Früher|Nächster

Cloud removal and optimal threshold selection of MODIS NDSI production

  • role： First author第一作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：wangxiaoy@lzu.edu.cn
  • Introduction：王晓艳，研究方向为定量遥感、积雪遥感。E-mail：wangxiaoy@lzu.edu.cn
WANG Xiaoyan1,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210008, China

CHEN Siyong2,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

GUO Hui1,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

XIE Peiyao1,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

WANG Jian34,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

HAO Xiaohua3

Resümee

The normalized difference snow index (NDSI) is the most commonly used index in snow identification. However, the application of MODIS NDSI products is restricted due to cloud occlusion. This study aims to produce daily cloud-free MODIS NDSI production with high accuracy and determine the optimal NDSI threshold in snow identification.In this work, a cloud removal method based on adjacent similar pixels is presented for MODIS NDSI products. First, MOD10A1 and MYD10A1 on the same day are combined. The rule is that MOD10A1 is updated by MYD10A1 at the same location when MOD10A1 is marked by clouds. However, MYD10A1 is cloud-free. Second, an adjacent temporal composite is created. The mean of the nearest valid NDSI values for the adjacent 2 days to that location was assigned to a cloudy pixel. Finally, the residual cloud pixels are processed based on the removed adjacent similar pixels. A weighted cloud-free similar pixel function is established to predict the cloudy target pixel on the NDSI image. The first n similar pixels in the w by w local window are selected, and a weighting function can be constructed to compute the NDSI value for the target pixel. In this study, n=20 and w=15 are recommended in practice. The cloud removal experiment is carried out with the MODIS NDSI products in the northeast China from October 1, 2017 to April 31, 2018. The optimal NDSI threshold of snow identification is then determined based on the Snow Depth (SD) data of the meteorological stations.The effectiveness of cloud removal was validated through “cloud assumption”. Results showed that the correlation coefficient r between the predicted NDSI value and the true value is 0.95, and the root mean square error is 0.08. The daily cloud free NDSI sequence has good agreement with the SD sequence measured by the meteorological stations. When the measured SD of a meteorological station is greater than or equal to 1 cm, the pixel where the station is located is a snow pixel; otherwise, the pixel is snow free. Accordingly, the true value of the binary snow distribution can be obtained according to the SD measured by meteorological station. Thereafter, the true value is used to analyze the optimal threshold of cloud free NDSI sequence in snow identification. The results show that the accuracy of snow identification is the highest when the NDSI threshold is 0.1 in non-forest areas, which can reach 95.6%; the optimal threshold of NDSI in forest areas is 0, and the corresponding snow identification accuracy is 93.5%.(1) The cloud removal method based on adjacent similar pixels is effective for the generation of daily cloud free MODIS NDSI products. (2) The daily cloud free NDSI sequence has good agreement with the SD sequence measured by the meteorological stations. (3) The optimal threshold of the cloud free NDSI sequence in snow identification is 0.1 in non-forest areas and 0 in forest areas.

Schlüsselwort

remote sensing;Snow;cloud removal;MODIS;NDSI;Northeast China;forest

References

  1. 1.
    Ault T W, Czajkowski K P, Benko T, Coss J, Struble J, Spongberg A, Templin M and Gross C. 2006. Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region. Remote Sensing of Environment, 105(4): 341-353
  2. 2.
    Barnett T P, Adam J C and Lettenmaier D P. 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066): 303-309
  3. 3.
    Cao Y G, Yang X C, Xu B and Zhu X H. 2007. Applications and limitations of a snow mapping algorithm based on MODIS data in Qinghai-Tibet Plateau. Science and Technology Review, 25(21): 51-54
  4. 4.
    Che T, Dai L Y, Zheng X M, Li X F and Zhao K. 2016. Estimation of snow depth from passive microwave brightness temperature data in forest regions of Northeast China. Remote Sensing of Environment, 183: 334-349
  5. 5.
    Che T and Li X. 2005. Spatial distribution and temporal variation of snow water resources in China during 1993—2002. Journal of Glaciology and Geocryology, 27(1): 64-67
  6. 6.
    Chen X N, Long D, Liang S L, He L, Zeng C, Hao X H and Hong Y. 2018. Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data. Remote Sensing of Environment, 215: 284-299
  7. 7.
    Cheng Q, Liu H Q, Shen H F, Wu P H and Zhang L P. 2017. A spatial and temporal nonlocal filter-based data fusion method. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4476-4488
  8. 8.
    Choi G, Robinson D A and Kang S. 2010. Changing Northern Hemisphere snow seasons. Journal of Climate, 23(19): 5305-5310
  9. 9.
    Da Ronco P and De Michele C. 2014. Cloud obstruction and snow cover in Alpine areas from MODIS products. Hydrology and Earth System Sciences, 18(11): 4579-4600
  10. 10.
    Deng J, Huang X D, Feng Q S, Ma X F and Liang T G. 2015. Toward improved daily cloud-free fractional snow cover mapping with multi-source remote sensing data in China. Remote Sensing, 7(6): 6986-7006
  11. 11.
    Dong C Y and Menzel L. 2016. Producing cloud-free MODIS snow cover products with conditional probability interpolation and meteorological data. Remote Sensing of Environment, 186: 439-451
  12. 12.
    Dozier J, Painter T H, Rittger K and Frew J E. 2008. Time-space continuity of daily maps of fractional snow cover and albedo from MODIS. Advances in Water Resources, 31(11): 1515-1526
  13. 13.
    Frei A, Tedesco M, Lee S, Foster J, Hall D K, Kelly R and Robinson D A. 2012. A review of global satellite-derived snow products. Advances in Space Research, 50(8): 1007-1029
  14. 14.
    Gafurov A, Vorogushyn S, Farinotti D, Duethmann D, Merkushkin A and Merz B. 2015. Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data. The Cryosphere, 9(2): 451-463
  15. 15.
    Gao Y, Xie H J, Yao T D and Xue C S. 2010. Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA. Remote Sensing of Environment, 114(8): 1662-1675
  16. 16.
    Gladkova I, Grossberg M, Bonev G, Romanov P and Shahriar F. 2012. Increasing the accuracy of MODIS/Aqua snow product using quantitative image restoration technique. IEEE Geoscience and Remote Sensing Letters, 9(4): 740-743
  17. 17.
    Hall D K, Riggs G A, Salomonson V V, DiGirolamo N E and Bayr K J. 2002. MODIS snow-cover products. Remote Sensing of Environment, 83(1/2): 181-194
  18. 18.
    Hall D K and Riggs G A. 2007. Accuracy assessment of the MODIS snow products. Hydrological Processes, 21(12): 1534-1547
  19. 19.
    Hall D K, Riggs G A, Foster J L and Kumar S V. 2010. Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product. Remote Sensing of Environment, 114(3): 496-503
  20. 20.
    Hao X H, Wang J and Li H Y. 2008. Evaluation of the NDSI threshold value in mapping snow cover of MODIS-A case study of snow in the Middle Qilian Mountains. Journal of Glaciology and Geocryology, 30(1): 132-138
  21. 21.
    Hou J L, Huang C L, Zhang Y, Guo J F and Gu J. 2019. Gap-filling of MODIS fractional snow cover products via non-local spatio-temporal filtering based on machine learning techniques. Remote Sensing, 11(1): 90
  22. 22.
    Huang X D, Deng J, Ma X F, Wang Y L, Feng Q S, Hao X H and Liang T G. 2016. Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China. The Cryosphere, 10(5): 2453-2463
  23. 23.
    Huang X D, Deng J, Wang W, Feng Q S and Liang T G. 2017. Impact of climate and elevation on snow cover using integrated remote sensing snow products in Tibetan Plateau. Remote Sensing of Environment, 190: 274-288
  24. 24.
    Huang X D, Liang T G, Zhang X T and Guo Z G. 2011. Validation of MODIS snow cover products using Landsat and ground measurements during the 2001-2005 snow seasons over northern Xinjiang, China. International Journal of Remote Sensing, 32(1): 133-152
  25. 25.
    Huang X D, Zhang X T, Li X and Liang T G. 2007. Accuracy analysis for MODIS snow products of MOD10A1 and MOD10A2 in Northern Xinjiang Area. Journal of Glaciology and Geocryology, 29(5): 722-729
  26. 26.
    Huang Y, Liu H X, Yu B L, Wu J P, Kang E L, Xu M, Wang S J, Klein A and Chen Y N. 2018. Improving MODIS snow products with a HMRF-based spatio-temporal modeling technique in the Upper Rio Grande Basin. Remote Sensing of Environment, 204: 568-582
  27. 27.
    Jing Y H, Shen H F, Li X H and Guan X B. 2019. A two-stage fusion framework to generate a spatio-temporally continuous MODIS NDSI product over the Tibetan Plateau. Remote Sensing, 11(19): 2261
  28. 28.
    Klein A G and Barnett A C. 2003. Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000-2001 snow year. Remote Sensing of Environment, 86(2): 162-176
  29. 29.
    Li X H, Fu W X, Shen H F, Huang C L and Zhang L P. 2017. Monitoring snow cover variability (2000-2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method. Journal of Hydrology, 551: 314-327
  30. 30.
    Li X H, Jing Y H, Shen H F and Zhang L P. 2019a. The recent developments in cloud removal approaches of MODIS snow cover product. Hydrology and Earth System Sciences, 23(5): 2401-2416
  31. 31.
    Li Y, Chen Y and Li Z. 2019b. Developing daily cloud-free snow composite products from MODIS and IMS for the Tienshan Mountains. Earth and Space Science, 6(2): 266-275
  32. 32.
    Liang T G, Zhang X T, Xie H J, Wu C X, Feng Q S, Huang X D and Chen Q G. 2008. Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements. Remote Sensing of Environment, 112(10): 3750-3761
  33. 33.
    Liu M, Yang W, Zhu X L, Chen J, Chen X H, Yang L Q and Helmer E H. 2019. An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series. Remote Sensing of Environment, 227: 74-89
  34. 34.
    Parajka J and Blöschl G. 2006. Validation of MODIS snow cover images over Austria. Hydrology and Earth System Sciences, 10(5): 679-689
  35. 35.
    Parajka J and Blöschl G. 2008. Spatio-temporal combination of MODIS images-potential for snow cover mapping. Water Resources Research, 44(3): W03406
  36. 36.
    Qin D H, Chen Z L, Luo Y, Ding Y H, Dai X S, Ren J W, Zhai P M, Zhang X Y, Zhao Z C, Zhang D E, Gao X J and Shen Y P. 2007. Updated understanding of climate change sciences. Advances in Climate Change Research, 3(2): 63-73
  37. 37.
    Qiu Y B, Zhang H, Chu D, Zhang X C, Yu X Q and Zheng Z J. 2017. Cloud removing algorithm for the daily cloud free MODIS-based snow cover product over the Tibetan Plateau. Journal of Glaciology and Geocryology, 39(3): 515-526
  38. 38.
    Riggs G .A, Hall D K and Román M O. 2016. MODIS snow products user guide for Collection 6. Aug. 2016. [Online].
  39. 39.
    Riggs G A, Hall D K and Román M O. 2017. Overview of NASA’s MODIS and visible infrared imaging radiometer suite (VIIRS) snow-cover earth system data records. Earth System Science Data, 9(2): 765-777
  40. 40.
    Rittger K, Painter T H, Dozier J. 2013. Assessment of Methods for Mapping Snow Cover from MODIS. Advances in Water Resources, 51: 367-380
  41. 41.
    Robinson D A and Kukla G. 1985. Maximum surface albedo of seasonally snow-covered lands in the Northern Hemisphere. Journal of Applied Meteorology and Climatology, 24(5): 402-411
  42. 42.
    Robinson D A, Scharfen G, Serreze M C, Kukla G and Barry R G. 1986. Snow melt and surface albedo in the Arctic Basin. Geophysical Research Letters, 13(9): 945-948
  43. 43.
    Salomonson V V and Appel I. 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89(3): 351-360
  44. 44.
    Simic A, Fernandes R, Brown R, Romanov P and Park W. 2004. Validation of VEGETATION, MODIS, and GOES + SSM/I snow-cover products over Canada based on surface snow depth observations. Hydrological Processes, 18(6): 1089-1104
  45. 45.
    Tang Z G, Wang J, Li H Y, Yan L L and Liang J. 2013. Accuracy validation and cloud obscuration removal of MODIS fractional snow cover products over Tibetan Plateau. Remote Sensing Technology and Application, 28(3): 423-430
  46. 46.
    Tran H, Nguyen P, Ombadi M, Hsu K L, Sorooshian S and Qing X. 2019. A cloud-free MODIS snow cover dataset for the contiguous United States from 2000 to 2017. Scientific Data, 6: 180300
  47. 47.
    Wang J, Che T, Li Z, Li H Y, Hao X H, Zheng Z J, Xiao P F, Li X F, Huang X D, Zhong X Y, Dai L Y, Li H X, Ke C Q and Li L H. 2018. Investigation on snow characteristics and their distribution in China. Advances in Earth Science, 33(1): 12-26
  48. 48.
    Wang J and Li S. 2005. Influence of climate change on the runoff of melting snow in the arid mountainous areas in China. Science in China Series D: Earth Sciences, 35(7): 664-670
  49. 49.
    Wang X W, Zheng H L, Chen Y N, Liu H A, Liu L, Huang H B and Liu K. 2014. Mapping snow cover variations using a MODIS daily cloud-free snow cover product in northeast China. Journal of Applied Remote Sensing, 8(1): 084681
  50. 50.
    Wang X Y, Wang J, Li H Y and Hao X H. 2017. Combination of NDSI and NDFSI for snow cover mapping in a mountainous and forested region. Journal of Remote Sensing, 21(2): 310-317
  51. 51.
    Yang J T, Jiang L M, Ménard C B, Luojus K, Lemmetyinen J and Pulliainen J. 2015. Evaluation of snow products over the Tibetan Plateau. Hydrological Processes, 29(15): 3247-3260
  52. 52.
    Yang M X, Yao T D and Koike T. 2000. Variation features of soil temperature in Northern Tibetan Plateau. Journal of Mountain Science, 18(1): 13-17
  53. 53.
    Yu J Y, Zhang G Q, Yao T D, Xie H J, Zhang H B, Ke C Q and Yao R Z. 2016. Developing daily cloud-free snow composite products from MODIS Terra-Aqua and IMS for the Tibetan Plateau. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2171-2180
  54. 54.
    Yu X Q, Qiu Y B, Ruan Y J, Shi L J and Laba Z M. 2017. Validation and comparison of binary cloudless snow products in high Asia. Remote Sensing Technology and Application, 32(1): 37-48
  55. 55.
    Zhang H B, Zhang F, Zhang G Q, Che T, Yan W, Ye M and Ma N. 2019. Ground-based evaluation of MODIS snow cover product V6 across China: implications for the selection of NDSI threshold. Science of the Total Environment, 651: 2712-2726
  56. 56.
    Zhao X Y, Zhao Z B, Liu J H, Liu Q and Li Y K. 2022. Influences of multi-source and multi-resolution DEMs on glacier simulation in Mt. Noijin Kangsang. Quaternary Sciences, 42(4): 1181-1192
  57. 57.
    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

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website