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Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-12-22 , DOI: 10.1016/j.rse.2020.112241
Najib Djamai , Richard Fernandes

For typical cloud conditions, a clear sky retrieval rate (CSRR) >67% is required to meet the Global Climate Observing System temporal interval requirement of 10 days when mapping canopy biophysical variables (‘variables’). Physically based algorithms suitable for global mapping of variables using multispectral satellite imagery, e.g. the Simplified Level 2 Prototype Processor (SL2P), typically have a CSRR between 25% and 75%. An Active Learning Regularization (ALR) approach was developed to increase the CSRR rate while satisfying uncertainty requirements. A local calibration database for each variable was produced from representative valid SL2P estimates and associated Sentinel-2 Multispectral Instrument surface reflectance estimates. Predictors for each variable were developed by i) using Least Absolute Shrinkage and Selection Operator regression to select a subset of spectral vegetation indices (VIs) from a provided library, ii) removing outliers from the calibration database by trimming the conditional distribution of each variable given a VI, and iii) calibrating a non-linear regression predictor of the variable given the selected VIs using the trimmed database. ALR was applied to MSI imagery acquired over the Canadian Prairies during the 2016 and 2018 growing seasons and validated with in-situ data collected over 50 fields by the SMAPVEX16-MB campaign. The mean CSRR during the 2018 growing season was ~98% (~70%) for ALR (SL2P) for all canopy variables except FCOVER and ~ 98% for FCOVER using both ALR and SL2P. In comparison to SL2P, ALR had increased agreement rates with in-situ leaf area index (86% versus 79%) and fraction cover (96% versus 79%) but not canopy water content (35% versus 53%). Intercomparison with valid SL2P estimates from different MSI images acquired within ±2 days found that 90% [±5%] of ALR estimates fell within the uncertainty of the valid estimates. These findings support the hypothesis that, over croplands, ALR significantly increases CSRR over SL2P without appreciably increasing uncertainty for variables retrieved by SL2P within thematic performance requirements.



中文翻译:

主动学习正则化使用Sentinel-2数据提高植被生物物理变量的晴朗天空检索率

对于典型的云条件,在绘制冠层生物物理变量(“变量”)时,需要有≥67%的晴朗天空检索率(CSRR)才能满足全球气候观测系统10天的时间间隔要求。适用于使用多光谱卫星图像对变量进行全局映射的基于物理的算法(例如,简化的2级原型处理器(SL2P))的CSRR通常在25%至75%之间。开发了一种主动学习正则化(ALR)方法,以提高CSRR率,同时满足不确定性要求。根据代表性的有效SL2P估计值和相关的Sentinel-2多光谱仪表面反射率估计值,为每个变量创建了一个本地校准数据库。i)使用最小绝对收缩和选择算子回归开发每个变量的预测变量,以从提供的库中选择光谱植被指数(VIs)的子集,ii)通过修整给定每个变量的条件分布,从校准数据库中删除异常值VI,以及iii)使用调整后的数据库在给定选定VI的情况下校准变量的非线性回归预测变量。ALR适用于2016年和2018年生长季节在加拿大大草原上采集的MSI图像,并通过SMAPVEX16-MB活动在50多个领域中收集的现场数据进行了验证。对于所有冠层变量,除FCOVER外,2018年生长期的平均CSRR为ALR(SL2P)的〜98%(〜70%),同时使用ALR和SL2P的FCOVER的平均CSRR为〜98%。与SL2P相比,ALR与原位叶面积指数(86%对79%)和部分覆盖率(96%对79%)的一致率增加,但冠层水含量(35%对53%)没有增加。从±2天之内获得的不同MSI图像与有效SL2P估计进行的比对发现,有90%[±5%]的ALR估计落在有效估计的不确定性之内。这些发现支持以下假设:相对于SL2P,ALR在耕地上显着提高了CSRR,而在主题性能要求范围内,SL2P检索的变量的不确定性却没有明显增加。从±2天内获得的不同MSI图像与有效SL2P估计进行的比对发现,有90%[±5%]的ALR估计落在有效估计的不确定性范围内。这些发现支持以下假设:相对于SL2P,ALR在耕地上显着提高了CSRR,而在主题性能要求范围内,SL2P检索的变量的不确定性却没有明显增加。从±2天之内获得的不同MSI图像与有效SL2P估计进行的比对发现,有90%[±5%]的ALR估计落在有效估计的不确定性之内。这些发现支持以下假设:相对于SL2P,ALR在耕地上显着提高了CSRR,而在主题性能要求范围内,SL2P检索的变量的不确定性却没有明显增加。

更新日期:2020-12-22
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