Publication Information

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{'为确定适合太湖水体叶绿素的反演算法,为同类卫星数据的建模和应用提供参考,本文根据太湖2007年11月、2009年4月和2011年8月实测水质参数以及同步光谱数据,结合水色遥感传感器MODIS、MERIS、GOCI及我国自主发射的HJ-1号卫星CCD传感器波段参数,基于差值模型、比值模型、三波段模型及APPEL模型,分别建立太湖水体叶绿素浓度反演模型,并分析模型的适宜性.结果显示,基于不同传感器数据APPEL模型的决定系数为0.7308~0.8107,模型相对误差为15%~24%,均方根误差为21%~32%;三波段模型基于不同传感器数据拟合的决定系数为0.6014~0.7610,相对误差为28%~36%,相对均方根误差为39%~46%;差值模型决定系数为0.4954~0.7244,相对误差为39%~53%,相对均方根误差为51%~72%;比值模型决定系数为0.4918~0.7098,相对误差为41%~55%,相对均方根误差为56%~75%.相比较而言,APPEL模型的稳定性较强,适合于不同传感器数据的太湖水体叶绿素浓度的反演.此外,相应不同传感器波段位置、波段宽度对模型反演的精度和稳定性的影响也不同,当波段位置接近叶绿素特征波长时,较窄的波宽有利于模型精度的提高,波段位置和叶绿素浓度特征波长相差较大时,合理增加波谱范围有利于叶绿素特征信息的获取.;In': [0], 'order': [1], 'to': [2, 41, 261], 'determine': [3], 'the': [4, 21, 24, 27, 30, 33, 47, 60, 66, 81, 85, 89, 98, 102, 111, 125, 133, 142, 163, 172, 186, 194, 203, 215, 226, 249, 256, 262, 273, 276, 280, 287, 290, 293], 'most': [5], 'suitable': [6, 224], 'retrieval': [7, 229, 250], 'model': [8, 35, 38, 91, 127, 188, 217, 251], 'for': [9, 20, 97, 225, 233, 252, 272], 'estimating': [10, 253], 'chlorophyll': [11, 43, 227, 254], 'concentration': [12, 44, 228], 'in': [13, 70], 'Lake': [14, 231], 'Taihu': [15, 232], 'and': [16, 36, 53, 65, 75, 95, 108, 110, 119, 131, 139, 141, 150, 161, 169, 171, 180, 192, 200, 202, 211, 222, 242], 'provide': [17], 'a': [18, 219], 'reference': [19], 'application': [22], 'of': [23, 49, 80, 88, 124, 155, 185, 230, 265, 275, 289], 'satellite': [25, 100], 'data,': [26, 101], 'difference': [28], 'model,': [29, 32], 'ratio': [31, 187], 'three-band': [34, 126], 'APPEL': [37, 90, 216], 'were': [39], 'built': [40], 'estimate': [42], 'based': [45], 'on': [46, 248], 'data': [48, 69], 'MODIS,': [50], 'MERIS,': [51], 'GOCI': [52], 'HJ-1': [54], 'CCD': [55], 'sensor.': [56], 'The': [57, 78, 121, 152, 182], 'dataset': [58], 'included': [59], 'measured': [61], 'water': [62], 'quality': [63], 'parameters': [64], 'synchronous': [67], 'spectra': [68], 'November': [71], '2007,': [72], 'April': [73], '2009': [74], 'August': [76], '2011.': [77], 'results': [79], 'analysis': [82], 'showed': [83, 218], 'that': [84], 'decision': [86, 122, 153, 183], 'coefficient': [87, 123, 154, 184], 'was': [92, 105, 116, 128, 136, 147, 158, 166, 177, 189, 197, 208, 223, 259, 270, 283], 'between': [93, 106, 117, 129, 137, 148, 159, 167, 178, 190, 198, 209], '0.7308': [94], '0.8107': [96], 'different': [99, 156, 234, 239, 246], 'relative': [103, 134, 164, 195], 'error': [104, 115, 135, 146, 165, 176, 196, 207], '15%': [107], '24%,': [109], 'root': [112, 143, 173, 204], 'mean': [113, 144, 174, 205], 'square': [114, 145, 175, 206], '21%': [118], '32%;': [120], '0.6014': [130], '0.7610,': [132], '28%': [138], '36%,': [140], '39%': [149, 168], '46%;': [151], 'models': [157], '0.4954': [160], '0.7244,': [162], '53%,': [170], '51%': [179], '72%;': [181], '0.4918': [191], '0.7098,': [193], '41%': [199], '55%,': [201], '56%': [210], '75%.To': [212], 'sum': [213], 'up,': [214], 'strong': [220], 'stability': [221], 'sensor': [235], 'data.': [236], 'In': [237], 'addition,': [238], 'band': [240, 243, 257, 268, 281, 294], 'widths': [241], 'positions': [244], 'had': [245], 'influences': [247], 'concentration.When': [255], 'position': [258, 282, 288], 'close': [260], 'characteristic': [263, 291], 'wavelength': [264], 'chlorophyll,': [266], 'narrow': [267], 'width': [269, 295], 'beneficial': [271], 'accuracy': [274], 'model;': [277], 'while': [278], 'when': [279], 'far': [284], 'away': [285], 'from': [286], 'wavelength,': [292], 'should': [296], 'be': [297], 'increased': [298], 'reasonably.': [299]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W2659933339', 'counts_by_year': [{'year': 2021, 'cited_by_count': 2}, {'year': 2020, 'cited_by_count': 1}, {'year': 2018, 'cited_by_count': 1}], 'updated_date': '2024-12-22T11:37:57.898051', 'created_date': '2017-06-30'}