Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been...Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.展开更多
Although land surface water only covers a small portion(~3.7%)of our planet,it plays many essential roles:e.g,freshwater storage,industrial consumption,agricultural irrigation,and biodiversity maintenance[1,2].Water s...Although land surface water only covers a small portion(~3.7%)of our planet,it plays many essential roles:e.g,freshwater storage,industrial consumption,agricultural irrigation,and biodiversity maintenance[1,2].Water storage variability is associated with various natural processes and human activities on the Earth[3,4]and it is well known that reservoir regulation is the most widely distributed and fundamental human activity capable of altering the global water cycle.In recent years,through reservoir regulation,humans have been able to obtain more freshwater from the Earth’s surface,with great benefits to human wellbeing.Currently,however,the extent to which human activities affect the global water cycle remains unknown.As a result,there is an urgent need to quantify and understand the role of human activities in the global water cycle to ensure that freshwater resources are managed sustainably at the global scale.展开更多
Soil moisture plays a crucial role in drought monitoring,flood forecasting,and water resource management.Data assimilation methods can integrate the strengths of land surface models(LSM)and remote sensing data to gene...Soil moisture plays a crucial role in drought monitoring,flood forecasting,and water resource management.Data assimilation methods can integrate the strengths of land surface models(LSM)and remote sensing data to generate highprecision and spatio-temporally continuous soil moisture products.However,one of the challenges of the land data assimilation system(LDAS)is how to accurately estimate model and observation errors.To address this,we had previously proposed a dualcycle assimilation algorithm that can simultaneously estimate the model and observation errors,LSM parameters,and observation operator parameters.However,this algorithm requires a large ensemble size to guarantee stable parameter estimates,resulting in low efficiency and limiting its large-scale applications.To address this limitation,the authors employed the following approaches:(1)using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM,and(2)replacing the ensemble Kalman filter framework with the extended Kalman filter(EKF)framework to improve the efficiency of parameter optimization for the dual-cycle algorithm.The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h.To evaluate the dual-cycle LDAS at the regional-scale,it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau,and soil moisture estimates were validated using in situ observations from four different climatic areas.The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture,land surface temperature,and evapotranspiration than did the open loop with default parameters.Furthermore,the spatial distribution of soil parameters(sand content,clay content,and porosity)obtained from the LDAS was more reasonable than those of default values.The EKF-based dual-cycle algorithm developed in this study is expected to improve the assimilation skills of land surface,ecological,and hydrological studies.展开更多
基金This work was supported by the National Natural Science Foundation of China(72221002,42271375)the Strategic Priority Research Program(XDA28060100)the Informatization Plan Project(CAS-WX2021PY-0109)of the Chinese Academy of Sciences.
文摘Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0206)the National Natural Science Foundation of China(42101343,42001416)。
文摘Although land surface water only covers a small portion(~3.7%)of our planet,it plays many essential roles:e.g,freshwater storage,industrial consumption,agricultural irrigation,and biodiversity maintenance[1,2].Water storage variability is associated with various natural processes and human activities on the Earth[3,4]and it is well known that reservoir regulation is the most widely distributed and fundamental human activity capable of altering the global water cycle.In recent years,through reservoir regulation,humans have been able to obtain more freshwater from the Earth’s surface,with great benefits to human wellbeing.Currently,however,the extent to which human activities affect the global water cycle remains unknown.As a result,there is an urgent need to quantify and understand the role of human activities in the global water cycle to ensure that freshwater resources are managed sustainably at the global scale.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0206)the National Key Research and Development Program of China(Grant No.2022YFC3002901)+1 种基金the National Natural Science Foundation of China(Grant No.42271491)the International Partnership Program of Chinese Academy of Sciences(Grant No.183311KYSB20200015)。
文摘Soil moisture plays a crucial role in drought monitoring,flood forecasting,and water resource management.Data assimilation methods can integrate the strengths of land surface models(LSM)and remote sensing data to generate highprecision and spatio-temporally continuous soil moisture products.However,one of the challenges of the land data assimilation system(LDAS)is how to accurately estimate model and observation errors.To address this,we had previously proposed a dualcycle assimilation algorithm that can simultaneously estimate the model and observation errors,LSM parameters,and observation operator parameters.However,this algorithm requires a large ensemble size to guarantee stable parameter estimates,resulting in low efficiency and limiting its large-scale applications.To address this limitation,the authors employed the following approaches:(1)using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM,and(2)replacing the ensemble Kalman filter framework with the extended Kalman filter(EKF)framework to improve the efficiency of parameter optimization for the dual-cycle algorithm.The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h.To evaluate the dual-cycle LDAS at the regional-scale,it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau,and soil moisture estimates were validated using in situ observations from four different climatic areas.The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture,land surface temperature,and evapotranspiration than did the open loop with default parameters.Furthermore,the spatial distribution of soil parameters(sand content,clay content,and porosity)obtained from the LDAS was more reasonable than those of default values.The EKF-based dual-cycle algorithm developed in this study is expected to improve the assimilation skills of land surface,ecological,and hydrological studies.
