سابِق|تال

Time series high-resolution albedo retrieval over a rugged terrain based on the ensemble kalman filter algorithm

  • role： First author第一作者
  • Affiliation：

    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China

  • Email：1902273367@qq.com
  • Introduction：郑凯旋，研究方向为生态定量遥感。E-mail：1902273367@qq.com
ZHENG Kaixuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China

  • Email：linxw@zjnu.edu.cn
  • Introduction：林兴稳，研究方向为地表辐射传输建模与产品真实性检验。E-mail：linxw@zjnu.edu.cn
LIN Xingwen1*,  
  • Affiliation：

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

    University of Chinese Academy of Sciences, Beijing 100049, China

WEN Jianguang23,  
  • Affiliation：

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

HAO Dalei2

ملخص

Land surface albedo is a key parameter to describe the surface energy budget. An increasing need for fine-scale albedo products is promoted in regional applications of radiative forcing and coarse-scale albedo product validation. However, the long-term fine-scale albedo products over mountainous areas are currently unavailable. The topographic slope, aspect, and land cover types make the sloping surface more heterogeneous than the flat surface. Existing fine-scale albedo estimation algorithms may carry the uncertainties due to the complex topography. Moreover, the fine-scale albedo observations are often unavailable due to cloud contamination, making it difficult to obtain time series albedo estimations.To overcome these problems, we adopt the improved Angular Bin algorithm and Ensemble Kalman Filter Algorithm in this study to estimate a time-series fine-scale satellite-based albedo over a rugged terrain. The preliminary approach of the new built albedo estimation over mountainous areas was carried out in the Heihe River Basin by using the Chinese GF-4 satellite data.Validation results against ground measurements over various land cover types and topographic slopes show that our algorithm is effective for the selected land surfaces and can achieve root mean square errors of not more than 0.03. When compared with the referenced albedo product retrieved by direct retrieval algorithm, the GF-4 albedo products show a good performance with the RMSE smaller than 0.02.The retrieved long time series GF-4 albedo can improve the understanding of scale effects among different spatial resolution albedo products and help upscale in ground-based albedo measurements to coarse-scale during the multi-scale validation workflow. This algorithm also provides an example for other satellite-based remote sensing product retrieval over a rugged terrain.

مفهوم

land surface albedo;rugged terrain;GF-4 satellite;EnKF;long time series

References

  1. 1.
    Cai J Y. 2019. Remote Sensing Image Spatial-Spectral and Spatial-Temporal Fusion based on Deep Learning. Wuhan: Wuhan University
  2. 2.
    Combal B, Isaka H and Trotter C. 2000. Extending a turbid medium BRDF model to allow sloping terrain with a vertical plant stand. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 798-810
  3. 3.
    Coops N C, Johnson M, Wulder M A and White J C. 2006. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 103(1): 67-80
  4. 4.
    Dickinson R E. 1995. Land processes in climate models. Remote Sensing of Environment, 51(1): 27-38
  5. 5.
    Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5): 10143-10162
  6. 6.
    Feng Z M, Wen J G, Xiao Q, You D Q, Lin X W and Wu X D. 2018. Comparison of global albedo products of MODIS V006 and V005 based on FLUXNET. Journal of Remote Sensing, 22(1): 97-109
  7. 7.
    Gao F, He T, Masek J G, Shuai Y M, Schaaf C B and Wang Z S. 2014. Angular effects and correction for medium resolution sensors to support crop monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11): 4480-4489
  8. 8.
    Gao F, Masek J G, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218
  9. 9.
    Guan Y H, Zhou G Q, Lu W S and Chen J P. 2007. Theory development and application of data assimilation methods. Meteorology and Disaster Reduction Research, 30(4): 1-8
  10. 10.
    He T, Liang S L, Wang D D, Cao Y F, Gao F, Yu Y Y and Feng M. 2018. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment, 204: 181-196
  11. 11.
    Holben B N. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11): 1417-1434
  12. 12.
    Huang B and Song H H. 2012. Spatiotemporal reflectance fusion via sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3707-3716
  13. 13.
    Justice C O, Townshend J R G, Holben B N and Tucker C J. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6(8): 1271-1318
  14. 14.
    Kalman R E. 1960. A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82(1): 35-45
  15. 15.
    Lacaze R, Smets B, Trigo I, Calvet J C, Jann A, Camacho F, Baret F, Kidd R, Defourny P, Tansey K. The Copernicus Global Land Service: Present and future. In Proceedings of the EGU General Assembly, Vienna, Austria, 7-12 April 2013
  16. 16.
    Li J, Li Y F, He L, Chen J and Plaza A. 2020. Spatio-temporal fusion for remote sensing data: an overview and new benchmark. Science China Information Sciences, 63(4): 140301
  17. 17.
    Li X, Liu S M, Ma M G, Xiao Q, Liu Q H, Jin R, Che T, Wang W Z, Qi Y, Li H Y, Zhu G F, Guo J W, Ran Y H, Wen J G, Wang S G. HiWATER: an integrated remote sensing experiment on hydrological and ecological processes in the Heihe River Basin. Advances in Earth Science, 27(5): 481-498
  18. 18.
    Li Z, Zhao L F and Fu Z. 2012. Estimating net radiation flux in the Tibetan Plateau by assimilating MODIS LST products with an ensemble Kalman filter and particle filter. International Journal of Applied Earth Observation and Geoinformation, 19: 1-11
  19. 19.
    Liang S L, Wang K C, Zhang X T and Wild M. 2010. Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 225-240
  20. 20.
    Liang S L, Zhao X, Liu S H, Yuan W P, Cheng X, Xiao Z Q, Zhang X T, Liu Q, Cheng J, Tang H R, Qu Y H, Bo Y C, Qu Y, Ren H Z, Yu K and Townshend J. 2013. A long-term global land surface satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth, 6(S1): 5-33
  21. 21.
    Lin X W. 2018a. Land Surface Albedo Remote Sensing Products Validation Over Rugged Terrain. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (林兴稳. 2018a. 山区地表反照率遥感产品真实性检验方法研究. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所)
  22. 22.
    Lin X W, Wen J G, Liu Q H, Xiao Q, You D Q, Wu S B, Hao D L and Wu X D. 2018b. A multi-scale validation strategy for albedo products over rugged terrain and preliminary application in Heihe River Basin, China. Remote Sensing, 10(2): 156
  23. 23.
    Lin X W, Wen J G, Wu S B, Hao D L, Xiao Q and Liu Q H. 2020. Advances in topographic correction methods for optical remote sensing imageries. Journal of Remote Sensing, 24(8): 958-974
  24. 24.
    Liu Q, Wang L Z, Qu Y, Liu N F, Liu S H, Tang H R and Liang S L. 2013. Preliminary evaluation of the long-term GLASS albedo product. International Journal of Digital Earth, 6(S1): 69-95
  25. 25.
    Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The Heihe integrated observatory network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 1-21
  26. 26.
    Lü C C, Wang Z W and Qian S M. 2003. A Review of Pixel Unmixing Models. Remote Sensing Information, 3: 55-58,60
  27. 27.
    Qu Y, Liang S L, Liu Q, He T, Liu S H and Li X W. 2015. Mapping surface broadband albedo from satellite observations: a review of literatures on algorithms and products. Remote Sensing, 7(1): 990-1020
  28. 28.
    Schaaf C B, Gao F, Strahler A H, Lucht W, Li X W, Tsang T, Strugnell N C, Zhang X Y, Jin Y F, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, D'Entremont R P, Hu B X, Liang S L, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1-2): 135-148
  29. 29.
    Schaaf C B, Li X W and Strahler A H. 1994. Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric-optical canopy model. IEEE Transactions on Geoscience and Remote Sensing, 32(6): 1186-1193
  30. 30.
    Shuai Y M, Masek J G, Gao F and Schaaf C B. 2011. An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sensing of Environment, 115(9): 2204-2216
  31. 31.
    Shuai Y M, Masek J G, Gao F, Schaaf C B and He T. 2014. An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment, 152: 467-479
  32. 32.
    Sun C K, Liu Q, Wen J G, Li D, Yu K and Zhang Z G. 2013. An algorithm for retrieving land surface albedo from HJ-1 CCD data. Remote Sensing for Land and Resources, 25(4): 58-63
  33. 33.
    Sun Y J, Wang Z H, Qin Q M, Han G H, Ren H Z and Huang J F. 2018. Retrieval of surface albedo based on GF-4 geostationary satellite image data. Journal of Remote Sensing, 22(2): 220-233
  34. 34.
    Trenberth K E, Fasullo J T and Kiehl J. 2009. Earth’s global energy budget. Bulletin of the American Meteorological Society, 90(3): 311-324
  35. 35.
    Wu M Q, Niu Z, Wang C Y, Wu C Y and Wang L. 2012. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. Journal of Applied Remote Sensing, 6(1): 1-13
  36. 36.
    Wu S B, Wen J G, Gastellu-Etchegorry J E, Jean P, Liu Q H, You D Q, Xiao Q, Hao D L, Lin X W and Yin T G. 2019. The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225: 403-415
  37. 37.
    Wu X D, Wen J G, Xiao Q, Yu Y Y, You D Q and Hueni A. 2017. Assessment of NPP VIIRS albedo over heterogeneous crop land in Northern China. Journal of Geophysical Research: Atmospheres, 122(24): 13138-13154
  38. 38.
    Xue J, Leung Y and Fung T. 2017. A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images. Remote Sensing, 9(12): 1-23
  39. 39.
    Yao T and Zhang Q. 2014. Study on land-surface albedo over different types of underlying surfaces in North China. Acta Physica Sinica, 63(8): 460-468
  40. 40.
    Yin G F, Li A N, Wu S B, Fan W L, Zeng Y L, Yan K, Xu B D, Li J and Liu Q H. 2018. PLC: a simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment, 215: 184-198
  41. 41.
    Zhang G D, Zhou H M, Wang C J, Xue H Z, Wang J D and Wan H W. 2019. Time series high-resolution land surface albedo estimation based on the ensemble Kalman filter algorithm. Remote Sensing, 11(7): 753
  42. 42.
    Zhang M H, Li X. 2020. Drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks. IEEE Internet of Things Journal, 7(8):7648-7662
  43. 43.
    Zhao X J. 2012.Study on the Spatio-temporal Characteristics of Oasisization in the reaches of Heihe River Basin from 1949 to 2009. Lanzhou: Lanzhou University (赵晓冏. 2012. 近60年黑河流域绿洲时空变化特征研究. 兰州: 兰州大学)
  44. 44.
    Zhu L. 2007. Study of Ensemble Kalman Filter. Nanjing:NanJing University of Information Science & Technology
  45. 45.
    Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623
  46. 46.
    Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165-177
  47. 47.
    Zhukov B, Oertel D, Lanzl F and Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1212-1226

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