Modeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently there is no dataset of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which were evaluated using the global coverage NCSS soil water retention curve database and showed better performance for global applications than the individual PTF and ensemble mean PTFs estimations. Soil hydraulic conductivities are estimated as the median values of multiple PTFs as done in Dai et al. [2013], and the results are shown to perform comparably to the estimates from the existing precision‐advanced models. Soil thermal properties are estimated following the schemes identified by Dai et al. [2019a], which evaluated several highly‐recommended schemes based on their land modeling applications. Using these approaches, we developed two global high‐resolution datasets of soil hydraulic and thermal parameters based on GSDE and SoilGrids soil composition databases. The delivered variables include six basic soil properties, four soil hydraulic parameters in the Campbell [1974] model, five soil hydraulic parameters in the van Genuchten [1980] model, and four soil thermal properties. The delivered datasets are available at a 30″× 30″ geographical spatial resolution, and provide four sets of vertical profiles following the resolutions of SoilGrids, Noah‐LSM, JULES, and CoLM/CLM. The datasets can be used in both regional and global applications.

Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

Extensive studies have focused on instantaneous and time‐lag impacts of climatic factors on vegetation growth; however, the chronical and accumulative indirect impacts of antecedent climatic factors carrying over for a period of time on vegetation growth, defined as cumulative effects, are less investigated. Here we aimed to disentangle the cumulative effects of climatic factors on vegetation growth by using vegetation indexes and accumulated meteorological data. First, we investigated the explanation and fit of climate changes on vegetation variations by applying stepwise multiple linear regression with Akaike information criterion. Then, we obtained the correlation coefficients and lagged time of climatic factors on vegetation growth whereby partial correlation and time‐lag effect analyses. Results showed that (i) consideration of cumulative climate effects increased the explanation and fit of climate changes on vegetation dynamics for more than 77% of vegetated surface with an average global explanation of 68.33%, which was approximately 3.35% higher than the scenario when only time‐lag effects were considered; (ii) big differences exhibited in the correlation coefficients and lagged times under the scenarios with cumulative climate effects considered or not; and (iii) positive accumulated temperature (accumulated solar radiation) effects with zero (three‐month) time lag dominates most mid‐high latitude ecosystems, and negative accumulated temperature effects with three‐month delay dominates the temperate arid and semiarid regions and tropical dry ecosystems. By comparison, accumulated precipitation had relatively complex cumulative effects on vegetation growth. We concluded that climatic factors had significant cumulative effects on vegetation growth; consideration of the cumulative effects helps us better understand the climate‐vegetation interactions.

Since the launch of the first land-observation satellite (Landsat-1) in 1972, land-cover mapping has accumulated a wide range of knowledge in the peer-reviewed literature. However, this knowledge has never been comprehensively analysed for new discoveries. Here, we developed the first spatialized database of scientific literature in English about land-cover mapping. Using this database, we tried to identify the spatial temporal patterns and spatial hotspots of land-cover mapping research around the world. Among other findings, we observed (1) a significant mismatch between hotspot areas of land-cover mapping and areas that are either hard to map or rich in biodiversity; (2) mapping frequency is positively related to economic conditions; (3) there is no obvious temporal trend showing improvement in mapping accuracy; (4) images with more spectral bands or a combination of data types resulted in increased mapping accuracies; (5) accuracy differences due to algorithm differences are not as large as those due to various types of data used; and (6) the complexity of a classification system decreases its mapping accuracy. We recommend that one way to improve our understanding of the challenges, advances, and applications of previous land-cover mapping is for journals to require area-based information at the time of manuscript submission. In addition, building a standard protocol for systematic assessment of land-cover mapping efforts at the global scale through international collaboration is badly needed.