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Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth's carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in Earth system science that affects various hydrological and agricultural processes. Convolutional long short-term memory (Conv-LSTM) networks are widely used deep learning models for spatio-temporal SM prediction, but they are often regarded as black boxes that lack interpretability and transparency. This study aims to interpret Conv-LSTM for spatio-temporal SM prediction in China, using the permutation importance and smooth gradient methods for global and local interpretation, respectively. The trained Conv-LSTM model achieved a high R2 of 0.92. The global interpretation revealed that precipitation and soil properties are the most important factors affecting SM prediction. Furthermore, the local interpretation showed that the seasonality of variables was more evident in the high-latitude regions, but their effects were stronger in low-latitude regions. Overall, this study provides a novel approach to enhance the trust-building for Conv-LSTM models and to demonstrate the potential of artificial intelligence-assisted Earth system modeling and understanding element prediction in the future. [ABSTRACT FROM AUTHOR]

Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) has significant impacts on the Earth's energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate and comprehensive real-time forecast of SM, we propose a spatial–temporal deep learning model based on the Convolutional Gated Recursive Units with Data Integration (DI_ConvGRU) to capture the spatial and temporal variation in SM simultaneously by modeling the influence of adjacent SM values in space and time. Experiments show that the DI_ConvGRU outperforms the ConvGRU with Linear Interpolation (interp_ConvGRU) and the Long Short-Term Memory with Data Integration (DI_LSTM). The best performance (Bias = 0.0132 m3/m3, ubRMSE = 0.022 m3/m3, R = 0.977) has been achieved through the use of spatial–temporal deep learning model and Data Integration term. In comparison with interp_ConvGRU and DI_LSTM, DI_ConvGRU has improved the model performance in 74.88% and 68.99% of the regions according to RMSE, respectively. The predictability of SM depends highly on SM memory characteristics. DI_ConvGRU can provide accurate spatial–temporal forecast for SM with missing data, making them potentially useful for applications such as filling observational gaps in satellite data. [ABSTRACT FROM AUTHOR]

Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information. [ABSTRACT FROM AUTHOR]

Accurate assessment of agricultural drought is useful for ecosystem services. This is a successive work of our previous study that assessed agricultural drought using the soil water deficit index (SWDI) based on ERA5-Land in the four southern provinces of China from 2017 to 2019. Firstly, in addition to ERA5-Land, the suitability of CLDAS (China Land Data Assimilation System) soil moisture for drought assessment was investigated. Then, the study was extended with more comprehensive analysis and a much longer period (1981–2020). Based on three climate zones, in situ soil moisture was used for evaluation of both reanalysis datasets and agricultural drought. It was found that ERA5-Land_SWDI and CLDAS_SWDI have a good correlation with the in situ SWDI. ERA5-Land and CLDAS demonstrate some differences in representing agricultural drought but have a similar performance evaluated by in situ soil moisture. Droughts from 2001 to 2010 were more serious than in the other three decades, and droughts have become longer and severer in some areas in the last 40 years. There was a good correlation between agricultural drought and meteorological drought. Our work offers important insights for agricultural drought risk management in the four southern provinces of China. [ABSTRACT FROM AUTHOR]

Transpiration represents more than 30% of the global land–atmosphere water exchange but is highly uncertain. Plant hydraulics was ignored in traditional land surface modeling, but recently plant hydraulics has been found to play an essential role in transpiration simulation. A new physical-based representation of plant hydraulic schemes (PHS) was recently developed and implemented in the Common Land Model (CoLM). However, it is unclear to what extent PHS can reduce these uncertainties. Here, we evaluated the PHS against measurements obtained at 81 FLUXNET sites. The transpiration of each site was estimated using an empirical evapotranspiration partitioning approach. The metric scores defined by the International Land Model Benchmarking Project (ILAMB) were used to evaluate the model performance and compare it with that of the CoLM default scheme (soil moisture stress (SMS)). The bias score of transpiration in PHS was higher than SMS for most sites, and more significant improvements were found in semi-arid and arid sites where transpiration was limited by soil moisture. The hydraulic redistribution in PHS optimized the soil water supply and thus improved the transpiration estimates. In humid sites, no significant improvement in seasonal or interannual variability of transpiration was simulated by PHS, which can be explained by the insensitivity of transpiration demand coupled to the photosynthesis response to precipitation. In arid and semi-arid sites, seasonal or interannual variability of transpiration was better captured by PHS than SMS, which was interpreted by the improved drought sensitivity for transpiration. Arid land is widespread and is expected to expand due to climate change, thus there is an urgent need to couple PHS in land surface models. [ABSTRACT FROM AUTHOR]

It is important to accurately assess agricultural drought because of its harmful impacts on the ecosystem and economy. Soil moisture reanalysis datasets provide an important way to assess agricultural drought. In this study, the ERA5-Land surface and subsurface soil moisture was used to estimate the soil water deficit index (SWDI) in four southern provinces of China. The ERA5-Land dataset was evaluated with in situ soil moisture observations from agrometeorological stations. Agricultural drought was assessed for three climate zones at a weekly scale from 2017 to 2019 and was compared with the atmospheric water deficit (AWD). It was found that both ERA5-Land soil moisture and the derived SWDI have relatively high accuracy, and the wet bias in the ERA5-Land dataset can be reduced by the calculation of the SWDI. The subsurface layer has better performance than the surface layer in drought monitoring, though they are highly correlated. Different climate zones demonstrate different drought periods and drought severity, and the temperate climate zone with no dry season has less droughts. The most severe droughts with the largest spatial extent occurred in the early winter, especially in 2019. Differences in the SWDI and AWD are mainly shown in southwestern Yunnan. The results of this study have important reference values for drought risk management. [ABSTRACT FROM AUTHOR]

To enhance the reality of Connected and Autonomous Vehicles (CAVs) kinematic simulation scenarios and to guarantee the accuracy and reliability of the verification, a four-layer CAVs kinematic simulation framework, which is composed with road network layer, vehicle operating layer, uncertainties modelling layer and demonstrating layer, is proposed in this paper. Properties of the intersections are defined to describe the road network. A target position based vehicle position updating method is designed to simulate such vehicle behaviors as lane changing and turning. Vehicle kinematic models are implemented to maintain the status of the vehicles when they are moving towards the target position. Priorities for individual vehicle control are authorized for different layers. Operation mechanisms of CAVs uncertainties, which are defined as position error and communication delay in this paper, are implemented in the simulation to enhance the reality of the simulation. A simulation platform is developed based on the proposed methodology. A comparison of simulated and theoretical vehicle delay has been analyzed to prove the validity and the creditability of the platform. The scenario of rear-end collision avoidance is conducted to verify the uncertainties operating mechanisms, and a slot-based intersections (SIs) control strategy is realized and verified in the simulation platform to show the supports of the platform to CAVs kinematic simulation and verification., Competing Interests: The authors declare no conflict of interest.