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首页> 外文期刊>Journal of oceanography >Estimating Photosynthetically Available Radiation at the Ocean Surface from ADEOS-II Global Imager Data
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Estimating Photosynthetically Available Radiation at the Ocean Surface from ADEOS-II Global Imager Data

机译：根据ADEOS-II全球成像仪数据估算海洋表面的光合有效辐射

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A simple, yet efficient and fairly accurate algorithm is presented to estimate photosynthetically available radiation (PAR) at the ocean surface from Global Imager (GLI) data. The algorithm utilizes plane-parallel radiation-transfer theory and separates the effects of the clear atmosphere and clouds, i.e., the planetary atmosphere is modeled as a clear atmosphere positioned above a cloud layer. PAR is computed as the difference between the incident 400-700 nm solar flux at the top of the atmosphere (known) and the solar flux reflected back to space by the atmosphere and surface (derived from GLI radiance), taking atmospheric absorption into account. Knowledge of pixel composition is not required, eliminating the need for cloud screening and arbitrary assumptions about sub-pixel cloudiness. For each GLI pixel, clear or cloudy, a daily PAR estimate is obtained. Diurnal changes in cloudiness are taken into account statistically, using a regional diurnal albedo climatology based on 5 years of Earth Radiation Budget Satellite (ERBS) data. The algorithm results are verified against other satellite estimates of PAR, the National Centers for Environmental Prediction (NCEP) reanalysis product, and in-situ measurements from fixed buoys. Agreement is generally good between GLI and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) estimates, with root-mean-squared (rms) differences of 7.9 (22%), 4.6 (13%), and 2.7 (8%) Einstein/m~2/day on daily, weekly, and monthly time scales, and a bias of only 0.8-0.9 (about 2%) Einstein/m~2/day. The rms differences between GLI and Visible and Infrared Spin Scan Radiometer (VISSR) estimates and between GLI and NCEP estimates are smaller and larger, respectively, on monthly time scales, i.e., 3.0 (7%) and 5.0 (14%) Einstein/m~2/day, and biases are 1.1 (2%) and -0.2 (-1%) Einstein/m~2/day. The comparison with buoy data also shows good agreement, with rms inaccuracies of 10.2 (23%), 6.3 (14%), and 4.5 (10%) Einstein/m~2/day on daily, weekly, and monthly time scales, and slightly higher GLI values by about 1.0 (2%) Einstein/m~2/day. The good statistical performance makes the algorithm suitable for large-scale studies of aquatic photosynthesis.
机译：提出了一种简单但有效且相当准确的算法，用于根据Global Imager（GLI）数据估算海洋表面的光合有效辐射（PAR）。该算法利用平面平行辐射传输理论并分离了透明大气和云的影响，即，将行星大气建模为位于云层上方的透明大气。 PAR计算为大气顶部（已知）的入射400-700 nm太阳通量与大气和表面反射回太空的太阳通量之差（由GLI辐射得出），并考虑了大气吸收。不需要像素组成方面的知识，从而无需进行云筛查和有关子像素浑浊的任意假设。对于每个GLI像素（晴天或阴天），都会获得每日PAR估算值。使用基于5年地球辐射预算卫星（ERBS）数据的区域昼间反照率气候学，统计学地考虑了乌云的昼夜变化。对照其他PAR卫星估计，国家环境预测中心（NCEP）重新分析产品以及固定浮标的现场测量结果验证了算法结果。 GLI与海景宽视场传感器（SeaWiFS）估算值之间通常吻合良好，均方根（rms）差异分别为7.9（22％），4.6（13％）和2.7（8％） ）在每天，每周和每月的时间尺度上的爱因斯坦/米2 /天，偏差只有0.8-0.9（约2％）爱因斯坦/米2 /天。 GLI与可见光和红外自旋扫描辐射仪（VISSR）估计值之间的均方根差以及GLI和NCEP估计值之间的均方根差分别在月度尺度上变小或变大，即爱因斯坦/ m为3.0（7％）和5.0（14％）大约2天/天，偏差为1.1（2％）和-0.2（-1％）爱因斯坦/米2 /天。与浮标数据的比较也显示出良好的一致性，在每天，每周和每月的时间尺度上，均方根误差均值分别为10.2（23％），6.3（14％）和4.5（10％）爱因斯坦/米2 /天，并且爱因斯坦/米2 /天的GLI值略高约1.0（2％）。良好的统计性能使该算法适合于水生光合作用的大规模研究。

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