سابِق|تال

Retrieving sparse non-photosynthetic vegetation fractional cover by Sentinel-1 and Sentinel-2

  • role： First author第一作者
  • Affiliation：

    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

  • Email：jijingqiang@163.com
  • Introduction：姬翠翠，研究方向为生态环境遥感。 E-mail： jijingqiang@163.com
JI Cuicui12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • Email：yixia.luo@qq.com
  • Introduction：骆义峡，研究方向为定量遥感的应用。 E-mail： yixia.luo@qq.com
LUO Yixia1*,  
  • Affiliation：

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Xiaosong23,  
  • Affiliation：

    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

XU Jinhong1,  
  • Affiliation：

    Tourism school, Lanzhou University of Arts and Science, Lanzhou 730000, China

YANG Xuemei4,  
  • Affiliation：

    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

CHEN Maolin1

ملخص

Quantitatively estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation (NPV), and bare soil plays an important role in establishing carbon dynamics models. Accurately obtaining the fractional cover of NPV provides the important information for the study of land desertification and vegetation transformation mechanisms. Although some progress has been made in obtaining NPV fractional cover (fNPV) by optical remote sensing in previous studies, many interfering factors and difficulties are still present. We will attempt to combine microwave and optical remote sensing information to obtain NPV fractional cover for further improving the accuracy of the fractional cover estimation of NPV.In this study, we used Minqin County in Gansu Province as the research area, and we employed Sentinel-1B IW GRD and Sentinel-2A as data sources. The experiments employed the control variable method with the linear index model and the random forest regression (RFR) model to conduct the fractional cover estimation of NPV by using microwave and optical remote sensing data. Then, the estimated endmember fractions were validated with reference to fraction measurements. In addition, the Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RMSE%) were employed as indicators to evaluate the inversion accuracy.Results show that (1) using cooperative Sentinel-1 and Sentinel-2 remote sensing data to estimate the fractional cover of NPV can effectively improve the estimated accuracy compared with using Sentinel-2 data alone. (2) The RFR model is an effective method for the fractional cover estimation of sparse NPV, and its estimation accuracy is higher than that of the linear index model. The validation RMSE of the random forest model and the estimated fNPV of the linear index model are 0.0149 and 0.0153, respectively. Obviously, the accuracy of fNPV estimation increases by 1.4% when using the RFR model instead of the linear index model. (3) The VH and VV polarization bands of Sentinel-1 data can effectively detect the characteristics of NPV. Especially, VH band is more sensitive to NPV, and its estimation accuracy is improved by 5.1% compared with that of VV band. (4) The accuracy of fNPV estimation can be improved when soil index is considered in each model, which illustrates that incorporating the soil characteristic information in the models is important for NPV extraction.Overall, the combination of Sentinel-1 and Sentinel-2 remote sensing data can effectively improve the accuracy of the fractional cover estimation of NPV by employing the RFR model. VV and VH polarization modes are sensitive to NPV vegetation detection, especially VH polarization mode. The accuracy of NPV extraction can be further improved by considering the soil index, which reflects the soil characteristics. Therefore, the combination of microwave and optical remote sensing data is an effective method to improve the accuracy of fNPV estimation. Incorporating polarization information with vegetation structure information and soil parameters with soil characteristic information is important for improving the accuracy of the fractional cover estimation of NPV.

مفهوم

non-photosynthetic vegetation;Sentinel-1;Sentinel-2;linear index model;random forest regression model;VV and VH polarization;Minqin County in Gansu province

References

  1. 1.
    Adams J B, Smith M O and Johnson P E. 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research: Solid Earth, 91(B8): 8098-8112
  2. 2.
    Arias M, Campo-Bescós M Á and Álvarez-Mozos J. 2020. Crop classification based on temporal signatures of sentinel-1 observations over Navarre Province, Spain. Remote Sensing, 12(2): 278
  3. 3.
    Asner G P and Lobell D B. 2000. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sensing of Environment, 74(1): 99-112
  4. 4.
    Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31
  5. 5.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  6. 6.
    Cao X, Chen J, Matsushita B, Imura H. 2010. Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area. International Journal of Remote Sensing, 31(6): 1589-1604
  7. 7.
    Chen X X and Vierling L. 2006. Spectral mixture analyses of hyperspectral data acquired using a tethered balloon. Remote Sensing of Environment, 103(3): 338-350
  8. 8.
    Clasen A, Somers B, Pipkins K, Tits L, Segl K, Brell M, Kleinschmit B, Spengler D, Lausch A and Förster M. 2015. Spectral unmixing of forest crown components at close range, airborne and simulated sentinel-2 and EnMAP spectral imaging scale. Remote Sensing, 7(11): 15361-15387
  9. 9.
    Collins L, Griffioen P, Newell G and Mellor A. 2018. The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment, 216: 374-384
  10. 10.
    DAI S M, QIU G Y and ZHAO M. 2008. Study on land desertification and its prevention and control measures in the Minqin oasis in Gansu Province. Arid Zone Research, 25(3): 319-324
  11. 11.
    Daughtry C S T, Mcmurtrey III J E, Chappelle E W, Dulaney W P, Irons J R and Satterwhite M B. 1995. Potential for discriminating crop residues from soil by reflectance and fluorescence. Agronomy Journal, 87(2): 165-171
  12. 12.
    Dong G W, Yang J, Peng Y N, Wang C and Zhang H. 2003. Forest characteristic detection with Pol-SAR. Journal of Tsinghua University (Science & Technology), 43(7): 953-956
  13. 13.
    Erinjery J J, Singh M and Kent R. 2018. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sensing of Environment, 216: 345-354
  14. 14.
    Fan W Y, Hu B X, Miller J and Li M Z. 2009. Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data. International Journal of Remote Sensing, 30(11): 2951-2962
  15. 15.
    Garrigues S, Allard D, Baret F and Weiss M. 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105(4): 286-298
  16. 16.
    Gašparović M and Dobrinić D. 2020. Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal sentinel-1 imagery. Remote Sensing, 12(12): 1952
  17. 17.
    Guerschman J P, Hill M J, Renzullo L J, Barrett D J, Marks A S and Botha E J. 2009. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment, 113(5): 928-945
  18. 18.
    Heinz D C and Chang C I. 2001. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(3): 529-545
  19. 19.
    Ji C C, Jia Y H, Li X S and Wang J Y. 2016. Research on linear and nonlinear spectral mixture models for estimating vegetation fractional cover of nitraria bushes. Journal of Remote Sensing, 20(6): 1402-1412
  20. 20.
    Ji C C. 2018. Research on Muti-Scale Spectral Mixture Analysis Method for Sparse Photosynthetic/Non-Photosynthetic Vegetation in Arid Area. Wuhan: Wuhan University
  21. 21.
    Ji C C, Li X S, Wei H D and Li S K. 2020. Comparison of different multispectral sensors for photosynthetic and non-photosynthetic vegetation-fraction retrieval. Remote Sensing, 12(1): 115
  22. 22.
    Jiapaer G, Chen X and Bao A M. 2011. A comparison of methods for estimating fractional vegetation cover in arid regions. Agricultural and Forest Meteorology, 151(12): 1698-1710
  23. 23.
    Kim Y and van Zyl J J. 2009. A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing, 47(8): 2519-2527
  24. 24.
    Kim Y H, OH J H and Kim Y I. 2015. Development of a fusion vegetation index using full-PolSAR and multispectral data. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(6): 547-555
  25. 25.
    Kumar P, Prasad R, Gupta D K, Mishra V N, Vishwakarma A K, Yadav V P, Bala R, Choudhary A and Avtar R. 2018. Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data. Geocarto International, 33(9): 942-956
  26. 26.
    Lary D J, Alavi A H, Gandomi A H and Walker A L. 2016. Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1): 3-10
  27. 27.
    Lehnert L, Meyer H, Thies B, Reudenbach C and Bendix J. 2014. Monitoring plant cover on the Tibetan Plateau: a multi-scale remote sensing based approach//Proceedings of the EGU General Assembly 2014. Vienna, Austria: [s.n.]
  28. 28.
    Li T, Li X S and Li F. 2015. Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 hyperion data. Acta Ecologica Sinica, 35(11): 3643-3652
  29. 29.
    Li X S, Zheng G X, Wang J Y, Ji C C, Sun B and Gao Z H. 2016. Comparison of methods for estimating fractional cover of photosynthetic and non-photosynthetic vegetation in the otindag sandy land using GF-1 wide-field view data. Remote Sensing, 8(10): 800
  30. 30.
    Liaw A and Wiener M. 2001. Classification and regression by RandomForest. R News, 2-3: 18-22
  31. 31.
    Luo Y X, Ji C C, Li X S, Xu J H and Yang X M. 2022. Vegetation index model for estimating sparse photosynthetic/non-photosynthetic vegetation fractional cover in arid zone. Remote Sensing Information， 37(3): 57-64
  32. 32.
    Macelloni G, Paloscia S, Pampaloni P, Marliani F and Gai M. 2001. The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops. IEEE Transactions on Geoscience and Remote Sensing, 39(4): 873-884
  33. 33.
    Mandal D, Kumar V, Bhattacharya A, Rao Y S, Siqueira P and Bera S. 2018. Sen4Rice: a processing chain for differentiating early and late transplanted rice using time-series sentinel-1 SAR data with Google earth engine. IEEE Geoscience and Remote Sensing Letters, 15(12): 1947-1951
  34. 34.
    Meng R, Wu J, Schwager K L, Zhao F, Dennison P E, Cook B D, Brewster K, Green T M and Serbin S P. 2017. Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem. Remote Sensing of Environment, 191: 95-109
  35. 35.
    Mitsopoulos I, Chrysafi I, Bountis D and Mallinis G. 2019. Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem. Journal of Environmental Management, 235: 266-275
  36. 36.
    Montorio R, Pérez-Cabello F, Alves D B and García-Martín A. 2020. Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests. Remote Sensing of Environment, 249: 112025
  37. 37.
    Okin G S. 2007. Relative spectral mixture analysis — A multitemporal index of total vegetation cover. Remote Sensing of Environment, 106(4): 467-479
  38. 38.
    Parks S A, Holsinger L M, Koontz M J, Collins L, Whitman E, Parisien M A, Loehman R A, Barnes J L, Bourdon J F, Boucher J, Boucher Y, Caprio A C, Collingwood A, Hall R J, Park J, Saperstein L B, Smetanka C, Smith R J and Soverel N. 2019. Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing, 11(14): 1735
  39. 39.
    Pope K O, Rey-Benayas J M and Paris J F. 1994. Radar remote sensing of forest and wetland ecosystems in the Central American tropics. Remote Sensing of Environment, 48(2): 205-219
  40. 40.
    Quintano C, Fernández-Manso A and Roberts D A. 2020. Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm. Remote Sensing of Environment, 244: 111815
  41. 41.
    Reynolds J F, Smith D M S, Lambin E F, Turner II B L, Mortimore M, Batterbury S P J, Downing T E, Dowlatabadi H, Fernandez R J, Herrick J E, Huber-Sannwald E, Jiang H, Leemans R, Lynam T, Maestre F T, Ayarza M and Walker B. 2007. Global desertification: building a science for dryland development. Science, 316(5826): 847-851
  42. 42.
    Roberts D A, Gardner M, Church R, Ustin S, Scheer G and Green R O. 1998. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3): 267-279
  43. 43.
    Serbin G, Hunt E R, Daughtry C S T, McCarty G W and Doraiswamy P C. 2009. An improved ASTER index for remote sensing of crop residue. Remote Sensing, 1(4): 971-991
  44. 44.
    Shao J, Li X S, Yang J T and Liu D C. 2021. Study on large scale grassland shrub monitoring based on optical and radar remote sensing. Journal of Arid Land Resources and Environment, 35(2): 130-135
  45. 45.
    Tian H F, Wu M Q, Niu Z, Wang C Y and Zhao X. 2015. Dryland crops recognition under complex planting structure based on Radarsat-2 images. Transactions of the Chinese Society of Agricultural Engineering, 31(23): 154-159
  46. 46.
    Tomppo E, Antropov O and Praks J. 2019. Cropland classification using sentinel-1 time series: methodological performance and prediction uncertainty assessment. Remote Sensing, 11(21): 2480
  47. 47.
    Vreugdenhil M, Wagner W, Bauer-Marschallinger B, Pfeil I, Teubner I, Rüdiger C and Strauss P. 2018. Sensitivity of sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sensing, 10(9): 1396
  48. 48.
    Whelen T and Siqueira P. 2018. Time-series classification of Sentinel-1 agricultural data over North Dakota. Remote Sensing Letters, 9(5): 411-420
  49. 49.
    Zheng G X, Li X S, Zhang K X and Wang J Y. 2016. Spectral mixing mechanism analysis of photosynthetic/non-photosynthetic vegetation and bared soil mixture in the hunshandake (Otindag) sandy land. Spectroscopy and Spectral Analysis, 36(4): 1063-1068

قراءة النص الكامل

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