High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

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.

Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface urban heat island intensity (SUHII) for the 32 major cities in mainland China, using MODIS land surface temperature data from 2008 to 2012, and estimated the energy consumption intensity (ECI) based on the correlation between energy consumption and the sum of nighttime lights. On this basis, the impact of energy consumption on the surface urban heat island in China’s 32 major cities was analyzed, by directly examining the relationship between SUHII and the urban-suburban difference in ECI. The results show that energy consumption has a significantly positive correlation with the nighttime SUHII, but no correlation with the daytime SUHII. It indicates that the cities with a larger urban-suburban difference in ECI have a far greater impact on SUHII during the nighttime. Therefore, the statistical analysis of the historical observation data in this study provides evidence for a long-held hypothesis that the anthropogenic heat released from energy consumption is an important contributor to the urban thermal environment.

Vegetation carbon phenology is a key indicator of vegetation actual photosynthesis activity and regulates terrestrial ecosystem carbon balance. A comprehensive understanding of the structural difference of carbon phenology and greenness phenology is still lacking. This study evaluated the structural difference of vegetation greenness phenology and carbon phenology on 95 eddy covariance flux sites (637 site‐years). Vegetation greenness phenology is extracted from remote sensing vegetation indices (i.e., enhanced vegetation index [EVI] and normalized difference vegetation index [NDVI]) of Moderate Resolution Imaging Spectroradiometer satellites; carbon phenology is extracted from daily gross primary production estimates at flux sites. We found that there exists remarkable asynchronization between vegetation greenness phenology and carbon phenology on the 95 flux sites. Generally, the asynchronization is more prevalent in the withering season, with the bias about 18.7 ± 13.0 days for EVI and about 29.8 ± 14.4 days for NDVI. In comparison, in the green‐up season, the bias is only about −1.6 ± 12.9 days for EVI and −6.0 ± 13.8 days for NDVI. We found that photoperiod is the dominant factor controlling the asynchronization and the decrease of photosynthetic capacity in the withering season, other than photosynthetically active radiation or temperature. Our study also reveals that EVI × PC and NDVI × PC are good proxies of vegetation carbon phenology, where PC is the photoperiod constraint and is quantified as . Using PC in the withering season reduces the bias of EVI from 18.7 ± 13.0 days to −2.4 ± 11.2 days and the bias of NDVI from 29.8 ± 14.4 days to −1.1 ± 12.7 days.