Application of fused data to grassland biomass estimation
YIN Xiaoli1,2, ZHANG Li2, XU Junyi1, LIU Liangyun2
1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; 2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Abstract：In order to realize the real-time and high-precision monitoring of grassland biomass, the authors established a biomass estimation model for grasslands based on the fused data from the spatial and temporal adaptive reflectance fusion model (STARFM) in this study. Firstly, the authors introduced the STARFM model to fuse the MODIS and the Landsat TM data in Xilin Hot, Inner Mongolia. It was found that NDVI is a better input data for STARFM to achieve high-precision NDVI through comparing reflectance data and NDVI data. The most efficient statistical model, an exponential model, was chosen for estimating biomass based on the high-precision NDVI and the field survey data. Finally, two exponential models were set up respectively, with the fused NDVI and the original MODIS NDVI as independent variables. It was found that R2 increased from 0.761 to 0.832 and RMSE decreased from 32.521g/m2 to 28.653 g/m2 after using the fused NDVI. The results obtained by the authors prove that the fused NDVI can improve the accuracy of the grassland biomass estimation.
[1] 李建龙,蒋平.遥感技术在大面积天然草地估产和预报中的应用探讨[J].武汉测绘科技大学学报,1998,23(2):153-158. Li J L,Jiang P.The study on the remote sensing technology in estimating and forecasting grassland field applications[J].Journal of Wuhan Technical University of Surveying and Mapping,1998,23(2):153-158. [2] 查勇,Gao J,倪绍祥.国际草地资源遥感研究新进展[J].地理科学进展,2003,22(6):607-617. Zha Y,Gao J,Ni S X.Most recent progress of international research on remote sensing of grassland resources[J].Progress in Geography,2003,22(6):607-617. [3] 程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008,20(1):13-18. Cheng H F,Zhang W B,Chen F.Advances in researches on application of remote sensing method to estimating vegetation coverage[J].Remote Sensing for Land and Resources,2008,20(1):13-18. [4] Todd S W,Hoffer R M,Milchunas D G.Biomass estimation on grazed and ungrazed rangelands using spectral indices[J].International Journal of Remote Sensing,1998,19(3):427-438. [5] 张连义,宝路如,尔敦扎玛,等.锡林郭勒盟草地植被生物量遥感监测模型的研究[J].中国草地学报,2008,30(1):6-14. Zhang L Y,Bao L R,Erdun Z M,et al.Research on remote sensing models for monitoring grassland vegetation biomass in Xilinguole[J].Chinese Journal of Grassland,2008,30(1):6-14. [6] Boelman N T,Stieglitz M,Rueth H M,et al.Response of NDVI,biomass,and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra[J].Oecologia,2003,135(3):414-421.doi:10.1007/s00442-003-1198-3. [7] Asner G P.Cloud cover in Landsat of observations of the Brazilian Amazon[J].International Journal of Remote Sensing,2001,22(18):3855-3862. [8] Jorgensen P V.Determination of cloud coverage over Denmark using Landsat MSS/TM and NOAA-AVHRR[J].International Journal of Remote Sensing,2000,21(17):3363-3368.doi:10.1080/014311600750019976. [9] Ju J C,Roy D P.The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally[J].Remote Sensing of Environment,2008,112(3):1196-1211. [10] Price J C.How unique are spectral signatures?[J].Remote Sensing of Environment,1994,49(3):181-186. [11] Gao F,Masek J G,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218. [12] Hilker T,Wulder M A,Coops N C,et al.Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model[J].Remote Sensing of Environment,2009,113(9):1988-1999. [13] Devendra S.Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data[J].International Journal of Applied Earth Observation and Geoinformation,2011,13(1):59-69. [14] Watts J D,Powell S L,Lawrence R L,et al.Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery[J].Remote Sensing of Environment,2011,115(1):66-75. [15] 朴世龙,方精云,贺金生,等.中国草地植被生物量及其空间分布格局[J].植物生态学报,2004,28(4):491-498. Piao S L,Fang J Y,He J S,et al.Spatial distribution of grassland biomass in China[J].Acta Phytoecologica Sinica,2004,28(4):491-498. [16] Zhu X L,Chen J,Gao F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote Sensing of Environment,2010,114(11):2610-2623.doi:10.1016/j.rse.2010.05.032. [17] Walker J J,de Beurs K M,Wynne R H,et al.Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology[J].Remote Sensing of Environment,2012,117:381-393.doi:10.1016/j.rse.2011.10.014. [18] 杨英莲,邱新法,殷青军.基于MODIS增强型植被指数的青海省牧草产量估产研究[J].气象,2007,33(6):102-107. Yang Y L,Qiu X F,Yin Q J.Study on monitoring system of Qinghai grassland output based MODIS EVI data[J].Meteorological Monthly,2007,33(6):102-107. [19] 李素英,李晓兵,莺歌,等.基于植被指数的典型草原区生物量模型:以内蒙古锡林浩特市为例[J].植物生态学报,2007,31(1):23-31. Li S Y,Li X B,Ying G,et al.Vegetation indexes biomass models for typical semi_arid steppe:A case study for Xilinhot in northern China[J].Journal of Plant Ecology,2007,31(1):23-31. [20] Xie Y C,Sha Z Y,Yu M,et al.A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China[J].Ecological Modelling,2009,220(15):1810-1818.
2013, Vol. 25 
