currently an Associate Professor with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing. His research interests include the estimation of fractional vegetation cover, land cover classification, and agriculture monitoring using remote sensing data.

Shunlin Liang (M’94–F’13) received the Ph.D. degree from Boston University, Boston, MA, USA. He is currently a Professor with the Department of Geographical Sciences, University of Maryland, College Park, MD, USA, and with the Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China. His research interests include the estimation of land surface variables from satellite data, earth energy balance, and assessment of environmental changes. He published over 270 SCI indexed peer-reviewed journal papers. He has authored the book Quantitative Remote Sensing of Land Surfaces (Wiley, 2004), co-authored the book Global LAnd Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis (Springer, 2013), edited the book Advances in Land Remote Sensing: System, Modeling, Inversion and Application (Springer, 2008), and co-edited the books Advanced Remote Sensing: Terrestrial Information Extraction and Applications (Academic Press, 2012) and Land Surface Observation, Modeling and Data Assimilation (World Scientific, 2013). Dr. Liang was an Associate Editor of the IEEE T RANSACTION ON G EOSCIENCE AND R EMOTE S ENSING and also a Guest Editor of several remote sensing related journals.

Qiangzi Li received the Ph.D. degree in cartography and geographical information system from the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China, in 2008. He is currently a Professor with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. His research interests include agriculture and ecological monitoring with remote sensing.

Xiangqin Wei received the M.E. degree in detection technology and automatic equipment from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2009, and the Ph.D. degree in cartography and geographic information system from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, in 2017. Since 2009, she has been an Assistant Professor with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, and the Demonstration Center for Spaceborne Remote Sensing, China National Space Administration. Her research interests include spaceborne remote sensing demonstration and the application of quantitative remote sensing.

Yunjun Yao received the Ph.D. degree from Peking University, Beijing, China, in 2010. From 2008 to 2009, he was with the Department of Geographical Sciences, University of Maryland, College Park, MD, USA, as a Joint Ph.D. Student. He is currently with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing. His research interests include the estimation of evapotranspiration and retrieval of surface biophysical parameters by remote sensing.

Xiaotong Zhang received the Ph.D. degree in cartography and geographical information science from Wuhan University, Wuhan, China, in 2010. He was with the Department of Geographical Sciences, University of Maryland, College Park, MD, USA, as a Joint Ph.D. Student. He is currently with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China. His research interests include the estimation of land surface radiation components from satellite data, and earth energy balance.

Yixuan Tu received the B.S. degree in information management and information system from Capital Normal University, Beijing, China, in 2016. She is currently pursuing the M.S. degree with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing. Her research interests include the estimation of fractional vegetation cover using remote sensing.