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Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters

机译:半解析算法的开发,用于从清澈到浑浊的水域中的遥感中检索悬浮颗粒物

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摘要

Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, R-rs(lambda), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g center dot m(-3). Different empirical and semi-analytical approaches developed to assess SPM from R-rs(lambda) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.
机译:长期以来,来自太空的悬浮颗粒物SPM的遥感一直被用来评估其在各个沿海地区的时空变化。相关算法通常是针对特定地点的,或者是在相对狭窄的浓度范围内开发的,这使其不适用于全球应用(或至少在较宽的SPM范围内)。在GlobCoast项目的框架中,已建立了一个大型SPM和遥感反射率原位数据集R-rs(lambda),将欧洲,法属圭亚那,北加拿大,越南和中国。该数据集涵盖受不同生物地球化学和物理过程(例如沉积物重悬,浮游植物开花事件和河流排放)(亚马逊河,湄公河,黄河,麦肯齐河等)不同影响的各种对比沿海环境。 SPM浓度跨越大约四个数量级,从0.15到2626 g中心点m(-3)。在此原位数据集上测试了用于评​​估R-rs(λ)的SPM的不同经验和半分析方法。由于它们均不能在整个SPM范围内提供令人满意的结果,因此已经开发了一种通用的半分析方法。该算法基于分别针对中低水和高混浊水校准的两个标准半分析方程式。还为中间环境开发了混合律。已经评估了SPM检索中的不确定性来源,例如生物光学变异性,大气校正误差和光谱带宽。已针对海洋颜色(SeaWiFS,MODIS-A / T,MERIS / OLCI,VIIRS)和高空间分辨率(LandSat8-OLI和Sentinel2-MSI)传感器计算了这些不同算法中涉及的系数。所提出的算法的性能从一个传感器到另一个传感器仅略有不同,这证明了所提出的方法在全球和对比沿海水域中的巨大潜在适用性。

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