收稿：2016-10-26，
纸质出版：2017-03
移动端阅览
作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来，遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数，之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型，通过分析两者间的相关性与敏感性，获取适宜进行生物量估算的指数。最后，绘制了研究区的生物量空间分布图。结果表明，所选的植被指数均与生物量显著相关。其中，红边叶绿素指数(CI
re
)与生物量的估算精度最高(决定性系数
R
2
为0.83；均方根误差RMSE为180.29 g·m
–2
)。虽然相关性较高，但部分指数，如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象，从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势，而且能够提高反演精度。此外，通过敏感性分析发现，归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR
re
)和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性，二者是生物量估算中最为稳定的指数。
Crop biomass plays an important role in food security and global carbon cycle
and the timely and efficient monitoring of biomass is crucial for precise and reasonable agricultural management. Recently
remote sensing technique has been proven to be an effective tool for biomass estimation and it can decrease the conduct of field surveys. The European Sentinel-2A satellite was successfully launched in late June 2015.This satellite can provide high spatial resolution (10 m
20 m
and 60 m) data freely. It uses a thirteen-band spectrum ranging from the visible region to the short-wave infrared region and thus is useful in imaging planted regions with high fragmentation. For this reason
the main objective of this paper is to explore the potential of winter wheat biomass estimation based on the new Sentinel data.In this study
17 Vegetation Indices (VIs) based on the combinations of canopy reflectance in blue
green
red
red-edge
and near-infrared bands were first derived from the Sentinel-2A imagery in April and May 2016. The Above Ground Biomass (AGB) data collected during the same period were then used for constructing the best-fit relationships between the selected VIs and AGB. The correlation and sensitivity of the relationships between them were then analyzed. Finally
the spatial distributions of the biomass in the study area were mapped through the estimation models.All the tested VIs were nonlinearly and significantly correlated with AGB and generated
R
2
ranging from 0.59 to 0.83 and RMSE ranging from 180.29 g·m
–2
to 0.289.79 g·m
–2
. Among these VIs
the red-edge chlorophyll index exhibited superior performance on AGB estimation (
R
2
=0.83
RMSE=180.29 g·m
–2
)
whereas the green chlorophyll index presented the highest estimation accuracy when the red-edge bands were not available (
R
2
=0.81
RMSE=191.15 g·m
–2
). The scatter-plots between the VIs and AGB showed that several VIs
such as the widely used normalized difference vegetation index
saturate at moderate-to-high biomass stages (higher than 1000g·m
–2
) mainly because of the strong light absorption of the red band and scattering of the near-infrared band at high LAI levels. In addition
the indices incorporated red-edge bands and thus were more closely related to the biomass compared with the original indices and were able to disrupt the saturation. Sensitivity analysis results indicated that although the
R
2
and RMSE values of some VIs were similar
the Vis had different sensitivities. For example
the normalized difference indices and ratio indices were more sensitive to biomass variations in the low and moderate-to-high biomass stages
respectively. On the basis of their high predictive ability
high sensitivity
and high degree of linearity
we consider the red-edge simple ratio and MERIS terrestrial chlorophyll index as a stable index for AGB estimation covering the entire growing season.Our research provides a reliable approach for winter wheat biomass estimation using the Sentinel-2A data. Given that the repeat cycle will be shortened to five days when the Sentinel-2B is launched
the Sentinel data with high spatial resolution and enhanced spectral information (including threered-edge bands) is meaningful in precision agriculture
especially in yield and production prediction.
Atzberger C. 2013. Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2): 949–981
Bao Y S, Gao W and Gao Z Q. 2009. Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions. Frontiers of Earth Science in China, 3(1): 118–128
Chen J M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3): 229–242
陈劲松, 黄健熙, 林珲, 裴志远. 2010. 基于遥感信息和作物生长模型同化的水稻估产方法研究. 中国科学: 信息科学, 40(S1): 173–183
Chen J S, Huang J X, Lin H and Pei Z Y. 2010. Rice yield estimation by assimilation remote sensing into crop growth model. Scientia Sinica Informations, 40(S1): 173–183
陈鹏飞, Tremblay N, 王纪华, Vigneault P, 黄文江, 李保国. 2010. 估测作物冠层生物量的新植被指数的研究. 光谱学与光谱分析, 30(2): 512–517
Chen P F, Tremblay N, Wang J H, Vigneault P, Huang W J and Li B G. 2010. New index for crop canopy fresh biomass estimation. Spectroscopy and Spectral Analysis, 30(2): 512–517
Dash J and Curran P J. 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25(23): 5403–5413
Dong T F, Liu J G, Qian B D, Zhao T, Jing Q, Geng X Y, Wang J F, Huffnan T and Shang J L. 2016. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 49: 63–74
Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F and Bargellini P. 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120: 25–36
Du X, Li Q Z, Dong T F and Jia K. 2015. Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model. Geocarto International, 30(3): 258–269
Feng X, Liu G, Chen J M, Chen M, Liu J, Ju W M, Sun R and Zhou W. 2007. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. Journal of Environmental Management, 85(3): 563–573
Gitelson A A, Gritz Y and Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271–282
Gitelson A A and Merzlyak M N. 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12): 2691–2697
Gitelson A A, Peng Y and Huemmrich K F. 2014. Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data. Remote Sensing of Environment, 147: 108–120
Gitelson A A, Viña A, Ciganda V, Rundquist D C and Arkebauer T J. 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32(8):L08403
Gnyp M L, Miao Y X, Yuan F, Ustin S L, Yu K, Yao Y K, Huang S Y and Bareth G. 2014. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, 155: 42–55
Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J and Strachan I B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3): 337–352
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195–213
Jiang Z Y, Huete A R, Didan K and Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10): 3833–3845
Jin X L, Yang G J, Xu X G, Yang H, Feng H K, Li Z H, Shen J X, Lan Y B and Zhao C J. 2015. Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data. Remote Sensing, 7(10): 13251–13272
Jordan C F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4): 663–666
Kross A, McNairn H, Lapen D, Sunohara M and Champagne C. 2015. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34: 235–248
Ma Y P, Wang S L, Zhang L, Hou Y Y, Zhuang L W, He Y B and Wang F T. 2008. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 10(4): 426–437
Meng J H, Du X and Wu B F. 2013. Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. International Journal of Digital Earth, 6(3): 203–218
Nguy-Robertson A, Gitelson A, Peng Y, Viña A, Arkebauer T and Rundquist D. 2012. Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agronomy Journal, 104(5): 1336–1347
Peng D L, Huang J F, Li C J, Liu L Y, Huang W J, Wang F M and Yang X H. 2014. Modelling paddy rice yield using MODIS data. Agricultural and Forest Meteorology, 184: 107–116
Rondeaux G, Steven M and Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2): 95–107
Rouse J W Jr, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS//Proceedings of the 3rd ERTS-1 Symposium.Washington DC: NASA:309-317
Sims D A and Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2/3): 337–354
谭昌伟, 王纪华, 赵春江, 王妍, 王君婵, 童璐, 朱新开, 郭文善. 2011. 利用Landsat TM遥感数据监测冬小麦开花期主要长势参数. 农业工程学报, 27(5): 224–230
Tan C W, Wang J H, Zhao C J, Wang Y, Wang J C, Tong L, Zhu X K and Guo W S. 2011. Monitoring wheat main growth parameters at anthesis stage by Landsat TM. Transactions of the CSAE, 27(5): 224–230
Viña A, Gitelson A A, Nguy-Robertson and Peng Y. 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115(12): 3468–3478
Wu C Y, Niu Z, Tang Q and Huang W J. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology, 148(8/9): 1230–1241
相关作者
相关机构
京公网安备11010802024621
