سابِق|تال

Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

  • role： First author第一作者
  • Affiliation：

    School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China

  • Email：zhengzhubin@gnnu.edu.cn
  • Introduction：郑著彬，研究方向为水色遥感与环境遥感。E-mail：zhengzhubin@gnnu.edu.cn
ZHENG Zhubin1,  
  • Affiliation：

    School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China

    School of Geography, Nanjing Normal University, Nanjing 210023, China

ZHANG Runfei12,  
  • Affiliation：

    School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China

    School of Geography, Nanjing Normal University, Nanjing 210023, China

LI Jianzhong12,  
  • Affiliation：

    School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China

LIN Lin1,  
  • Affiliation：

    School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China

YANG Hong1

ملخص

The chlorophyll-a (Chla) concentration that refers to the content of Chla contained in per unit volume water, is a key indicator describing the eutrophication degree of lake waters. Accurate quantification of Chla concentration is of great significance for water environment assessment and water quality monitoring and has become a focus on the study of watercolor remote sensing. Orbita hyperspectral (OHS) satellite is a new generation of hyperspectral satellites launched by Zhuhai Orbita Aerospace Technology Co., Ltd. in 2018, which covers spectral range of 400~1000 nm and 32 spectral channels with both high spectral and high spatial resolution (2.5 nm and 10 m, respectively), showing great potential for inland water quality monitoring application. However, this satellite has a short operating period from launch, and the applicability of the generated images needs to be further investigated and validated.Dianchi Lake, a typical eutrophic plateau lake, was used as the study area for Chla concentration retrieval based on the OHS hyperspectral imagery. We collected in-situ spectra and Chla concentration from two cruise surveys in Dianchi Lake and acquired the satellite-ground synchronization data within one day of the OHS satellite overpass. Data from two field campaigns including 72 sampling sites were used for model calibration and validation, and ground data matched with satellite overpass including 10 sampling sites was used to further validate the retrieval results after the calibrated model was applied to the OHS imagery. We first utilized all 72 in-situ spectra to explore the relationship between all possible combinations of band ratio and Chla concentrations to seek the optimal band ratio model. Immediately after we used the spectral response function of the OHS imagery to resample the in-situ spectra to the band configuration of the OHS imagery, the OHS-based band ratio model was calibrated using 48 field-measured data according to the optimal band ratio combination of in-situ spectra, and the remaining 24 data were used to evaluate model accuracy. We further validated the retrieval results using the Chla concentrations at 10 sampling points synchronized with the OHS image after the OHS-based band ratio model was applied to the OHS image, and the spatial pattern of Chla concentration in Dianchi Lake was revealed.The band ratio Rrs(716)/Rrs(595) had the highest correlation with Chla concentration in terms of the in-situ spectra with R2=0.819, and the corresponding OHS-based band ratio model (B17/B9) was suitable for remote sensing retrieval of Chla concentration in Dianchi Lake with R2 of 0.804, the root-mean-square error (RMSE) of 6.99 μg/L and the mean absolute percentage error (MAPE) of 6.32%. The retrieval results of the OHS-based band ratio model applied to the OHS image and the spatial pattern of Chla concentration were reasonable with acceptable errors (RMSE=12.47 μg/L, MAPE=22.53%). The spatial pattern of Chla concentration in Dianchi Lake showed a decreasing trend from the lakeshore to the center of the lake on April 2, 2019, the northeast and southeast decrease fitted a power function, whereas the northwest decrease described a linear function. The pixel reflectance of the nearshore waters may be higher than that of the normal waters due to the land adjacency effect, which may lead to a high concentration of retrieved Chla along the coast. In the OHS imagery of Dianchi Lake, four nearshore water pixels could be easily influenced by the land adjacency effect, so these four pixels needed to be masked to eliminate the influence. In addition, compared with the existing Chla concentration retrieval algorithms, the band ratio model (B17/B9) proposed in this study improved the retrieval accuracy of Chla concentration.In conclusion, the OHS-based band ratio model works efficiently and reliably for retrieving Chla concentration in Dianchi Lake. OHS hyperspectral data show great potential in terms of accurate retrieval of Chla concentration for inland waters, providing a new means for remote sensing monitoring of Chla concentration. However, whether the OHS-based band ratio model developed in this study applies to other water bodies with different optical properties still needs to be further investigated and tested. In future studies, the performance of the model will be further examined by collecting more field data in different lakes.

مفهوم

Orbita hyperspectral imagery;chlorophyll-a;Dianchi Lake;Land adjacency effect

References

  1. 1.
    Beck R, Zhan S G, Liu H X, Tong S, Yang B, Xu M, Ye Z X, Huang Y, Shu S, Wu Q S, Wang S J, Berling K, Murray A, Emery E, Reif M, Harwood J, Young J, Nietch C, Macke D, Martin M, Stillings G, Stump R and Su H B. 2016. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing of Environment, 178: 15-30
  2. 2.
    Bi S, Li Y M, Lü H, Zhu L, Mu M, Lei S H, Xu J, Wen S and Ding X L. 2018. Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data. Journal of Lake Sciences, 30(3): 701-712
  3. 3.
    Brando V E, Anstee J M, Wettle M, Dekker A G, Phinn S R and Roelfsema C. 2009. A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment, 113(4): 755-770
  4. 4.
    Chen Y W, Chen K N and Hu Y H. 2006. Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method. Journal of Lake Sciences, 18(5): 550-552
  5. 5.
    Du C, Wang S X, Zhou Y and Yan F L. 2009. Remote chlorophyll a retrieval in Taihu lake by three-band model using Hyperion hyperspectral data. Environmental Science, 30(10): 2904-2910
  6. 6.
    Feng L. 2021. Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing. Journal of Lake Sciences, 33(3): 647-652
  7. 7.
    Gilerson A A, Gitelson A A, Zhou J, Gurlin D, Moses W, Ioannou I and Ahmed S A. 2010. Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Optics Express, 18(23): 24109-24125
  8. 8.
    Gordon H R and Morel A Y. 1983. Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review. New York: Springer-Verlag: 1-114.
  9. 9.
    Guo Y L, Li Y M, Li Y, Lü H, Liu G, Wang X D and Zhang S M. 2015. A three band chlorophyll-a concentration estimation model based on GOCI imagery. Environmental Science, 36(9): 3175-3185
  10. 10.
    Guo Y L, Huang C C, Li Y M, Du C G, Shi L F, Li Y, Chen W Q, Wei H J, Cai E X and Ji G X. 2022. Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing. Remote Sensing of Environment, 276: 113045
  11. 11.
    Gurlin D, Gitelson A A and Moses W J. 2011. Remote estimation of chl-a concentration in turbid productive waters — return to a simple two-band NIR-red model?. Remote Sensing of Environment, 115(12): 3479-3490
  12. 12.
    Härmä P, Vepsäläinen J, Hannonen T, Pyhälahti T, Kämäri J, Kallio K, Eloheimo K and Koponen S. 2001. Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland. Science of the Total Environment, 268(1/3): 107-121
  13. 13.
    Huang C C, Wang X L, Yang H, Li Y M, Wang Y H, Chen X and Xu L J. 2014. Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009. Science of the Total Environment, 485-486: 1-11
  14. 14.
    Kong F X and Gao G. 2005. Hypothesis on cyanobacteria bloom-forming mechanism in large shallow eutrophic lakes. Acta Ecologica Sinica, 25(3): 589-595
  15. 15.
    Kuchinke C P, Gordon H R, Harding Jr L W and Voss K J. 2009. Spectral optimization for constituent retrieval in Case 2 waters Ⅱ: validation study in the Chesapeake Bay. Remote Sensing of Environment, 113(3): 610-621
  16. 16.
    Kutser T, Herlevi A, Kallio K and Arst H. 2001. A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes. Science of the Total Environment, 268(1/3): 47-58
  17. 17.
    Li J, Chen X L, Tian L Q, Huang J and Feng L. 2015. Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 145-156
  18. 18.
    Li Y, Li Y M, Lü H, Zhu L, Wu C Q, Du C G and Wang S. 2014. Muti-model collaborative retrieval of chlorophyll a in Taihu lake based on data assimilation. Environmental Science, 35(9): 3389-3396
  19. 19.
    Li Y M, Huang J Z, Wei Y C and Lu W N. 2006. Inversing chlorophyll concentration of Taihu lake by analytic model. Journal of Remote Sensing, 10(2): 169-175
  20. 20.
    Li Z H, Chen Z Z, Wang L Y, Sun D Y, Zhao B R and Wang S Q. 2021. Remote sensing inversion of concentration of phytoplankton chlorophyll and carotenoid from GOCI measurements in coastal waters. Acta Optica Sinica, 41(2): 7-17
  21. 21.
    Liu D, Pan D L, Bai Y, He X Q, Wang D F, Wei J A and Zhang L. 2015. Remote sensing observation of particulate organic carbon in the Pearl River estuary. Remote Sensing, 7(7): 8683-8704
  22. 22.
    Liu G, Li L, Song K S, Li Y M, Lyu H, Wen Z D, Fang C, Bi S, Sun X P, Wang Z M, Cao Z G, Shang Y X, Yu G L, Zheng Z B, Huang C C, Xu Y F and Shi K. 2020. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters. Remote Sensing of Environment, 239: 111648
  23. 23.
    Liu G, Li Y M, Lü H, Mu M, Lei S H, Wen S, Bi S and Ding X L. 2017. Remote sensing of chlorophyll-a concentration in Lake Hongze using long time series MERIS observations. Environmental Science, 38(9): 3645-3656
  24. 24.
    Liu G, Simis S G H, Li L, Wang Q, Li Y M, Song K S, Lyu H, Zheng Z B and Shi K. 2018. A four-band semi-analytical model for estimating phycocyanin in Inland waters from simulated MERIS and OLCI data. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1374-1385
  25. 25.
    Liu Q, Pan D L, Bai Y, Wu K, Authur Chen C T, Liu Z L and Zhang L. 2014. Estimating dissolved organic carbon inventories in the East China Sea using remote-sensing data. Journal of Geophysical Research: Oceans, 119(10): 6557-6574
  26. 26.
    Liu Y, Li J S, Xiao C C, Zhang F F and Wang S L. 2022. Inland water chlorophyll-a retrieval based on ZY-1 02D satellite hyperspectral observations. National Remote Sensing Bulletin, 26(1): 168-178
  27. 27.
    Lyu H, Li X J, Wang Y N, Jin Q, Cao K, Wang Q and Li Y M. 2015. Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes. Science of the Total Environment, 530-531: 373-382
  28. 28.
    Ma R H, Tang J W, Duan H T and Pan D L. 2009. Progress in lake water color remote sensing. Journal of Lake Sciences, 21(2): 143-158
  29. 29.
    Mobley C D. 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36): 7442-7455
  30. 30.
    Morel A and Prieur L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22(4): 709-722
  31. 31.
    Morel A Y and Gordon H R. 1980. Report of the working group on water color. Boundary-Layer Meteorology, 18(3): 343-355
  32. 32.
    Mu M, Li Y M, Bi S, Lyu H, Xu J, Lei S H, Miao S, Zeng S, Zheng Z B and Du C G. 2021. Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences. Ecological Indicators, 124: 107416
  33. 33.
    Neil C, Spyrakos E, Hunter P D and Tyler A N. 2019. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sensing of Environment, 229: 159-178
  34. 34.
    Paerl H W and Paul V J. 2012. Climate change: links to global expansion of harmful cyanobacteria. Water Research, 46(5): 1349-1363
  35. 35.
    Shen M, Duan H T, Cao Z G, Xue K and Ma R H. 2017. Remote sensing estimation algorithm of diffuse attenuation coefficient applicable to different satellite data in Lake Taihu, China. Journal of Lake Sciences, 29(6): 1473-1484
  36. 36.
    Smith M E, Lain L R and Bernard S. 2018. An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters. Remote Sensing of Environment, 215: 217-227
  37. 37.
    Sun D Y, Li Y M, Huang J Z, Le C F, Gong S Q and Wu L. 2008. Spectral absorption characteristics and slope coefficients analysis of in-water constituents in Meiliang Bay of Lake Taihu in the summer season. Acta Ecologica Sinica, 28(2): 749-760
  38. 38.
    Tang J W, Tian G L, Wang X Y, Wang X M and Song Q J. 2004. The methods of water spectra measurement and analysis Ⅰ: above-water method. Journal of Remote Sensing, 8(1): 37-44
  39. 39.
    Vincent R K, Qin X M, McKay R M L, Miner J, Czajkowski K, Savino J and Bridgeman T. 2004. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment, 89(3): 381-392
  40. 40.
    Wang J F, Chen J A, Chen Q, Jin Z X and Zeng H X. 2021. Biogeochemical cycling of phosphorus in deep-water reservoirs. Quaternary Sciences, 41(4): 1192-1205
  41. 41.
    Yang W, Matsushita B K, Chen J, Fukushima T and Ma R H. 2010. An enhanced three-band index for estimating chlorophyll-a in turbid case-II waters: case studies of Lake Kasumigaura, Japan, and Lake Dianchi, China. IEEE Geoscience and Remote Sensing Letters, 7(4): 655-659
  42. 42.
    Yin Z Y, Li J S, Fan H S, Gao M and Xie Y. 2021. Preliminary study on water quality parameter inversion for the Yuqiao reservoir based on Zhuhai-1 hyperspectral satellite data. Spectroscopy and Spectral Analysis, 41(2): 494-498
  43. 43.
    Zhang R F, Zheng Z B, Liu G, Du C G, Du C, Lei S H, Xu Y F, Xu J, Mu M, Bi S and Li J Z. 2021. Simulation and assessment of the capabilities of Orbita Hyperspectral (OHS) imagery for remotely monitoring chlorophyll-a in eutrophic plateau lakes. Remote Sensing, 13(14): 2821
  44. 44.
    Zheng Z B, Li Y M, Guo Y L, Xu Y F, Liu G and Du C G. 2015. Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China. Remote Sensing, 7(10): 13975-13999

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