Donana - Phenology MeanMean (2020)

Abstract: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.Methods: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083TechnicalInfo: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation indexand for each one two statistics are given:expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask)within the general name rule proposed:locality_variable_timestamp.extensionvariable formed in:Phenology-SubvariableStatisticsThe subvariables are:mean: mean value across the year - values range between 0-0.5stdintra: standard deviation of the value across the year - values range between 0-0.05maxpos: day of the year of the maximum value - values range between 0-0.5sdinter: standard deviation across years - values range between 0-0.05The statistics are:mean: Expected value of the subvariable across 100 simulationCVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the meanThe timestamp refer to a year or to two years

摘要：基于生态系统功能属性框架的物候年度汇总统计。结果基于S2插值和Harmonic模型贝叶斯实现的预测。方法：方法的具体细节可参见Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019)所撰《基于稀疏和不规则卫星遥感植被指数时间序列的贝叶斯谐波建模：云与火的故事》。Remote Sens 12:83。DOI: 10.3390/rs12010083技术信息：物候并非一个标量变量，而是一组基于MCARI2植被指数的子变量集合，对于每个子变量，均提供两个统计量：期望值（平均值）和标准差大于平均值10%的所有像素的掩码（CVmask）。在一般命名规则下，变量形成如下：物候-子变量-统计。子变量包括：mean（平均值）：年度内平均值，值域介于0-0.5之间；stdintra（年内标准差）：年度内值的标准差，值域介于0-0.05之间；maxpos（最大值日）：年内最大值对应的日期，值域介于0-0.5之间；sdinter（跨年标准差）：跨年标准差，值域介于0-0.05之间。统计量包括：mean（平均值）：子变量在100次模拟中的期望值；CVmask（CV掩码）：0-1掩码，值1对应于与平均值相比标准差小于10%的像素。时间戳指代年份或两年。