Assessment of FY-3D MERSI/NDVI global product
-
摘要: MERSI/NDVI是风云三号D星的一个关键业务产品，深入了解其质量状况对推广产品应用、改进产品算法都具有重要意义。文中针对业务化运行后的全球MERSI/NDVI产品（2019年5月至2020年12月），以同期Terra MODIS/NDVI产品（MOD13A2）为参考，通过空间格局和时间序列的对比、APU（准确度Accuracy，精密度Precision，不确定性Uncertainty）指标计算以及回归分析等手段，评估MERSI/NDVI数据质量和可用性。结果显示，MERSI/NDVI和MODIS/NDVI在空间分布和时序特征方面具有较高一致性，但MERSI/NDVI有对高值低估、低值高估的倾向，故动态范围略窄；在全球平均水平上，MERSI/NDVI比MODIS/NDVI系统性偏低0.02—0，P和U值为0.06—0.08，MERSI/NDVI与MODIS/NDVI的差别由小到大的顺序大致为：裸土荒漠、稀疏灌丛和草地、密闭灌丛与农田、除常绿阔叶林以外的森林、常绿阔叶林；以MODIS/NDVI为自变量、MERSI/NDVI为因变量的线性回归模型精度较高（R2：0.91—0.95，RMSE：0.048—0.068），回归系数具有一定的时间变化（斜率：0.87—0.94，截距：0.02—0.04）。本研究是首次对风云三号D星MERSI/NDVI产品开展近乎全样本的对比检验，证明该产品基本可以替代MODIS/NDVI在全球开展物候信息提取、植被长势监测等应用。Abstract: FY-3D MERSI/NDVI is a critical operational product used in many studies (ecosystem monitoring, climate change, agriculture drought, etc.), and it is essential to obtain a comprehensive assessment of this product's quality. In this paper, the first assessment results of global MERSI/NDVI for the period from May 2019 to December 2020 are reported using Terra MODIS/NDVI as the reference. Quantitative measures of APU (Accuracy, Precision and Uncertainty) are calculated and the variations associated with different factors (seasons, land cover types and NDVI values) are analyzed. Results indicate that generally the two products share a high similarity concerning the spatial pattern and temporal profiles features. The dynamic range of MERSI/NDVI is slightly narrower because it overestimates NDVI for barren land and underestimates NDVI for dense vegetation. Sensitivity analysis indicates that the overestimation is mainly attributed to overestimation of NIR reflectance, whereas the underestimation is mainly attributed to overestimation of red reflectance. Phonological features conveyed by the two NDVI products are consistent, but there are slightly noisier fluctuations in MERSI/NDVI time series probably caused by cloud contamination during growing season. Over the 20 months period checked in this study, the global mean of the Accuracy value ranges within −0.02—0, and the global mean of the Precision and Uncertainty values generally range within 0.06—0.08. With respect to the spatial pattern, APU values are the highest in forests, moderate in grassland/shrubland, and lowest in desert. Linear regression models with MODIS/NDVI as independent variables and MERSI/NDVI as dependent variables achieve high accuracies (R2: 0.91—0.95, RMSE: 0.048—0.068), confirming that it is feasible to build a compatible long-term NDVI dataset using both products. This study is the first cross-sensor comparison study using almost all of the global MERSI/NDVI data available since the operational application of the FY-3D satellite. Overall, the MERSI/NDVI data are of very high quality and can be effectively used for deriving vegetation phonological and greenness information. Its performance on the global scale will be monitored continually.
-
Key words:
- FY-3D /
- MERSI /
- NDVI /
- Quality evaluation
-
图 1 FY-3D/MERSI和TERRA/MODIS的红光、近红外光谱响应函数对比
Figure 1. SRF comparison for red and NIR channels between FY-3D/MERSI and TERRA/MODIS
图 2 MODIS/NDVI（a1—d1）和MERSI/NDVI（a2—d2）在不同季的空间格局对比 （a. 春，b. 夏，c. 秋，d. 冬）
Figure 2. Spatial pattern comparisons between MERSI/NDVI （a1—d1） and MODIS/NDVI （a2—d2） for different seasons （a. Spring，b. Summer，c. Autumn，d. Winter）
图 3 MODIS/NDVI和MERSI/NDVI在不同季节的差值概率分布 （a. 春，b. 夏，c. 秋，d. 冬）
Figure 3. Histograms of NDVI difference （MERSI/NDVI−MODIS/NDVI） for different seasons （a. Spring，b. Summer，c. Autumn，d. Winter）
图 4 MODIS/NDVI和MERSI/NDVI在不同季节的散点 （a. 春，b. 夏，c. 秋，d. 冬）
Figure 4. Scatterplots of MERSI/NDVI and MODIS/NDVI for different seasons （a. Spring，b. Summer，c. Autumn，d. Winter）
图 5 不同纬度带的NDVI均值时间序列对比 （2019年第161天至2020年第321天）
Figure 5. NDVI time series comparison for different latitudes （DOY161 in 2019 to DOY321 in 2020）
图 6 不同土地覆盖类型的NDVI时间序列对比 （2019年第161天至2020年第321天）
Figure 6. NDVI time series comparisons for different land cover types （DOY161 in 2019 to DOY321 in 2020）
图 8 APU随土地覆盖类型的变化特征 （1：常绿针叶林，2：常绿阔叶林，3：落叶针叶林，4：落叶阔叶林，5：混交林，6：密闭灌丛，7：开放灌丛，8：木质稀树草原，9：稀树草原，10：草地，12：农田，14：农田和自然交错，16：荒漠/裸地）
Figure 8. Averaged APU for different land cover types （1：Evergreen，needleleaf forest，2：Evergreen broadleaf forest，3：Deciduous needleleaf forest， 4：Deciduous broadleaf forest，5：Mixed forests， 6：Closed shrublands，7：Open shrublands，8：Woody savannas， 9：Savannas， 10： Ggrasslands， 12：Croplands，14：Cropland/Natural vegetation mosaic， 16：Barren or sparsely vegetated）
图 9 APU随时间的变化 （2019年第161天至2020年第321天）
Figure 9. Temporal evolution of global-mean APU （DOY161 in 2019 to DOY321 in 2020）
图 11 RMA回归斜率（a）、截距（b）、RMSE（c）和R2（d）随时间的变化特征 （2019年第161天至2020年第321天）
Figure 11. RMA regression slopes（a），intercepts（b），RMSE（c） and R2（d） （DOY161 in 2019 to DOY321 in 2020）
图 12 红光和近红外反射率的差异造成的NDVI差异 （MERSI−MODIS）
Figure 12. NDVI differences attributed to red reflectance differences and NIR reflectance differences，respectively
图 13 不同覆盖度条件下的植被光谱反射率 （Lu，et al，2021）
Figure 13. Influence of canopy green vegetation fraction on spectral reflectance （Lu，et al，2021）
-
[1] Bevington P R, Robinson D K. 2003. Data Reduction and Error Analysis for the Physical Sciences. Boston: McGraw-Hill [2] Brown M E,Pinzón J E,Didan K,et al. 2006. Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation,SeaWiFS,MODIS,and landsat ETM+ sensors. IEEE Trans Geosci Remote Sens,44(7):1787-1793 doi: 10.1109/TGRS.2005.860205 [3] Carter C, Chen M, Jiang Z, et al. 2020. （STAR） NOAA NESDIS STAR VIIRS VI algorithm theoretical basis document. Version: 2.0. https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_VIIRS_VI_v2.0.pdf [4] Didan K, Munoz A B, Solano R, et al. 2015a. MODIS vegetation index user's guide （MOD13 series） version 3.00. https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf [5] Didan K, Munoz A B, Miura T, et al. 2015b. Multi-sensor vegetation index and phenology earth science data records: Algorithm theoretical basis document and user guide, Version 4.0. Vegetation Index and Phenology Lab, University of Arizona. https://lpdaac.usgs.gov/documents/178/VIP_User_Guide_ATBD_V4.pdf [6] Didan K, Munoz A, Comptom T, et al. 2018. Suomi national polar-orbiting partnership visible infrared imaging radiometer suite vegetation index product suite user guide & abridged algorithm theoretical basis document version 2.1.2. https://viirsland.gsfc.nasa.gov/PDF/SNPP_VIIRS_VI_UserGuide_09-26-2017_KDidan.pdf [7] Fan X W,Liu Y B. 2016. A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS J Photogramm Remote Sens,121:177-191 doi: 10.1016/j.isprsjprs.2016.09.008 [8] Fensholt R. 2004. Earth observation of vegetation status in the Sahelian and Sudanian West Africa:Comparison of Terra MODIS and NOAA AVHRR satellite data. Int J Remote Sens,25(9):1641-1659 doi: 10.1080/01431160310001598999 [9] Fensholt R,Nielsen T S,Stisen S. 2006. Evaluation of AVHRR PAL and GIMMS 10-day composite NDVI time series products using SPOT-4 vegetation data for the African continent. Int J Remote Sens,27(13):2719-2733 doi: 10.1080/01431160600567761 [10] Gallo K,Ji L,Reed B,et al. 2004. Comparison of MODIS and AVHRR 16-day normalized difference vegetation index composite data. Geophys Res Lett,31(7):L07502 [11] Gallo K,Ji L,Reed B,et al. 2005. Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data. Remote Sens Environ,99(3):221-231 doi: 10.1016/j.rse.2005.08.014 [12] Han X Z,Yang J,Tang S H,et al. 2020. Vegetation products derived from Fengyun-3D medium resolution spectral imager-Ⅱ. J Meteor Res,34(4):775-785 doi: 10.1007/s13351-020-0027-5 [13] Huete A,Didan K,Miura T,et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ,83(1-2):195-213 doi: 10.1016/S0034-4257(02)00096-2 [14] Ji L,Gallo K,Eidenshink J C,et al. 2008. Agreement evaluation of AVHRR and MODIS 16-day composite NDVI data sets. Int J Remote Sens,29(16):4839-4861 doi: 10.1080/01431160801927194 [15] Jiang Z, Huete A, Wang Y, et al. 2011. Evaluation of MODIS VI Products using the AERONET-based surface reflectance validation network dataset∥34th International Symposium on Remote Sensing of Environment. Sydney, Australia, 10-15 April 2011. https://www.isprs.org/proceedings/2011/ISRSE-34/211104015Final00786.pdf [16] Lu J S,Li W Y,Yu M L,et al. 2021. Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precis Agric,22(1):51-74 doi: 10.1007/s11119-020-09729-z [17] Marshall M,Okuto E,Kang Y,et al. 2016. Global assessment of vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products. Biogeosciences,13(3):625-639 doi: 10.5194/bg-13-625-2016 [18] Miura T,Huete A R,Yoshioka H. 2000. Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans Geosci Remote Sens,38(3):1399-1409 doi: 10.1109/36.843034 [19] Miura T,Turner J P,Huete A R. 2013. Spectral compatibility of the NDVI across VIIRS,MODIS,and AVHRR:An analysis of atmospheric effects using EO-1 Hyperion. IEEE Trans Geosci Remote Sens,51(3):1349-1359 doi: 10.1109/TGRS.2012.2224118 [20] Miura T,Muratsuchi J,Vargas M. 2018. Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations. J Appl Remote Sens,12(4):045004 doi: 10.1117/1.JRS.12.045004 [21] Nagol J R,Vermote E F,Prince S D. 2009. Effects of atmospheric variation on AVHRR NDVI data. Remote Sens Environ,113(2):392-397 doi: 10.1016/j.rse.2008.10.007 [22] Shabanov N,Vargas M,Miura T,et al. 2015. Evaluation of the performance of Suomi NPP VIIRS top of canopy vegetation indices over AERONET sites. Remote Sens Environ,162:29-44 doi: 10.1016/j.rse.2015.02.004 [23] Skakun S,Justice C O,Vermote E,et al. 2018. Transitioning from MODIS to VIIRS:An analysis of inter-consistency of NDVI data sets for agricultural monitoring. Int J Remote Sens,39(4):971-992 doi: 10.1080/01431161.2017.1395970 [24] Swinnen E,Veroustraete F. 2008. Extending the SPOT-VEGETATION NDVI time series (1998-2006) back in time with NOAA-AVHRR data (1985-1998) for Southern Africa. IEEE Trans Geosci Remote Sens,46(2):558-572 doi: 10.1109/TGRS.2007.909948 [25] Trishchenko A P,Cihlar J,Li Z Q. 2002. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens Environ,81(1):1-18 doi: 10.1016/S0034-4257(01)00328-5 [26] Tucker C J,Pinzon J E,Brown M E,et al. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens,26(20):4485-4498 doi: 10.1080/01431160500168686 [27] van Leeuwen W J D,Orr B J,Marsh S E,et al. 2006. Multi-sensor NDVI data continuity:Uncertainties and implications for vegetation monitoring applications. Remote Sens Environ,100(1):67-81 doi: 10.1016/j.rse.2005.10.002 [28] Vargas M,Miura T,Shabanov N,et al. 2013. An initial assessment of Suomi NPP VIIRS vegetation index EDR. J Geophys Res,118(22):12301-12316 doi: 10.1002/2013JD020439 [29] Xu N,Niu X H,Hu X Q,et al. 2018. Prelaunch calibration and radiometric performance of the advanced MERSI Ⅱ on FengYun-3D. IEEE Trans Geosci Remote Sens,56(8):4866-4875 doi: 10.1109/TGRS.2018.2841827 [30] Yang Z D,Zhang P,Gu S Y,et al. 2019. Capability of Fengyun-3D satellite in earth system observation. J Meteor Res,33(6):1113-1130 doi: 10.1007/s13351-019-9063-4 -
下载:
点击查看大图
计量
- 文章访问数: 139
- HTML全文浏览量: 28
- PDF下载量: 42
- 被引次数: 0
