The PPNH （non-homogenous Poisson processes） are frequently used as models for events that come about randomly in a given time period, for example, failure times, time of accidents occurrences, etc. In this work, PPN...The PPNH （non-homogenous Poisson processes） are frequently used as models for events that come about randomly in a given time period, for example, failure times, time of accidents occurrences, etc. In this work, PPNH is used to model monthly maximum observations of urban ozone corresponding to a period of five years from the meteorological stations of Merced, Pedregal and Plateros, located in the metropolitan area of Mexico City. The interest data are the times in which the observations surpassed the permissible level of ozone of 0.11 ppm, settled by the Mexican Official Norm （NOM-020-SSA 1-1993） to preserve public health.展开更多
Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from ...Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals(10,15,20,and 30 d).Then,we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period beforefinally assessing the impacts of adding environmental covariates.The results showed the following:(1)in SOM mapping,the highest accuracy was obtained using day-of-year(DOY)120 to 140 synthetic images with 20 d time intervals,as well as with different time intervals,ranked as follows:20 d>30 d>15 d>10 d;(2)when using synthetic images at different time intervals to predict SOM,the best time period for predicting SOM was always within May;and(3)adding environmental covariates effectively improved the SOM mapping performance,and the multiyear average temperature was the most important factor.In general,our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery,thereby allowing detailed mapping of large areas of cultivated soil.展开更多
文摘The PPNH （non-homogenous Poisson processes） are frequently used as models for events that come about randomly in a given time period, for example, failure times, time of accidents occurrences, etc. In this work, PPNH is used to model monthly maximum observations of urban ozone corresponding to a period of five years from the meteorological stations of Merced, Pedregal and Plateros, located in the metropolitan area of Mexico City. The interest data are the times in which the observations surpassed the permissible level of ozone of 0.11 ppm, settled by the Mexican Official Norm （NOM-020-SSA 1-1993） to preserve public health.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28100000)the K.C.Wong Education Foundation,Jilin Provincial Development and Reform Commission Innovation Capacity Building Project(grant number 2021C044-10)the Special fund project for high-tech indus-trialization of science and technology cooperation between Jilin Province and the Chinese Academy of Sciences(2021SYHZ0013).
文摘Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals(10,15,20,and 30 d).Then,we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period beforefinally assessing the impacts of adding environmental covariates.The results showed the following:(1)in SOM mapping,the highest accuracy was obtained using day-of-year(DOY)120 to 140 synthetic images with 20 d time intervals,as well as with different time intervals,ranked as follows:20 d>30 d>15 d>10 d;(2)when using synthetic images at different time intervals to predict SOM,the best time period for predicting SOM was always within May;and(3)adding environmental covariates effectively improved the SOM mapping performance,and the multiyear average temperature was the most important factor.In general,our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery,thereby allowing detailed mapping of large areas of cultivated soil.
