[1] [1] GOWER J, KING S, STATHAM S, et al. The Malaspina Dragon: a newly-discovered pattern of the early spring bloom in the Strait of Georgia, British Columbia, Canada［J］. Progress in Oceanography, 2013, 115: 181-188.

[2] [2] XING Q G, TOSI L, BRAGA F, et al. Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data［J］. Natural Hazards, 2015, 78(1): 7-21.

[3] [3] CUI T W, ZHANG J, SUN L E, et al. Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation［J］. International Journal of Remote Sensing, 2012, 33(17/18): 5513-5527.

[4] [4] XIAO Y, ZHANG J, CUI T W. High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea, China［J］. International Journal of Remote Sensing, 2017, 38(5/6): 1626-1641.

[5] [5] XING Q G, WU L L, TIAN L Q, et al. Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign［J］. Marine Pollution Bulletin, 2018, 133: 150-156.

[6] [6] XING Q G, HU C M. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique［J］. Remote Sensing of Environment, 2016, 178: 113-126.

[7] [7] XU Q, ZHANG H, CHENG Y, et al. Monitoring and tracking the green tide in the Yellow Sea with satellite imagery and trajectory model［J］.?IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(11): 5172-5181.

[8] [8] GOWER J, YOUNG E, KING S. Satellite images suggest a new Sargassum source region in 2011［J］. Remote Sensing Letters, 2013, 4(8): 764-773.

[9] [9] HU C M, FENG L, HARDY R F, et al. Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae［J］. Remote Sensing of Environment, 2015, 167: 229-246.

[10] [10] XING Q G, GUO R H, WU L L, et al. High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in winter in the Yellow Sea［J］. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1815-1819.

[11] [11] XING Q G, ZHENG X Y, SHI P, et al. Monitoring “green tide” in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images［J］. Spectroscopy and Spectral Analysis, 2011, 31(6): 1644-1647.

[12] [12] YUE J B, YANG G J, TIAN Q J, et al. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 226-244.

[13] [13] CHENG K H, CHAN S N, LEE J H W. Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs)［J］. Marine Pollution Bulletin, 2020, 152: 110889.

[14] [14] LAURENS L, WOLFRUM E J. High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis［J］. Journal of Agricultural & Food Chemistry, 2013, 61(50):12307-12314.

[15] [15] HU C. A novel ocean color index to detect floating algae in the global oceans［J］. Remote Sensing of Environment, 2009, 113(10): 2118-2129.

[16] [16] SON Y B, MIN J E, RYU J H. Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using geostationary ocean color imager (GOCI) data［J］. Ocean Science Journal, 2012, 47(3): 359-375.

[17] [17] ZHANG X L, ZHANG F, QI Y X, et al. New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)［J］. International Journal of Applied Earth Observation and Geoinformation, 2019, 78: 215-226.

[18] [18] KAZMI W, GARCIA-RUIZ F J, NIELSEN J, et al. Detecting creeping thistle in sugar beet fields using vegetation indices［J］. Computers and Electronics in Agriculture, 2015, 112: 10-19.

[19] [19] LU J S, CHENG D L, GENG C M, et al. Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize［J］. Biosystems Engineering, 2021, 202: 42-54.

[20] [20] ZHANG Y C, MA R H, HU M Q, et al. Combining citizen science and land use data to identify drivers of eutrophication in the Huangpu River system［J］. Science of the Total Environment, 2017, 584: 651-664.

[21] [21] YAN Q S, ZHU Y, ZHOU Y L, et al. Enhancing image visuality by multi-exposure fusion［J］. Pattern Recognition Letters, 2019, 127: 66-75.

[22] [22] XU Y D, SUN B B. Color-compensated multi-scale exposure fusion based on physical features［J］. Optik-International Journal for Light and Electron Optics, 2020, 223: 165494.

[23] [23] CLEDAT E, RUFENER M, CUCCI D A. Compensating over- and underexposure in optical target pose determination［J］. Pattern Recognition, 2021, 116: 107930.

[24] [24] HU S, YANG H, ZHANG J H, et al. Small-scale early aggregation of green tide macroalgae observed on the Subei Bank, Yellow Sea［J］. Marine Pollution Bulletin, 2014, 81(1): 166-173.

[25] [25] WANG Z L, XIAO J, FAN S L, et al. Who made the world’s largest green tide in China?—an integrated study on the initiation and early development of the green tide in Yellow Sea［J］. Limnology and Oceanography, 2015, 60(4): 1105-1117.