The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from l...The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.展开更多
Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground...Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.展开更多
Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and th...Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and their extensive influence on ecological processes in temperate and cold regions.climatic warming substantially advanced SOS on the tibetan Plateau from 1982 to 2011.However,it is unclear why EOS showed little delay despite increasing tem-perature over this period.Methods We used multiple methods to determine EOS from the satellite-observed normalized-difference vegetation index and investigated the relationships between EOS and its potential drivers on the tibetan Plateau over 1982-2011.Important findings We found a slight but non-significant delay in regionally averaged EOS of 0.7 day decade−1(P=0.18)and a widespread but weak delaying trend across the Plateau over this period.the inter-annual variations in regionally averaged EOS were driven mainly by pre-season temperature(partial R=0.62,P<0.01),and precipitation and insolation showed weak impact on EOS(P>0.10).Pre-season warming delayed EOS mainly in the eastern half and north-western area of the plateau.In the south-west,EOS was significantly and positively related to SOS,suggesting potentially indirect effects of winter weather conditions on the following autumn’s phenology through regulation of spring phenology.EOS was more strongly related with pre-season temperature in colder and wetter areas,reflecting vegetation adaptation to local climate.Interestingly,pre-season temperature had weaker delaying effects on EOS for vegeta-tion with a shorter growing season,for which SOS had a stronger control on inter-annual variations in EOS than for vegetation with a longer growing season.this indicates that shorter-season tibetan Plateau vegetation may have lower plasticity in adjusting the length of its growing season,whenever it begins,and that climate change is more likely to shift the growing season than extend it for that vegetation.展开更多
Aims Light-use efficiency(LUE)is an important tool for scaling up local CO_(2)flux(F_(CO_(2)))tower observations to regional and global carbon dynamics.Using a data set including F_(CO_(2))and environmental factors ob...Aims Light-use efficiency(LUE)is an important tool for scaling up local CO_(2)flux(F_(CO_(2)))tower observations to regional and global carbon dynamics.Using a data set including F_(CO_(2))and environmental factors obtained from an alpine meadow on the Tibetan Plateau,we examined both diurnal and seasonal changes in LUE and the environmental factors controlling these changes.Our objectives were to(i)characterize the diurnal and daily variability of LUE in an alpine meadow,(ii)clarify the causes of this variability,and(iii)explore the possibility of applying the LUE approach to this alpine meadow by examining the relationship between daily LUE and hourly LUE at satellite visiting times.Methods First,we obtained the LUE—the ratio of the gross primary production(GPP)to the absorbed photosynthetically active radiation(APAR)—from the flux tower and meteorological observations.We then characterized the patterns of diurnal and seasonal changes in LUE,explored the environmental controls on LUE using univariate regression analyses and evaluated the effects of diffuse radiation on LUE by assigning weights through a linear programming method to beam photosynthetically active radiation(PAR)and diffuse PAR,which were separated from meteorological observations using an existing method.Finally,we examined the relationships between noontime hourly LUE and daily LUE and those between adjusted noontime hourly and daily LUE because satellites visit the site only once or twice a day,near noon.Important Findings The results showed that(i)the LUE of the alpine meadow generally followed the diurnal and seasonal patterns of solar radiation but fluctuated with changes in cloud cover.(ii)The fraction of diffuse light played a dominant role in LUE variation.Daily minimum temperature and vapor pressure deficit also affected LUE variation.(iii)The adjusted APAR,defined as the weighted linear sum of diffuse APAR and beam APAR,was linearly correlated with GPP on different temporal scales.(iv)Midday adjusted LUE was closely related to daily adjusted LUE,regardless of the cloud cover.The results indicated the importance of considering radiation direction when developing LUE-based GPP-estimating models.展开更多
Plant spring phenology is receiving increasing attention owing to the recognition of its high sensitivity to ongoing climatic warming [1]. Changes in plant spring phenology can substantially influence a wide range of ...Plant spring phenology is receiving increasing attention owing to the recognition of its high sensitivity to ongoing climatic warming [1]. Changes in plant spring phenology can substantially influence a wide range of ecosystem structure and functions, which can not only affect human-beings but also in turn affect climate [2]. Warming experiments have been widely conducted to help understand, and thus predict plant phenological response to climate. Most of these experiment-based studies have focused on reporting the signs and magn让udes of phenological responses, and a few have included temperature sensitivity (phenological shifts per unit temperature change). However, applying the outputs of these experiments to predict future phenological response to climate change remains challenging.展开更多
Global climate change is expected to have a signifcant impact on ecosystems worldwide,especially for alpine meadows which are considered as one of the most vulnerable components.However,the effects of global warming o...Global climate change is expected to have a signifcant impact on ecosystems worldwide,especially for alpine meadows which are considered as one of the most vulnerable components.However,the effects of global warming on the plant nitrogen-phosphorus stoichiometry and resorption in alpine meadows remain unclear.Therefore,to investigate the plant nitrogen-phosphorus stoichiometry and resorption in alpine meadows on the Qinghai-Tibet Plateau,we conducted an artifcial warming study using open-top chambers(OTCs)over the 3 years of warming period.We selected three dominant species,four height types of OTCs(0.4,0.6,0.8 and 1 m)and four warming methods(year-round warming,winter warming,summer-autumn-winter warming and spring-summer-autumn warming in the experiment)in this experiment.In our study,soil temperature signifcantly increased with increasing the height of OCTs under the different warming methods.Kobresia pygmaea presented an increase in nitrogen(N)limitation and Kobresia humilis presented an increase in phosphorus(P)limitation with increasing temperature,while Potentilla saundersiana was insensitive to temperature changes in terms of nitrogen and phosphorus limitations.Both nitrogen resorption effciency:phosphorus resorption effciency and N:P trends in response to rising temperatures were in the same direction.The differential responses of the chemical stoichiometry of the three species to warming were observed,refecting that the responses of nitrogen and phosphorus limitations to warming are multifaceted,and the grassland ecosystems may exhibit a certain degree of self-regulatory capability.Our results show that using chemical dosage indicators of a single dominant species to represent the nitrogen and phosphorus limitations of the entire ecosystem is inaccurate,and using N:P to refect the nutritional limitations might have been somewhat misjudged in the context of global warming.展开更多
Vegetation green-up is occurring earlier due to climate warming across the Northern Hemisphere,with substantial infuences on ecosystems.However,it is unclear whether temperature responses differ among various green-up...Vegetation green-up is occurring earlier due to climate warming across the Northern Hemisphere,with substantial infuences on ecosystems.However,it is unclear whether temperature responses differ among various green-up stages.Using high-temporal-resolution satellite data of vegetation greenness and averaging over northern vegetation(30-75°N),we found the negative interannual partial correlation between the middle green-up stage timing(50%greenness increase in spring-summer)and temperature(R_(P)=-0.73)was stronger than those for the onset(15%increase,R_(P)=-0.65)and end(90%increase,R_(P)=-0.52)of green-up during 2000-2022.Spatially,at high latitudes,the middle green-up stage showed stronger temperature responses than the onset,associated with greater low-temperature constraints and stronger control of snowmelt on green-up onset as well as greater spring frost risk.At middle latitudes,correlations with temperature were similar between the onset and middle stages of green-up,except for grasslands of the Mongolian Plateau and interior western USA,where correlations with temperature were weaker for the middle stage due to water limitation.In contrast,the end of the green-up showed weaker temperature responses than the middle due to insuffcient water and high climatic temperature during the end of the green-up in most of the study region,except for cold regions in the interior western USA,western Russia and the Tibetan Plateau,where temperature was still a main driver during end of green-up.Our fndings underscore the differences in temperature responses among green-up stages,which alters the temporal alignment between plants and environmental resources.展开更多
In the the original published figure,the‘Achnatherum grassland’chart showed six lines when there should have been three.Please see below the corrected figure.
Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused ...Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2016YFA0602701)the National Natural Science Foundation of China(Grant Nos.41721091,41630754,91644225)the Open Program(Grant No.SKLCS-OP-2017-02)from the State Key Laboratory of Cryospheric Science,Northwest Institute of EcoEnvironment and Resources,Chinese Academy of Sciences
文摘The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.
基金The field investigation was partly supported by a program on long-term monitoring of alpine ecosystems on the Tibetan Plateau from the Ministry of Environment,Japan to T.Y.Program for New Century Excellent Talents in University to C.J.Director-encouragement fund from National Institute for Environmental Studies to S.A.
文摘Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.
基金This work was funded by grants from the National Natural Science Foundation of China(41571103 and 41501103)the‘Strategic Priority Research Program(B)’of the Chinese Academy of Sciences(XDB03030404)+2 种基金the National Basic Research Program of China(2013CB956303)the China Postdoctoral Science Foundation(2015M580137)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2015055).
文摘Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and their extensive influence on ecological processes in temperate and cold regions.climatic warming substantially advanced SOS on the tibetan Plateau from 1982 to 2011.However,it is unclear why EOS showed little delay despite increasing tem-perature over this period.Methods We used multiple methods to determine EOS from the satellite-observed normalized-difference vegetation index and investigated the relationships between EOS and its potential drivers on the tibetan Plateau over 1982-2011.Important findings We found a slight but non-significant delay in regionally averaged EOS of 0.7 day decade−1(P=0.18)and a widespread but weak delaying trend across the Plateau over this period.the inter-annual variations in regionally averaged EOS were driven mainly by pre-season temperature(partial R=0.62,P<0.01),and precipitation and insolation showed weak impact on EOS(P>0.10).Pre-season warming delayed EOS mainly in the eastern half and north-western area of the plateau.In the south-west,EOS was significantly and positively related to SOS,suggesting potentially indirect effects of winter weather conditions on the following autumn’s phenology through regulation of spring phenology.EOS was more strongly related with pre-season temperature in colder and wetter areas,reflecting vegetation adaptation to local climate.Interestingly,pre-season temperature had weaker delaying effects on EOS for vegeta-tion with a shorter growing season,for which SOS had a stronger control on inter-annual variations in EOS than for vegetation with a longer growing season.this indicates that shorter-season tibetan Plateau vegetation may have lower plasticity in adjusting the length of its growing season,whenever it begins,and that climate change is more likely to shift the growing season than extend it for that vegetation.
基金Supported by the projects‘Integrated Study for Terrestrial Carbon Management of Asia in the 21st Century Based on Scientific Advancements’and‘Early Detection and Prediction of Climate Warming Based on the Long-Term Monitoring of Alpine Ecosystems on the Tibetan Plateau’funded by the Ministry of the Environment,Japanresearch fund from the Program for New Century Excellent Talents in University,from Ministry of Education,China,to J.C.
文摘Aims Light-use efficiency(LUE)is an important tool for scaling up local CO_(2)flux(F_(CO_(2)))tower observations to regional and global carbon dynamics.Using a data set including F_(CO_(2))and environmental factors obtained from an alpine meadow on the Tibetan Plateau,we examined both diurnal and seasonal changes in LUE and the environmental factors controlling these changes.Our objectives were to(i)characterize the diurnal and daily variability of LUE in an alpine meadow,(ii)clarify the causes of this variability,and(iii)explore the possibility of applying the LUE approach to this alpine meadow by examining the relationship between daily LUE and hourly LUE at satellite visiting times.Methods First,we obtained the LUE—the ratio of the gross primary production(GPP)to the absorbed photosynthetically active radiation(APAR)—from the flux tower and meteorological observations.We then characterized the patterns of diurnal and seasonal changes in LUE,explored the environmental controls on LUE using univariate regression analyses and evaluated the effects of diffuse radiation on LUE by assigning weights through a linear programming method to beam photosynthetically active radiation(PAR)and diffuse PAR,which were separated from meteorological observations using an existing method.Finally,we examined the relationships between noontime hourly LUE and daily LUE and those between adjusted noontime hourly and daily LUE because satellites visit the site only once or twice a day,near noon.Important Findings The results showed that(i)the LUE of the alpine meadow generally followed the diurnal and seasonal patterns of solar radiation but fluctuated with changes in cloud cover.(ii)The fraction of diffuse light played a dominant role in LUE variation.Daily minimum temperature and vapor pressure deficit also affected LUE variation.(iii)The adjusted APAR,defined as the weighted linear sum of diffuse APAR and beam APAR,was linearly correlated with GPP on different temporal scales.(iv)Midday adjusted LUE was closely related to daily adjusted LUE,regardless of the cloud cover.The results indicated the importance of considering radiation direction when developing LUE-based GPP-estimating models.
基金supported by the National Natural Science Foundation of China(42030508,41988101)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(2019QZKK0301)+1 种基金funding from the European Research Council(ERC-SyG-2013-610028 IMBALANCE-P)funding from the project “Inside out”(#POIR.04.04.00-00-5F85/18-00)funded by the HOMING programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund。
基金supported by the National Key Research & Development Program of China (2018YFA0606101, 2017YFA0604802)the Strategic Priority Research Program of Chinese Academy of Sciences (XDA19070303)the National Natural Science Foundation of China (41501103)
文摘Plant spring phenology is receiving increasing attention owing to the recognition of its high sensitivity to ongoing climatic warming [1]. Changes in plant spring phenology can substantially influence a wide range of ecosystem structure and functions, which can not only affect human-beings but also in turn affect climate [2]. Warming experiments have been widely conducted to help understand, and thus predict plant phenological response to climate. Most of these experiment-based studies have focused on reporting the signs and magn让udes of phenological responses, and a few have included temperature sensitivity (phenological shifts per unit temperature change). However, applying the outputs of these experiments to predict future phenological response to climate change remains challenging.
基金the National Natural Science Foundation of China(31770501)the Science and Technology Innovation Base Free Research Program of Tibetan Autonomous Region of China(to Zhiyong,Yang).
文摘Global climate change is expected to have a signifcant impact on ecosystems worldwide,especially for alpine meadows which are considered as one of the most vulnerable components.However,the effects of global warming on the plant nitrogen-phosphorus stoichiometry and resorption in alpine meadows remain unclear.Therefore,to investigate the plant nitrogen-phosphorus stoichiometry and resorption in alpine meadows on the Qinghai-Tibet Plateau,we conducted an artifcial warming study using open-top chambers(OTCs)over the 3 years of warming period.We selected three dominant species,four height types of OTCs(0.4,0.6,0.8 and 1 m)and four warming methods(year-round warming,winter warming,summer-autumn-winter warming and spring-summer-autumn warming in the experiment)in this experiment.In our study,soil temperature signifcantly increased with increasing the height of OCTs under the different warming methods.Kobresia pygmaea presented an increase in nitrogen(N)limitation and Kobresia humilis presented an increase in phosphorus(P)limitation with increasing temperature,while Potentilla saundersiana was insensitive to temperature changes in terms of nitrogen and phosphorus limitations.Both nitrogen resorption effciency:phosphorus resorption effciency and N:P trends in response to rising temperatures were in the same direction.The differential responses of the chemical stoichiometry of the three species to warming were observed,refecting that the responses of nitrogen and phosphorus limitations to warming are multifaceted,and the grassland ecosystems may exhibit a certain degree of self-regulatory capability.Our results show that using chemical dosage indicators of a single dominant species to represent the nitrogen and phosphorus limitations of the entire ecosystem is inaccurate,and using N:P to refect the nutritional limitations might have been somewhat misjudged in the context of global warming.
基金supported by the National Natural Science Foundation of China(42301388)the Fundamental Research Funds for the Central Universities,and the Top-Notch Young Talents Program of China(to M.Shen).
文摘Vegetation green-up is occurring earlier due to climate warming across the Northern Hemisphere,with substantial infuences on ecosystems.However,it is unclear whether temperature responses differ among various green-up stages.Using high-temporal-resolution satellite data of vegetation greenness and averaging over northern vegetation(30-75°N),we found the negative interannual partial correlation between the middle green-up stage timing(50%greenness increase in spring-summer)and temperature(R_(P)=-0.73)was stronger than those for the onset(15%increase,R_(P)=-0.65)and end(90%increase,R_(P)=-0.52)of green-up during 2000-2022.Spatially,at high latitudes,the middle green-up stage showed stronger temperature responses than the onset,associated with greater low-temperature constraints and stronger control of snowmelt on green-up onset as well as greater spring frost risk.At middle latitudes,correlations with temperature were similar between the onset and middle stages of green-up,except for grasslands of the Mongolian Plateau and interior western USA,where correlations with temperature were weaker for the middle stage due to water limitation.In contrast,the end of the green-up showed weaker temperature responses than the middle due to insuffcient water and high climatic temperature during the end of the green-up in most of the study region,except for cold regions in the interior western USA,western Russia and the Tibetan Plateau,where temperature was still a main driver during end of green-up.Our fndings underscore the differences in temperature responses among green-up stages,which alters the temporal alignment between plants and environmental resources.
文摘In the the original published figure,the‘Achnatherum grassland’chart showed six lines when there should have been three.Please see below the corrected figure.
基金supported by the National Natural Science Foundation of China(Project Nos.42271331 and 42022060)The Hong Kong Polytechnic University(Project Nos.4-ZZND and Q-CDBP).
文摘Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.
