Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad applications.Most users prefer to use gap-filled remote sensing products w...Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad applications.Most users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)algorithms.Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources.This study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,i.e.Harmonic ANalysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation data.Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE.According to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation data.Compared with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites.The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user communities.To our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.展开更多
Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously...Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.展开更多
Based on TIMESAT 3.2 platform, MODIS NDVI data(2000–2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season(SG...Based on TIMESAT 3.2 platform, MODIS NDVI data(2000–2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season(SGS), the end of growth season(EGS) and the length of growth season(LGS). The spatial and temporal variation of vegetation phenology and its response to climate changes are analyzed respectively. The conclusions are as follows:(1) SGS is mainly delayed as a whole. Areas delayed are more than the advanced in EGS, and EGS is a little delayed as a whole. LGS is generally shortened.(2) With the altitude rising, SGS is delayed, EGS is advanced, and LGS is shortened and phenophase appears a big variation below 3000 m and above 5000 m.(3) From 2000 to 2015, the temperature appears a slight increase along with a big fluctuation, and the precipitation increases evidently.(4) Response of phenophase to precipitation is not obvious in the low elevation humid regions, where SGS arrives early and EGS delays; while, in the upper part of the mountain regions, SGS delays and EGS advances with temperature rising, SGS arrives early and EGS delays with precipitation increasing.展开更多
基金supported by the National Natural Science Foundation of China(grant number 42171371 and No.41701492)Massimo Menenti acknowledges the support of the MOST High Level Foreign Expert program(grant number G2022055010L)the Chinese Academy of Sciences President s International Fellowship Initiative(grant number 2020VTA0001).
文摘Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad applications.Most users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)algorithms.Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources.This study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,i.e.Harmonic ANalysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation data.Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE.According to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation data.Compared with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites.The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user communities.To our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.
文摘Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.
基金National Natural Science Foundation of China,No.40971118Physical Geography Key Disciplines Construction Subjects of Hebei Province
文摘Based on TIMESAT 3.2 platform, MODIS NDVI data(2000–2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season(SGS), the end of growth season(EGS) and the length of growth season(LGS). The spatial and temporal variation of vegetation phenology and its response to climate changes are analyzed respectively. The conclusions are as follows:(1) SGS is mainly delayed as a whole. Areas delayed are more than the advanced in EGS, and EGS is a little delayed as a whole. LGS is generally shortened.(2) With the altitude rising, SGS is delayed, EGS is advanced, and LGS is shortened and phenophase appears a big variation below 3000 m and above 5000 m.(3) From 2000 to 2015, the temperature appears a slight increase along with a big fluctuation, and the precipitation increases evidently.(4) Response of phenophase to precipitation is not obvious in the low elevation humid regions, where SGS arrives early and EGS delays; while, in the upper part of the mountain regions, SGS delays and EGS advances with temperature rising, SGS arrives early and EGS delays with precipitation increasing.
