# Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

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## Abstract

**:**

^{2}= 0.73, root-mean-square error (RMSE) = 30.61 g/m

^{2}), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R

^{2}= 0.77, RMSE = 17.22 g/m

^{2}), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

^{2}(43°02′–44°52′N, 115°13′–117°06′E). The elevation in this area shows a decreasing gradient from the southeast to the northwest. This area is characterized by a typical temperate continental semi-arid climate. Winters are cold and dry, where conditions are influenced by the air flow from the Mongolian plateau, whereas summers are wet and warm, where conditions are influenced by monsoons. This area has an average annual temperature of 0.5–1.0 °C and an annual mean precipitation of 350 mm. Precipitation is often distributed unevenly in the region, and droughts occur frequently [46]. In addition, 90% of this area is dominated by temperate steppe, and the typical growing season is from May to September. Grassland biomass shows a decreasing trend from southeast to northwest that follows the elevation transition in the study area [35,47].

**Figure 1.**The location of Xilinhot. The elevation in the left-bottom corner was generated by using Shuttle Radar Topography Mission (SRTM) data (http://srtm.csi.cgiar.org/). The image on the right is the RGB (red, green, blue) composite of Landsat bands 5, 4, and 3 overlaid with field locations (orange dots) where samples were collected in 2011.

#### 2.2. Field Data

#### 2.3. Remote Sensing Data

**Table 1.**Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) image pairs for generating synthetic Normalized Difference Vegetation Index (NDVI) data during 2005–2013. Years highlighted with bold font lacked Landsat images, and therefore, the Landsat images from adjacent year (1-yr interval) or 2-yr intervals were used.

Year | Landsat TM Path = 124, Row = 29/30 | MODIS (DOY) Tile = h26v04 |
---|---|---|

2005 | 09/02/2005 | 241/2005 |

2006 | 09/21/2006 | 265/2006 |

2007 | 09/08/2007 | 249/2007 |

2008 | 09/08/2007 | 249/2007 |

2009 | 08/12/2009 | 225/2009 |

2010 | 08/31/2010 | 241/2010 |

2011 | 08/02/2011 | 209/2011 |

2012 | 08/02/2011 | 209/2011 |

2013 | 08/02/2011 | 209/2011 |

## 3. Methods

#### 3.1. Procedure for Grassland AGB Estimation

**Figure 2.**Procedures for developing the aboveground biomass (AGB) estimation model. Pre_NDVI represents the synthetic NDVI series.

^{2}), root-mean-square error (RMSE), and relative RMSE (RMSE

_{r}) [52]. The RMSE and RMSE

_{r}were calculated as follows:

#### 3.2. The STARFM Algorithm for NDVI Image Fusion

**Table 2.**Description of the input images used for the validation of the three schemes for data fusion.

Input MODIS ${t}_{k}$ | Input Landsat ${t}_{k}$ | Input MODIS ${t}_{0}$ | Validation Landsat ${t}_{0}$ | |
---|---|---|---|---|

Scheme 1 | 09/06/2007–09/13/2007 DOY 249 | 09/08/2007 | 05/17/2007–05/24/2007 DOY 137 | 05/19/2007 |

Scheme 2 | 09/22/2006–09/29/2006 DOY 265 | 09/21/2006 | 05/17/2007–05/24/2007 DOY 137 | 05/19/2007 |

Scheme 3 | 08/29/2005–09/05/2005 DOY 241 | 09/02/2005 | 05/17/2007–05/24/2007 DOY 137 | 05/19/2007 |

Statistics | Formula ^{a} |
---|---|

Mean value | $\overline{p}={\displaystyle {\displaystyle \sum}_{i=1}^{N}}{\displaystyle {\displaystyle \sum}_{j=1}^{M}}\frac{{p}_{\left(i,j\right)}}{N\times M}$ |

Standard deviation | $\text{\sigma}=\sqrt{\frac{1}{N\times M}{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{\displaystyle {\displaystyle \sum}_{j=1}^{M}}{\left({p}_{\left(i,j\right)}-\overline{p}\right)}^{2}}$ |

Entropy | $\text{H}=-{\displaystyle {\displaystyle \sum}_{j=1}^{M}}{\displaystyle {\displaystyle \sum}_{\text{i}=1}^{N}}{P}_{\left(i\right)}ln{P}_{\left(i\right)}$ |

Average gradient | $\overline{g}=\frac{1}{\left(M-1\right)\left(N-1\right)}\times {\displaystyle {\displaystyle \sum}_{i=1}^{M-1}}{\displaystyle {\displaystyle \sum}_{j=1}^{N-1}}\sqrt{\frac{{\left(p\left(i,j\right)-p\left(i+1,j\right)\right)}^{2}+{\left(p\left(i,j\right)-p\left(i,j+1\right)\right)}^{2}}{2}}$ |

Mean absolute difference | $\overline{D}={\displaystyle {\displaystyle \sum}_{i=1}^{N}}{\displaystyle {\displaystyle \sum}_{j=1}^{M}}\frac{\left|{p}_{\left(i,j\right)}-{q}_{\left(i,j\right)}\right|}{N\times M}$ |

^{a}Here, ${p}_{\left(i,j\right)}$ represents the NDVI values of the ith row and jth column in image P; $N$ represents the number of rows of the images; $M$ is the column of the images; ${P}_{\left(i\right)}$ is the frequency of the pixel whose gray value was i (to calculate the entropy, values for NDVI images in our study were standardized to 0–255); ${q}_{\left(i,j\right)}$ represents the NDVI values of the ith row and jth column in image Q.

#### 3.3. Biomass Estimation Model: Support Vector Machine Algorithm

## 4. Results and Discussion

#### 4.1. Accuracy Assessment of Synthetic NDVI Based on STARFM

^{2}) and lower root-mean-square errors (R

^{2}= 0.72, RMSE = 0.021 for Scheme 1; R

^{2}= 0.77, RMSE = 0.024 for Scheme 2; R

^{2}= 0.67, RMSE = 0.037 for Scheme 3) (Figure 4).

**Figure 4.**Difference maps and scatter plots between the observed Landsat NDVI images (taken 19 May 2007) and the synthetic NDVI images (taken during 17–24 May 2007) predicted by (Left) (

**a**,

**d**) Scheme 1; (Middle) (

**b**,

**e**) Scheme 2, and (Right) (

**c**,

**f**) Scheme 3.

**Table 4.**Accuracy assessment of the fused images. TM_NDVI is the observed TM NDVI image and Pre_NDVI represents the predicted NDVI images at 30-m resolution that were derived from STARFM with Schemes 1, 2, and 3. Numbers in bold represent the best fit among the three schemes.

Type | Mean | Standard Deviation | Entropy | Average Gradient | Mean Absolute Difference |
---|---|---|---|---|---|

TM_NDVI | 0.240 | 0.049 | 3.619 | 0.014 | / |

Pre_NDVI_Scheme1 | 0.244 | 0.053 | 3.653 | 0.013 | 0.019 |

Pre_NDVI_Scheme2 | 0.245 | 0.045 | 3.566 | 0.012 | 0.018 |

Pre_NDVI_Scheme3 | 0.247 | 0.060 | 3.725 | 0.016 | 0.022 |

#### 4.2. Prediction of Time-Series Synthetic NDVI

**Figure 5.**Temporal variations of the regional mean and standard deviation (shaded area) of NDVI for grasslands in the study areas during 2005–2013 (growing season). (

**a**) MODIS NDVI; (

**b**) synthetic NDVI; (

**c**) scatter plots between MODIS NDVI and synthetic NDVI at 8-d intervals; (

**d**) scatter plots between the standard deviation of MODIS NDVI and synthetic NDVI at 8-d intervals; (

**e**) MODIS NDVI

_{max}(black) and synthetic NDVI

_{max}(gray). Error bars represent the standard deviations of MODIS NDVI

_{max}(black) and synthetic NDVI

_{max}(gray).

#### 4.3. Development of the AGB Estimation Model

^{2}, RMSE, and RMSE

_{r}for both the training set and testing set (Table 5 and Table 6). Therefore, we finally chose to integrate the SVM-AGB model and the synthetic NDVI to predict AGB for grasslands in Xilinhot during 2005–2013.

^{2}= 0.77, RMSE = 17.22 g/m

^{2}, RMSE

_{r}= 24.8%) with other AGB estimation models in the same region, e.g., the exponential model based on 250-m MODIS NDVI (R

^{2}= 0.447) by Kawamura et al. [57]; the ANN model based on elevation, Landsat NDVI, and Landsat reflectance data (R

^{2}= 0.82, RMSE = 60.01 g/m

^{2}, RMSE

_{r}= 40.61%) by Xie et al. [35]; and the power function model based on 250-m MODIS NDVI (R

^{2}= 0.568, RMSE = 673.88 kg/ha) by Jin et al. [47]. The studies by Kawamura et al. [57] and Jin et al. [47] mainly used 250-m MODIS NDVI data, and the accuracies of their models were lower than that of our estimation model (R

^{2}= 0.447 by Kawamura et al.; R

^{2}= 0.568 by Jin et al.; R

^{2}= 0.77 by our synthetic NDVI-derived SVM-AGB model). Meanwhile, in the study of Xie et al. [35], although their ANN model was built on Landsat NDVI and reflectance images and possessed higher accuracy, the limitations of sparse temporal data associated with Landsat images restricted their model’s application to the requirements of frequent time series and dynamic monitoring. In general, our synthetic NDVI-derived SVM-AGB estimation model had both higher spatial resolutions (30 m) and temporal resolutions (8 d) than the other models in Table 5 and Table 6 and showed improvements compared to other AGB estimation models generated from a single type of remotely sensed data [33,35,47,56].

**Table 5.**Comparison of the accuracy of different AGB estimation models based on synthetic NDVI data.

AGB Model | Regression Equation | Training Set | Testing Set | ||||
---|---|---|---|---|---|---|---|

R^{2} | RMSE | RMSE_{r} | R^{2} | RMSE | RMSE_{r} | ||

(g/m^{2}) | (g/m^{2}) | ||||||

Linear regression model | $\text{y}=2178*x-1140$ | 0.71 | 31.40 | 42.6% | 0.79 | 26.48 | 34.6% |

Power function model | $\text{y}=1.044*{10}^{5}*{x}^{12.59}$ | 0.68 | 33.62 | 44.6% | 0.84 | 28.03 | 38.0% |

Exponential model | $\text{y}=3.902*{10}^{-4}*{e}^{21.61*x}$ | 0.67 | 34.14 | 45.1% | 0.84 | 28.60 | 38.8% |

SVM-AGB | / | 0.77 | 17.22 | 24.8% | 0.83 | 22.60 | 31.3% |

AGB Model | Regression Equation | Training Set | Testing Set | ||||
---|---|---|---|---|---|---|---|

R^{2} | RMSE | RMSE_{r} | R^{2} | RMSE | RMSE_{r} | ||

(g/m^{2}) | (g/m^{2}) | ||||||

Linear regression model | $\text{y}=2010*x-1043$ | 0.66 | 34.13 | 46.3% | 0.64 | 34.19 | 42.1% |

Power function model | $\text{y}=2.55*{10}^{5}*{x}^{14.15}$ | 0.68 | 33.21 | 44.8% | 0.69 | 31.23 | 40.9% |

Exponential model | $\text{y}=6.771*{10}^{-5}*{e}^{\left(24.71*x\right)}$ | 0.68 | 33.36 | 44.9% | 0.69 | 31.24 | 41.1% |

SVM-AGB | / | 0.73 | 30.61 | 43.0% | 0.72 | 22.89 | 37.1% |

#### 4.4. Drought Condition Monitoring with Time-Series Biomass Maps

^{2}and the regional mean was 16.9 g/m

^{2}. During July, nearly half of the grassland biomass values reached 40–50 g/m

^{2}. Grassland biomass reached its peak values during the end of July and the start of August. In the last week of July (DOY: 209), the highest value reached to 190 g/m

^{2}in the southeast region, while in the northwest region, the biomass was as low as 35 g/m

^{2}. Then, biomass started to decrease during the third week of August. In the first week of September (DOY: 233), the regional mean AGB was 34.0 g/m

^{2}, and it decreased to 26.2 g/m

^{2}during the last week of September.

**Figure 6.**Spatial distribution of biomass in 2007 and 2011 for each 8-d intervals during the growing season.

**Figure 7.**The 8-d intervals of the regional mean biomass estimated based on synthetic NDVI and SVM-AGB model during the growing season (May to September) of 2005–2013 in Xilinhot. Dash line represents the regional mean biomass of 9-yr mean (2005–2013).

^{2}, while in 2011, the biomass values reached up to 150 g/m

^{2}. For the remainder of the growing season, no large differences were detected between the biomass maps in 2007 and 2011.

^{2}, which was 88.9% higher than the 9-yr mean. Jin et al. [47] stated that 2012 was a prime harvest year for all of Xilingol, and the biomass value was 40% higher than the annual average (2005–2013) and twice the value in 2009.

**Figure 8.**Time series of precipitation and predicted synthetic NDVI (8-d intervals) for Xilinhot in 2007 (precipitation data at site Xilinhot were obtained from the China meteorological data sharing service system).

**Figure 9.**The Standardized Anomalies Index (SAI) indicating biomass anomalies in Xilinhot during July 2007. The land cover map in Xilinhot in the left panel was adapted from the land cover data set provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). The unutilized lands represent lands that have not been used (including desert, Gobi region, saline, wetlands, bare soil).

## 5. Conclusions

^{2}= 0.77 and RMSE = 17.22 g/m

^{2}; synthetic imagery: R

^{2}= 0.73 and RMSE = 30.61 g/m

^{2}). Importantly, the spatial-temporal resolution was improved (8 d, 30 m), which indicates that integrating the synthetic NDVI data and SVM model can produce accurate grassland AGB estimates for natural resource applications.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- He, C.; Zhang, Q.; Li, Y.; Li, X.; Shi, P. Zoning grassland protection area using remote sensing and cellular automata modeling—A case study in Xilingol steppe grassland in northern China. J. Arid Environ.
**2005**, 63, 814–826. [Google Scholar] [CrossRef] - Jobbagy, E.G.; Sala, O.E. Controls of grass and shrub aboveground production in the Patagonian steppe. Ecol. Appl.
**2000**, 10, 541–549. [Google Scholar] [CrossRef] - Nordberg, M.L.; Evertson, J. Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data. Ambio
**2003**, 32, 502–509. [Google Scholar] [CrossRef] [PubMed] - Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ.
**2002**, 83, 195–213. [Google Scholar] [CrossRef] - Price, J.C. How unique are spectral signatures. Remote Sens. Environ.
**1994**, 49, 181–186. [Google Scholar] [CrossRef] - Asner, G.P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens.
**2001**, 22, 3855–3862. [Google Scholar] [CrossRef] - Jorgensen, P.V. Determination of cloud coverage over Denmark using Landsat MSS/TM and NOAA–AVHRR. Int. J. Remote Sens.
**2000**, 21, 3363–3368. [Google Scholar] [CrossRef] - Ju, J.C.; Roy, D.P. The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally. Remote Sens. Environ.
**2008**, 112, 1196–1211. [Google Scholar] [CrossRef] - Gur, E.; Zalevsky, Z. Resolution-enhanced remote sensing via multi spectral and spatial data fusion. Int. J. Image Data Fusion
**2011**, 2, 149–165. [Google Scholar] [CrossRef] - Carper, W.J.; Lillesand, T.M.; Kiefer, R.W. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm. Eng. Remote. Sens.
**1990**, 56, 459–467. [Google Scholar] - Shettigara, V.K. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogramm. Eng. Remote. Sens.
**1992**, 58, 561–567. [Google Scholar] - Yocky, D.A. Multiresolution wavelet decomposition image merger of Landsat thematic mapper and SPOT panchromatic data. Photogramm. Eng. Remote. Sens.
**1996**, 62, 1067–1074. [Google Scholar] - Fu, D.J.; Chen, B.Z.; Wang, J.; Zhu, X.L.; Hilker, T. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens.
**2013**, 5, 6346–6360. [Google Scholar] [CrossRef] - Gao, F.; Masek, J.; Schwaller, M.; Hall, F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens.
**2006**, 44, 2207–2218. [Google Scholar] - Zhu, X.L.; Chen, J.; Gao, F.; Chen, X.H.; Masek, J.G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ.
**2010**, 114, 2610–2623. [Google Scholar] [CrossRef] - Hilker, T.; Wulder, M.A.; Coops, N.C.; Seitz, N.; White, J.C.; Gao, F.; Masek, J.G.; Stenhouse, G. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sens. Environ.
**2009**, 113, 1988–1999. [Google Scholar] [CrossRef] - Walker, J.J.; de Beurs, K.M.; Wynne, R.H.; Gao, F. Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sens. Environ.
**2012**, 117, 381–393. [Google Scholar] [CrossRef] - Tian, F.; Wang, Y.J.; Fensholt, R.; Wang, K.; Zhang, L.; Huang, Y. Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau. Remote Sens.
**2013**, 5, 4255–4279. [Google Scholar] [CrossRef] - Schmidt, M.; Udelhoven, T.; Gill, T.; Roder, A. Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian savanna. J. Appl. Remote Sens.
**2012**, 6, 063512. [Google Scholar] - Watts, J.D.; Powell, S.L.; Lawrence, R.L.; Hilker, T. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sens. Environ.
**2011**, 115, 66–75. [Google Scholar] [CrossRef] - Singh, D. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. Int. J. Appl. Earth Obs.
**2011**, 13, 59–69. [Google Scholar] [CrossRef] - Singh, D. Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data. Atmosfera
**2012**, 25, 43–63. [Google Scholar] - Bhandari, S.; Phinn, S.; Gill, T. Preparing Landsat image time series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sens.
**2012**, 4, 1856–1886. [Google Scholar] [CrossRef] - Senf, C.; Leitao, P.J.; Pflugmacher, D.; van der Linden, S.; Hostert, P. Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens. Environ.
**2015**, 156, 527–536. [Google Scholar] [CrossRef] - Huang, B.; Song, H.H. Spatiotemporal reflectance fusion via sparse representation. IEEE Trans. Geosci. Remote Sens.
**2012**, 50, 3707–3716. [Google Scholar] [CrossRef] - Hilker, T.; Wulder, M.A.; Coops, N.C.; Linke, J.; McDermid, G.; Masek, J.G.; Gao, F.; White, J.C. A new data fusion model for high spatial and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ.
**2009**, 113, 1613–1627. [Google Scholar] [CrossRef] - Gevaert, C.M.; García-Haro, F.J. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens. Environ.
**2015**, 156, 34–44. [Google Scholar] [CrossRef] - Shen, H.F.; Wu, P.H.; Liu, Y.L.; Ai, T.H.; Wang, Y.; Liu, X.P. A spatial and temporal reflectance fusion model considering sensor observation differences. Int. J. Remote Sens.
**2013**, 34, 4367–4383. [Google Scholar] [CrossRef] - Meng, J.H.; Du, X.; Wu, B.F. Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. Int. J. Digit. Earth
**2013**, 6, 203–218. [Google Scholar] [CrossRef] - Huang, B.; Zhang, H.K. Spatio-temporal reflectance fusion via unmixing: Accounting for both phenological and land-cover changes. Int. J. Remote Sens.
**2014**, 35, 6213–6233. [Google Scholar] [CrossRef] - Zhang, F.; Zhu, X.L.; Liu, D.S. Blending MODIS and Landsat images for urban flood mapping. Int. J. Remote Sens.
**2014**, 35, 3237–3253. [Google Scholar] [CrossRef] - Emelyanova, I.V.; McVicar, T.R.; van Niel, T.G.; Li, L.T.; van Dijk, A.I.J.M. Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection. Remote Sens. Environ.
**2013**, 133, 193–209. [Google Scholar] [CrossRef] - Gao, T.; Xu, B.; Yang, X.C.; Jin, Y.X.; Ma, H.L.; Li, J.Y.; Yu, H.D. Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011. Int. J. Remote Sens.
**2013**, 34, 7796–7810. [Google Scholar] [CrossRef] - Tomppo, E.; Nilsson, M.; Rosengren, M.; Aalto, P.; Kennedy, P. Simultaneous use of Landsat-TM and IRS-1C WIFS data in estimating large area tree stem volume and aboveground biomass. Remote Sens. Environ.
**2002**, 82, 156–171. [Google Scholar] [CrossRef] - Xie, Y.C.; Sha, Z.Y.; Yu, M.; Bai, Y.F.; Zhang, L. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecol. Model
**2009**, 220, 1810–1818. [Google Scholar] [CrossRef] - Jin, Y.Q.; Liu, C. Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks. Int. J. Remote Sens.
**1997**, 18, 971–979. [Google Scholar] [CrossRef] - Uno, Y.; Prasher, S.O.; Lacroix, R.; Goel, P.K.; Karimi, Y.; Viau, A.; Patel, R.M. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Comput. Electron. Agric.
**2005**, 47, 149–161. [Google Scholar] [CrossRef] - Yool, S.R. Land cover classification in rugged areas using simulated moderate-resolution remote sensor data and an artificial neural network. Int. J. Remote Sens.
**1998**, 19, 85–96. [Google Scholar] [CrossRef] - Ban, Y.F. Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops. Can. J. Remote Sens.
**2003**, 29, 518–526. [Google Scholar] [CrossRef] - Erbek, F.S.; Ozkan, C.; Taberner, M. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int. J. Remote Sens.
**2004**, 25, 1733–1748. [Google Scholar] [CrossRef] - Mathur, P.; Govil, R. Detecting temporal changes in satellite imagery using ANN. In Proceedings of the RAST 2005 2nd International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 9–11 June 2005; pp. 645–647.
- Balabin, R.M.; Lomakina, E.I. Support vector machine regression (SVR/lS-SVM)—An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst
**2011**, 136, 1703–1712. [Google Scholar] [CrossRef] [PubMed] - Chen, G.; Hay, G.J.; Zhou, Y.L. Estimation of forest height, biomass and volume using support vector regression and segmentation from Lidar transects and Quickbird imagery. In Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010; pp. 1–4.
- Gleason, C.J.; Im, J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens. Environ.
**2012**, 125, 80–91. [Google Scholar] [CrossRef] - Jachowski, N.R.A.; Quak, M.S.Y.; Friess, D.A.; Duangnamon, D.; Webb, E.L.; Ziegler, A.D. Mangrove biomass estimation in southwest Thailand using machine learning. Appl. Geogr.
**2013**, 45, 311–321. [Google Scholar] [CrossRef] - Tu, Y. Based on Meteorological Data of Drought Disaster Forecast Study in Pastoral Area of Xilingol League. Master’s Thesis, Inner Mongolia Normal University, Hohhot, China, 2013. [Google Scholar]
- Jin, Y.X.; Yang, X.C.; Qiu, J.J.; Li, J.Y.; Gao, T.; Wu, Q.; Zhao, F.; Ma, H.L.; Yu, H.D.; Xu, B. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, northern china. Remote Sens.
**2014**, 6, 1496–1513. [Google Scholar] [CrossRef] - Guan, L.L.; Liu, L.Y.; Peng, D.L.; Hu, Y.; Jiao, Q.J.; Liu, L.L. Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories. J. Appl. Remote Sens.
**2012**, 6. [Google Scholar] [CrossRef] - Masek, J.G.; Vermote, E.F.; Saleous, N.E.; Wolfe, R.; Hall, F.G.; Huemmrich, K.F.; Gao, F.; Kutler, J.; Lim, T.K. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett.
**2006**, 3, 68–72. [Google Scholar] [CrossRef] - Bai, Y.F.; Han, X.G.; Wu, J.G.; Chen, Z.Z.; Li, L.H. Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature
**2004**, 431, 181–184. [Google Scholar] [CrossRef] [PubMed] - Vermonte, E.F.; Kotchenova, S.Y.; Ray, J.P. MODIS Surface Reflectance User’s Guide, Version 1.4. Available online: http://modis-sr.ltdri.org/guide/MOD09_UserGuide_v1_3.pdf (accessed on 16 November 2015).
- Makela, H.; Pekkarinen, A. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. For. Ecol. Manag.
**2004**, 196, 245–255. [Google Scholar] [CrossRef] - Katz, R.W.; Glantz, M.H. Anatomy of a rainfall index. Mon. Weather Rev.
**1986**, 114, 764–771. [Google Scholar] [CrossRef] - Jarihani, A.A.; McVicar, T.R.; van Niel, T.G.; Emelyanova, I.V.; Callow, J.N.; Johansen, K. Blending Landsat and MODIS data to generate multispectral indices: A comparison of “Index-then-blend”and “Blend-then-index” approaches. Remote Sens.
**2014**, 6, 9213–9238. [Google Scholar] [CrossRef][Green Version] - Hsu, C.W.; Chang, C.C.; Lin, C.J. A Practical Guide to Support Vector Classification. Available online: https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (accessed on 19 November 2015).
- Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens.
**2013**, 5, 1704–1733. [Google Scholar] [CrossRef][Green Version] - Kawamura, K.; Akiyama, T.; Yokota, H.; Tsutsumi, M.; Yasuda, T.; Watanabe, O.; Wang, S.P. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agric. Ecosyst. Environ.
**2005**, 107, 83–93. [Google Scholar] [CrossRef] - Chun, F.; Li, C.L.; Bao, Y.H. The wavelet analysis of average temperature and precipitation in Xilinhot during 57 years. J. Inner Mong. Normal Univ. (Nat. Sci. Ed.)
**2013**, 42, 47–52. (In Chinese) [Google Scholar] - Farmer's Daily. Available online: http://szb.farmer.com.cn/nmrb/html/2011–04/13/nw.D110000nmrb_20110413_1–03.htm?div=-1 (accessed on 4 September 2015).
- Hang, Y.L.; Bao, G.; Bao, Y.H.; Burenjirigala; Altantuya, D. Spatiotemporal changes of vegetation coverage in Xilingol grassland and its responses to climate change during 2000–2010. Acta Agres. Sin.
**2014**, 22, 1194–1204. (In Chinese) [Google Scholar] - Yu, H.D.; Yang, X.C.; Xu, B.; Jin, Y.X.; Gao, T.; Li, J.Y. Changes of grassland vegetation growth in Xilingol league over 10 years and analysis on the influence factors. J. Geo-Inf. Sci.
**2013**, 15, 270–279. (In Chinese) [Google Scholar] - Xilinhaote News. Available online: http://xilinhaote.nmgnews.com.cn/system/2009/07/20/010253966.shtml (accesed on 4 September 2015).
- Liu, C.L.; Fan, R.H.; Wu, J.J.; Yan, F. Temporal lag of grassland vegetation growth response to precipitation in Xilinguolemeng. Arid Land Geogr.
**2009**, 32, 512–518. (In Chinese) [Google Scholar]

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## Share and Cite

**MDPI and ACS Style**

Zhang, B.; Zhang, L.; Xie, D.; Yin, X.; Liu, C.; Liu, G. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. *Remote Sens.* **2016**, *8*, 10.
https://doi.org/10.3390/rs8010010

**AMA Style**

Zhang B, Zhang L, Xie D, Yin X, Liu C, Liu G. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. *Remote Sensing*. 2016; 8(1):10.
https://doi.org/10.3390/rs8010010

**Chicago/Turabian Style**

Zhang, Binghua, Li Zhang, Dong Xie, Xiaoli Yin, Chunjing Liu, and Guang Liu. 2016. "Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation" *Remote Sensing* 8, no. 1: 10.
https://doi.org/10.3390/rs8010010