1. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China 3. China Resources Satellite Application Center, Beijing 100094, China 4. Urban and Rural Planning Management Center of the Ministry of Housing and Urban Rural Development of the People’s Republic of China, Beijing 100835, China
Different spatial resolutions, spectral resolutions and radiation resolutions influence the accurate estimation of remotely sensed chlorophyll a concentration of water. In this study, GF-1 WFV and Landsat8 OLI imagery was used as objects, and the cooperative methods of single-band substitution, single-band fusion and three-band fusion were respectively used to analyze dominant characteristics of spatial resolution and spectral resolution for improving the precision of chlorophyll a concentration inversion in multi-source remote sensing data. On such a basis, the optimal combination of GF-1 WFV and Landsat8 OLI data was further explored so as to improve the inversion accuracy of chlorophyll a concentration and promote the application of domestic high-resolution satellite GF-1 imagery. The results show that, in the GF-1 WFV and Landsat8 OLI cooperative inversion process, the spectral resolution and radiation resolution of near infrared band dominate the characteristics, and the influence of the near infrared band spectrum resolution enhancement is more favorable for improving the inversion accuracy of chlorophyll a concentration, whereas in the blue and red bands, the higher the spatial resolution, the higher the accuracy of chlorophyll a concentration inversion. The combination factors of GF-1 WFV and Landsat8 OLI optimal chlorophyll a concentration synergistic inversion spectral index are as follows: Landsat8 OLI near infrared band, GF-1 WFV and Landsat8 OLI fused red band, GF-1 WFV and Landsat8 OLI fused blue band. The GF-1 WFV and Landsat8 OLI separate inversion accuracy with average relative errors of 41.93% and 38.37%, respectively. After optimization, the average relative error of synergistic inversion is reduced to 17.35%. This study preliminarily explored the spectral resolution and spatial resolution of GF-1 WFV and Landsat8 OLI imagery of water chlorophyll a concentration cooperative inversion dominant characteristics and the optimal coordinated way. The authors are in the hope of providing reference for the channel design of the following domestic satellites and the cooperative inversion of multi-source satellites.
封红娥, 李家国, 朱云芳, 韩启金, 张宁, 田淑芳. GF-1与Landsat8水体叶绿素a浓度协同反演——以太湖为例[J]. 国土资源遥感, 2019, 31(4): 182-189.
Honge FENG, Jiaguo LI, Yunfang ZHU, Qijin HAN, Ning ZHANG, Shufang TIAN. Synergistic inversion method of chlorophyll a concentration in GF-1 and Landsat8 imagery: A case study of the Taihu Lake. Remote Sensing for Land & Resources, 2019, 31(4): 182-189.
Ma R H, Dai J F . Quantitative estimation of chlorophyll-a and total suspended matter concentration with Landsat ETM based on field spectral features of Lake Taihu[J]. Journal of Lake Sciences, 2005,17(2):97-103.
[2]
Zimba P V, Gitelson A . Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems:Model tuning and accuracy optimization[J]. Aquaculture, 2006,256(1-4):272-286.
[3]
Dall'Olmo G, Gitelson A A . Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:Experimental results[J]. Applied Optics, 2005,44(3):412-422.
Xu W J, Yang B, Tian L , et al. Retrieval of chlorophyll-a concentration by using MODIS data in Hebei Sea Area[J]. Remote Sensing for Land and Resources, 2012,24(4):152-156.doi: 10.6046/gtzyyg.2012.04.25.
[5]
Elalem A, Chokmani K, Laurion I . Comparative analysis of four models to estimate chlorophyll-a concentration in Case-2 waters using moderate resolution imaging spectroradiometer (MODIS) imagery[J]. Remote Sensing, 2012,4(8):2373-2400.
Zhang M H, Su H, Ji B W . Retrieving nearshore chlorophyll-a concentration using MODIS time-series images in the Fujian Province(China)[J]. Acta Scientiae Circumstantiae, 2018,38(12):4831-4839.
Kuang D, Han X Z, Liu X . Quantitative estimation of Taihu chlorophyll-a concentration using HJ-1A and 1B CCD imagery[J]. China Environmental Science, 2010,30(9):1268-1273.
Tang J W, Tian G L, Wang X Y , et al. The methods of water spectra measurement and analysis Ⅰ:Above-water method[J]. Journal of Remote Sensing, 2004,8(1):37-44.
Chen Y W, Chen K N, Hu Y H . Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method[J]. Journal of Lake Sciences, 2006,18(5):550-552.
[10]
Lathrop R G, Lillesand T M, Yandell B S . Testing the utility of simple multi-data Thematic Mapper calibration algorithms for monitoring turbid inland water[J]. International Journal of Remote Sensing, 1991,12(10):2045-2063.
[11]
Kahru M, Michell B G, Diaz A , et al. MODIS detects a devastating algal bloom in Paracas Bay,Peru[J]. EOS Transactions American Geophysical Union, 2004,85(45):465-472.
Jiang X C, Deng Z D, Wu G Y , et al. Comparison on fusion algorithms of Landsat 8 OLI multi-spectral and panchromatic images[J]. Information Technology and Network Security, 2018,37(8):31-35.
[13]
Antoine D, Morel A . A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument):Principle and implementation for atmospheres carrying various aerosols including absorbing ones[J]. International Journal of Remote Sensing, 1999,20(9):1875-1916.
[14]
Gitelson. A . The peak near 700 nm on radiance spectral of algae and water:Relationships of its magnitude and position with chlorophyll concentration[J]. International Journal of Remote Sensing, 1992,13(17):3367-3373.
[15]
Gitelson A A, Merzlyak M N . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. Journal of Plant Physiology, 2003,160(3):271-282.
[16]
Gons H J . Optical teledetection of chlorophyll a in turbid inland waters[J]. Environmental Science and Technology, 1999,33(7):1127-1132.
Shu X Z, Ying Q, Kuang D B . Relationship between algal chlorophyll concentration and spectral reflectance of inland water[J]. Journal of Remote Sensing, 2000,4(l):41-45.
Wang S S, Li Y M, Wang Y B . Suitability of the retrieval models for estimating chlorophyll concentration in Lake Taihu[J]. Journal of Lake Sciences, 2015,27(1):150-162.
2019, Vol. 31 
), 朱云芳2, 韩启金3, 张宁4, 田淑芳1
