Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
05 Jan 2021
Data description paper |
| 05 Jan 2021
| 05 Jan 2021
An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018
Yongzhe Chen
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049,
China
Xiaoming Feng
CORRESPONDING AUTHOR
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
Bojie Fu
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049,
China
Related authors
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
Short summary
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
Short summary
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Jinxia An, Guangyao Gao, Chuan Yuan, Juan Pinos, and Bojie Fu
Hydrol. Earth Syst. Sci., 26, 3885–3900, https://doi.org/10.5194/hess-26-3885-2022, https://doi.org/10.5194/hess-26-3885-2022, 2022
Short summary
Short summary
An in-depth investigation was conducted of all rainfall-partitioning components at inter- and intra-event scales for two xerophytic shrubs. Inter-event rainfall partitioning amount and percentage depended more on rainfall amount, and rainfall intensity and duration controlled intra-event rainfall-partitioning variables. One shrub has larger branch angle, small branch and smaller canopy area to produce stemflow more efficiently, and the other has larger biomass to intercept more rainfall.
Shuang Song, Shuai Wang, Xutong Wu, Yongyuan Huang, and Bojie Fu
Hydrol. Earth Syst. Sci., 26, 2035–2044, https://doi.org/10.5194/hess-26-2035-2022, https://doi.org/10.5194/hess-26-2035-2022, 2022
Short summary
Short summary
A reasonable assessment of the contribution of the water resources in a river basin to domestic crops supplies will be the first step in balancing the water–food nexus. Our results showed that although the Yellow River basin had reduced its virtual water outflow, its importance to crop production in China had been increasing when water footprint networks were considered. Our complexity-based approach provides a new perspective for understanding changes in a basin with a severe water shortage.
Bojie Fu, Xutong Wu, Zhuangzhuang Wang, Xilin Wu, and Shuai Wang
Earth Syst. Dynam., 13, 795–808, https://doi.org/10.5194/esd-13-795-2022, https://doi.org/10.5194/esd-13-795-2022, 2022
Short summary
Short summary
To understand the dynamics of a coupled human and natural system (CHANS) and promote its sustainability, we propose a conceptual
pattern–process–service–sustainabilitycascade framework. The use of this framework is systematically illustrated by a review of CHANS research experience in China's Loess Plateau in terms of coupling landscape patterns and ecological processes, linking ecological processes to ecosystem services, and promoting social–ecological sustainability.
Maierdang Keyimu, Zongshan Li, Bojie Fu, Guohua Liu, Fanjiang Zeng, Weiliang Chen, Zexin Fan, Keyan Fang, Xiuchen Wu, and Xiaochun Wang
Clim. Past, 17, 2381–2392, https://doi.org/10.5194/cp-17-2381-2021, https://doi.org/10.5194/cp-17-2381-2021, 2021
Short summary
Short summary
We created a residual tree-ring width chronology and reconstructed non-growth-season precipitation (NGSP) over the period spanning 1600–2005 in the southeastern Tibetan Plateau (SETP), China. Reconstruction model verification as well as similar variations of NGSP reconstruction and Palmer Drought Severity Index reconstructions from the surrounding region indicate the reliability of the present reconstruction. Our reconstruction is representative of NGSP variability of a large region in the SETP.
Xuejing Leng, Xiaoming Feng, Bojie Fu, and Yu Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-377, https://doi.org/10.5194/hess-2021-377, 2021
Manuscript not accepted for further review
Short summary
Short summary
At present, there is a lack of time series of runoff generated by glacial regions in the world. In this paper, we quantified glacial runoff (including meltwater runoff and delayed runoff) in arid regions of China from 1961 to 2015 by using remote sensing datasets of glacier mass balance with high resolution. Glacier runoff is the water resource used by oases in arid regions of China. The long-term glacial runoff data can indicate the climate risk faced by different basins in arid regions.
Xianfeng Liu, Xiaoming Feng, Philippe Ciais, and Bojie Fu
Hydrol. Earth Syst. Sci., 24, 3663–3676, https://doi.org/10.5194/hess-24-3663-2020, https://doi.org/10.5194/hess-24-3663-2020, 2020
Short summary
Short summary
Freshwater availability is crucial for sustainable development across the Asian and eastern European regions. Our results indicate widespread decline in terrestrial water storage (TWS) over the region during 2002–2017, primarily due to the intensive over-extraction of groundwater and warmth-induced surface water loss. The findings provide insights into changes in TWS and its components over the Asian and eastern European regions, where there is growing demand for food grains and water supplies.
Jianjun Zhang, Guangyao Gao, Bojie Fu, Cong Wang, Hoshin V. Gupta, Xiaoping Zhang, and Rui Li
Hydrol. Earth Syst. Sci., 24, 809–826, https://doi.org/10.5194/hess-24-809-2020, https://doi.org/10.5194/hess-24-809-2020, 2020
Short summary
Short summary
We proposed an approach that integrates universal multifractals and a segmentation algorithm to precisely identify extreme precipitation (EP) and assess spatiotemporal EP variation over the Loess Plateau, using daily data. Our results explain how EP contributes to the widely distributed severe natural hazards. These findings are of great significance for ecological management in the Loess Plateau. Our approach is also helpful for spatiotemporal EP assessment at the regional scale.
Chuan Yuan, Guangyao Gao, Bojie Fu, Daming He, Xingwu Duan, and Xiaohua Wei
Hydrol. Earth Syst. Sci., 23, 4077–4095, https://doi.org/10.5194/hess-23-4077-2019, https://doi.org/10.5194/hess-23-4077-2019, 2019
Short summary
Short summary
The stemflow dynamics of two xerophytic shrubs were investigated at the inter- and intra-event scales with high-temporal-resolution data in 54 rain events. Stemflow process was depicted by intensity, duration and time lags to rain events. Funneling ratio was calculated as the ratio of stemflow to rainfall intensities. Rainfall intensity and raindrop momentum controlled stemflow intensity and time lags. Influences of rainfall characteristics on stemflow variables showed temporal dependence.
Yuan Zhang, Xiaoming Feng, Xiaofeng Wang, and Bojie Fu
Hydrol. Earth Syst. Sci., 22, 1749–1766, https://doi.org/10.5194/hess-22-1749-2018, https://doi.org/10.5194/hess-22-1749-2018, 2018
Short summary
Short summary
We characterized drought by linking climate anomalies with the change in precipitation–runoff relationships in China's Loess Plateau, where drought is of major concern for revegetation. Multi-year drought causes a change in the precipitation–runoff relationship in this water limited area. The drought causing a decrease in runoff ratio is vital to ecosystem management. The revegetation in the Loess Plateau should live with the spatially varied drought.
Guangyao Gao, Jianjun Zhang, Yu Liu, Zheng Ning, Bojie Fu, and Murugesu Sivapalan
Hydrol. Earth Syst. Sci., 21, 4363–4378, https://doi.org/10.5194/hess-21-4363-2017, https://doi.org/10.5194/hess-21-4363-2017, 2017
Short summary
Short summary
This study extracted spatio-temporal patterns in the effects of LUCC and precipitation variability on sediment yield across the Loess Plateau during 1961–2011. The impacts of precipitation on sediment yield declined with time and the precipitation-sediment relationship showed a coherent spatial pattern. The sediment coefficient, representing the effect of LUCC, decreases linearly with fraction of area treated with erosion control measures and the slopes were highly variable among the catchments.
Yonggang Yang and Bojie Fu
Hydrol. Earth Syst. Sci., 21, 1757–1767, https://doi.org/10.5194/hess-21-1757-2017, https://doi.org/10.5194/hess-21-1757-2017, 2017
Short summary
Short summary
This paper investigates soil water migration processes in the Loess Plateau using isotopes. The soil water migration is dominated by piston-type flow, but rarely preferential flow. Soil water from the soil lay (20–40 cm) contributed to 6–12% of plant xylem water, while soil water at the depth of 40–60 cm is the largest component (range from 60 to 66 %), soil water below 60 cm depth contributed 8–14 % to plant xylem water, and only 5–8 % is derived from precipitation.
Ji Zhou, Bojie Fu, Guangyao Gao, Yihe Lü, and Shuai Wang
Hydrol. Earth Syst. Sci., 21, 1491–1514, https://doi.org/10.5194/hess-21-1491-2017, https://doi.org/10.5194/hess-21-1491-2017, 2017
Short summary
Short summary
We constructed an integrated probabilistic assessment to describe, simulate and evaluate the stochasticity of soil erosion in restoration vegetation in the Loess Plateau. We found that morphological structures in vegetation are the source of different stochasticities of soil erosion, and proved that the Poisson model is fit for predicting erosion stochasticity. This assessment could be an important complement to develop restoration strategies to improve understanding of stochasticity of erosion.
Chuan Yuan, Guangyao Gao, and Bojie Fu
Hydrol. Earth Syst. Sci., 21, 1421–1438, https://doi.org/10.5194/hess-21-1421-2017, https://doi.org/10.5194/hess-21-1421-2017, 2017
Short summary
Short summary
We computed stemflow yield and efficiency, and analyzed the influential mechanism at smaller scales of leaf and raindrop. We found that precipitation was the most influential meteorological feature on stemflow. The smaller threshold precipitation to start stemflow and the more beneficial leaf traits might partly explain the larger and more efficient stemflow production. At defoliated period, the newly exposed stems replaced leaves to intercept raindrops and might really matter in stemflow yield.
N. Lu, J. Liski, R. Y. Chang, A. Akujärvi, X. Wu, T. T. Jin, Y. F. Wang, and B. J. Fu
Biogeosciences, 10, 7053–7063, https://doi.org/10.5194/bg-10-7053-2013, https://doi.org/10.5194/bg-10-7053-2013, 2013
J. Zhou, B. J. Fu, N. Lü, G. Y. Gao, Y. H. Lü, and S. Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-10083-2013, https://doi.org/10.5194/hessd-10-10083-2013, 2013
Revised manuscript not accepted
Y. D. Xu, B. J. Fu, and C. S. He
Hydrol. Earth Syst. Sci., 17, 2185–2193, https://doi.org/10.5194/hess-17-2185-2013, https://doi.org/10.5194/hess-17-2185-2013, 2013
Related subject area
Pedology
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
European soil bulk density and organic carbon stock database using machine learning based pedotransfer function
The patterns of soil nitrogen stocks and C : N stoichiometry under impervious surfaces in China
Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices
Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
Colombian soil texture: building a spatial ensemble model
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
A repository of measured soil freezing characteristic curves: 1921 to 2021
A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records
The Boreal–Arctic Wetland and Lake Dataset (BAWLD)
A first investigation of hydrogeology and hydrogeophysics of the Maqu catchment in the Yellow River source region
Radionuclide contamination in flood sediment deposits in the coastal rivers draining the main radioactive pollution plume of Fukushima Prefecture, Japan (2011–2020)
Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019
EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia
A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models
Spatial radionuclide deposition data from the 60 km radial area around the Chernobyl Nuclear Power Plant: results from a sampling survey in 1987
Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska
Soil moisture and matric potential – an open field comparison of sensor systems
CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort
An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0
Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau
Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone
A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves
WoSIS: providing standardised soil profile data for the world
Post-Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain
A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region
The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Richer-de-Forges, and Zhou Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-493, https://doi.org/10.5194/essd-2023-493, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We produced a new dataset for soil bulk density (BD) and soil organic carbon (SOC) stock across Europe using machine learning. The proposed approach performed much better in BD prediction and slightly better in SOC stock prediction than traditional pretransfer functions. The outcomes present a meaningful advancement in enhancing the predictive accuracy of BD, and the resultant BD and SOC stock datasets across the Europe enable more precise soil hydrological and biological modelling.
Qian Ding, Hua Shao, Chi Zhang, and Xia Fang
Earth Syst. Sci. Data, 15, 4599–4612, https://doi.org/10.5194/essd-15-4599-2023, https://doi.org/10.5194/essd-15-4599-2023, 2023
Short summary
Short summary
A soil survey in 41 Chinese cities showed the soil nitrogen (N) in impervious surface areas (ISA; NISA) was 0.59±0.35 kg m−2, lower than in pervious soils. Eastern China had the highest NISA but the lowest natural soil N in China. Soil N decreased linearly with depth in ISA but nonlinearly in natural ecosystems. Temperature was negatively correlated with C : NISA but positively correlated with natural soil C : N. The unique NISA patterns imply intensive disturbance in N cycle by soil sealing.
Lukas Rimondini, Thomas Gumbricht, Anders Ahlström, and Gustaf Hugelius
Earth Syst. Sci. Data, 15, 3473–3482, https://doi.org/10.5194/essd-15-3473-2023, https://doi.org/10.5194/essd-15-3473-2023, 2023
Short summary
Short summary
Peatlands have historically sequestrated large amounts of carbon and contributed to atmospheric cooling. However, human activities and climate change may instead turn them into considerable carbon emitters. In this study, we produced high-quality maps showing the extent of peatlands in the forests of Sweden, one of the most peatland-dense countries in the world. The maps are publicly available and may be used to support work promoting sustainable peatland management and combat their degradation.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
Short summary
Short summary
The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 395–409, https://doi.org/10.5194/essd-15-395-2023, https://doi.org/10.5194/essd-15-395-2023, 2023
Short summary
Short summary
We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
Short summary
Short summary
These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
Short summary
Short summary
The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
Short summary
Short summary
Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
Short summary
Short summary
We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
Yueli Chen, Xingwu Duan, Minghu Ding, Wei Qi, Ting Wei, Jianduo Li, and Yun Xie
Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, https://doi.org/10.5194/essd-14-2681-2022, 2022
Short summary
Short summary
We reconstructed the first annual rainfall erosivity dataset for the Tibetan Plateau in China. The dataset covers 71 years in a 0.25° grid. The reanalysis precipitation data are employed in combination with the densely spaced in situ precipitation observations to generate the dataset. The dataset can supply fundamental data for quantifying the water erosion, and extend our knowledge of the rainfall-related hazard prediction on the Tibetan Plateau.
Alexander R. Groos, Janik Niederhauser, Bruk Lemma, Mekbib Fekadu, Wolfgang Zech, Falk Hänsel, Luise Wraase, Naki Akçar, and Heinz Veit
Earth Syst. Sci. Data, 14, 1043–1062, https://doi.org/10.5194/essd-14-1043-2022, https://doi.org/10.5194/essd-14-1043-2022, 2022
Short summary
Short summary
Continuous observations and measurements from high elevations are necessary to monitor recent climate and environmental changes in the tropical mountains of eastern Africa, but meteorological and ground temperature data from above 3000 m are very rare. Here we present a comprehensive ground temperature monitoring network that has been established between 3493 and 4377 m in the Bale Mountains (Ethiopian Highlands) to monitor and study the afro-alpine climate and ecosystem in this region.
Tianyu Yue, Shuiqing Yin, Yun Xie, Bofu Yu, and Baoyuan Liu
Earth Syst. Sci. Data, 14, 665–682, https://doi.org/10.5194/essd-14-665-2022, https://doi.org/10.5194/essd-14-665-2022, 2022
Short summary
Short summary
This paper provides new rainfall erosivity maps over mainland China based on hourly data from 2381 stations (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001). The improvement from the previous work was also assessed. The improvement in the R-factor map occurred mainly in the western region, because of an increase in the number of stations and an increased temporal resolution from daily to hourly data.
David Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne E. Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, and Jennifer D. Watts
Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, https://doi.org/10.5194/essd-13-5127-2021, 2021
Short summary
Short summary
Wetlands, lakes, and rivers are important sources of the greenhouse gas methane to the atmosphere. To understand current and future methane emissions from northern regions, we need maps that show the extent and distribution of specific types of wetlands, lakes, and rivers. The Boreal–Arctic Wetland and Lake Dataset (BAWLD) provides maps of five wetland types, seven lake types, and three river types for northern regions and will improve our ability to predict future methane emissions.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
Short summary
Short summary
The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Olivier Evrard, Caroline Chartin, J. Patrick Laceby, Yuichi Onda, Yoshifumi Wakiyama, Atsushi Nakao, Olivier Cerdan, Hugo Lepage, Hugo Jaegler, Rosalie Vandromme, Irène Lefèvre, and Philippe Bonté
Earth Syst. Sci. Data, 13, 2555–2560, https://doi.org/10.5194/essd-13-2555-2021, https://doi.org/10.5194/essd-13-2555-2021, 2021
Short summary
Short summary
This dataset provides an original compilation of radioactive dose rates and artificial radionuclide activities in sediment deposited after floods in the rivers draining the main radioactive pollution plume in Fukushuma, Japan, between November
2011 and November 2020. In total, 782 sediment samples collected from 27 to 71 locations during 16 fieldwork campaigns were analysed. This provides a unique post-accidental dataset to better understand the environmental fate of radionuclides.
Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang
Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, https://doi.org/10.5194/essd-13-1385-2021, 2021
Short summary
Short summary
Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.
Alexander Kmoch, Arno Kanal, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 83–97, https://doi.org/10.5194/essd-13-83-2021, https://doi.org/10.5194/essd-13-83-2021, 2021
Short summary
Short summary
The Soil Map of Estonia is the most detailed and information-rich dataset for soils in Estonia. But its information is not immediately usable for analyses or modelling. We derived parameters including soil layering, soil texture (clay, silt, and sand content), coarse fragments, and rock content and aggregated and predicted physical variables related to water and carbon cycles (bulk density, hydraulic conductivity, organic carbon content, available water capacity).
Xavier Morel, Birger Hansen, Christine Delire, Per Ambus, Mikhail Mastepanov, and Bertrand Decharme
Earth Syst. Sci. Data, 12, 2365–2380, https://doi.org/10.5194/essd-12-2365-2020, https://doi.org/10.5194/essd-12-2365-2020, 2020
Short summary
Short summary
Nuuk fen site is a well-instrumented Greenlandic site where soil physical variables and greenhouse gas fluxes are monitored. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models. For the first time we measured soil carbon and nitrogen density, profiles, and stocks in the Nuuk peatland. This new dataset can contribute to further develop joint modeling of greenhouse gas emissions and soil carbon in land-surface models.
Valery Kashparov, Sviatoslav Levchuk, Marina Zhurba, Valentyn Protsak, Nicholas A. Beresford, and Jacqueline S. Chaplow
Earth Syst. Sci. Data, 12, 1861–1875, https://doi.org/10.5194/essd-12-1861-2020, https://doi.org/10.5194/essd-12-1861-2020, 2020
Short summary
Short summary
Sampling and analysis methodology and spatial radionuclide deposition data from the 60 km area around the Chernobyl Nuclear Power Plant, sampled in 1987 by the Ukrainian Institute of Agricultural Radiology, are useful for reconstructing doses to human and wildlife populations, answering the current lack of scientific consensus on the effects of radiation on wildlife in the Chernobyl Exclusion Zone and evaluating future management options for the Chernobyl-impacted areas of Ukraine and Belarus.
Kristen Manies, Mark Waldrop, and Jennifer Harden
Earth Syst. Sci. Data, 12, 1745–1757, https://doi.org/10.5194/essd-12-1745-2020, https://doi.org/10.5194/essd-12-1745-2020, 2020
Short summary
Short summary
Boreal ecosystems are unique in that their mineral soil is covered by what can be quite thick layers of organic soil. Layers within this organic soil have different bulk densities, carbon composition, and nitrogen composition. We summarize these properties by soil layer and examine if and how they are affected by soil drainage and stand age. These values can be used to initialize and validate models as well as gap fill when these important soil properties are not measured.
Conrad Jackisch, Kai Germer, Thomas Graeff, Ines Andrä, Katrin Schulz, Marcus Schiedung, Jaqueline Haller-Jans, Jonas Schneider, Julia Jaquemotte, Philipp Helmer, Leander Lotz, Andreas Bauer, Irene Hahn, Martin Šanda, Monika Kumpan, Johann Dorner, Gerrit de Rooij, Stefan Wessel-Bothe, Lorenz Kottmann, Siegfried Schittenhelm, and Wolfgang Durner
Earth Syst. Sci. Data, 12, 683–697, https://doi.org/10.5194/essd-12-683-2020, https://doi.org/10.5194/essd-12-683-2020, 2020
Short summary
Short summary
Soil water content and matric potential are central hydrological state variables. A large variety of automated probes and sensor systems for field monitoring exist. In a field experiment under idealised conditions we compared 15 systems for soil moisture and 14 systems for matric potential. The individual records of one system agree well with the others. Most records are also plausible. However, the absolute values of the different measuring systems span a very large range of possible truths.
Marco Pfeiffer, José Padarian, Rodrigo Osorio, Nelson Bustamante, Guillermo Federico Olmedo, Mario Guevara, Felipe Aburto, Francisco Albornoz, Monica Antilén, Elías Araya, Eduardo Arellano, Maialen Barret, Juan Barrera, Pascal Boeckx, Margarita Briceño, Sally Bunning, Lea Cabrol, Manuel Casanova, Pablo Cornejo, Fabio Corradini, Gustavo Curaqueo, Sebastian Doetterl, Paola Duran, Mauricio Escudey, Angelina Espinoza, Samuel Francke, Juan Pablo Fuentes, Marcel Fuentes, Gonzalo Gajardo, Rafael García, Audrey Gallaud, Mauricio Galleguillos, Andrés Gomez, Marcela Hidalgo, Jorge Ivelic-Sáez, Lwando Mashalaba, Francisco Matus, Francisco Meza, Maria de la Luz Mora, Jorge Mora, Cristina Muñoz, Pablo Norambuena, Carolina Olivera, Carlos Ovalle, Marcelo Panichini, Aníbal Pauchard, Jorge F. Pérez-Quezada, Sergio Radic, José Ramirez, Nicolás Riveras, Germán Ruiz, Osvaldo Salazar, Iván Salgado, Oscar Seguel, Maria Sepúlveda, Carlos Sierra, Yasna Tapia, Francisco Tapia, Balfredo Toledo, José Miguel Torrico, Susana Valle, Ronald Vargas, Michael Wolff, and Erick Zagal
Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, https://doi.org/10.5194/essd-12-457-2020, 2020
Short summary
Short summary
The CHLSOC database is the biggest soil organic carbon (SOC) database that has been compiled for Chile yet, comprising 13 612 data points. This database is the product of the compilation of numerous sources including unpublished and difficult-to-access data, allowing us to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC have a wide range, reflecting the variety of ecosystems that exists in Chile.
Corey R. Lawrence, Jeffrey Beem-Miller, Alison M. Hoyt, Grey Monroe, Carlos A. Sierra, Shane Stoner, Katherine Heckman, Joseph C. Blankinship, Susan E. Crow, Gavin McNicol, Susan Trumbore, Paul A. Levine, Olga Vindušková, Katherine Todd-Brown, Craig Rasmussen, Caitlin E. Hicks Pries, Christina Schädel, Karis McFarlane, Sebastian Doetterl, Christine Hatté, Yujie He, Claire Treat, Jennifer W. Harden, Margaret S. Torn, Cristian Estop-Aragonés, Asmeret Asefaw Berhe, Marco Keiluweit, Ágatha Della Rosa Kuhnen, Erika Marin-Spiotta, Alain F. Plante, Aaron Thompson, Zheng Shi, Joshua P. Schimel, Lydia J. S. Vaughn, Sophie F. von Fromm, and Rota Wagai
Earth Syst. Sci. Data, 12, 61–76, https://doi.org/10.5194/essd-12-61-2020, https://doi.org/10.5194/essd-12-61-2020, 2020
Short summary
Short summary
The International Soil Radiocarbon Database (ISRaD) is an an open-source archive of soil data focused on datasets including radiocarbon measurements. ISRaD includes data from bulk or
whole soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil surface.
Hong Zhao, Yijian Zeng, Shaoning Lv, and Zhongbo Su
Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, https://doi.org/10.5194/essd-10-1031-2018, 2018
Short summary
Short summary
The Tibet-Obs soil properties dataset was compiled based on in situ and laboratory measurements of soil profiles across three climate zones on the Tibetan Plateau. The appropriate parameterization schemes of soil hydraulic and thermal properties were discussed for their applicability in land surface modeling. The uncertainties of existing soil datasets were evaluated. This paper contributes to land surface modeling and hydro-climatology communities for their studies of the third pole region.
Valery Kashparov, Sviatoslav Levchuk, Marina Zhurba, Valentyn Protsak, Yuri Khomutinin, Nicholas A. Beresford, and Jacqueline S. Chaplow
Earth Syst. Sci. Data, 10, 339–353, https://doi.org/10.5194/essd-10-339-2018, https://doi.org/10.5194/essd-10-339-2018, 2018
Short summary
Short summary
Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone describe data from analysis of samples collected by the Ukrainian Institute of Agricultural Radiology after the Chernobyl nuclear accident between May 1986 and 2014 at sites inside the Chernobyl Exclusion Zone and other areas of interest. The data and supporting documentation are freely available from the Environmental Information Data Centre: https://doi.org/10.5285/782ec845-2135-4698-8881-b38823e533bf.
Carsten Montzka, Michael Herbst, Lutz Weihermüller, Anne Verhoef, and Harry Vereecken
Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, https://doi.org/10.5194/essd-9-529-2017, 2017
Short summary
Short summary
Global climate models require adequate parameterization of soil hydraulic properties, but typical resampling to the model grid introduces uncertainties. Here we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the problems. It preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters that enables modellers to perturb hydraulic parameters for model ensemble generation.
Niels H. Batjes, Eloi Ribeiro, Ad van Oostrum, Johan Leenaars, Tom Hengl, and Jorge Mendes de Jesus
Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, https://doi.org/10.5194/essd-9-1-2017, 2017
Short summary
Short summary
Soil is an important provider of ecosystem services. Yet this natural resource is being threatened. Professionals, scientists, and decision makers require quality-assessed soil data to address issues such as food security, land degradation, and climate change. Procedures for safeguarding, standardising, and subsequently serving of consistent soil data to underpin broad-scale mapping and modelling are described. The data are freely accessible at doi:10.17027/isric-wdcsoils.20160003.
J. S. Chaplow, N. A. Beresford, and C. L. Barnett
Earth Syst. Sci. Data, 7, 215–221, https://doi.org/10.5194/essd-7-215-2015, https://doi.org/10.5194/essd-7-215-2015, 2015
Short summary
Short summary
The data set ‘Post Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain’ was developed to enable data collected by the Natural Environment Research Council after the Chernobyl accident to be made publicly available. Data for samples collected between May 1986 (immediately after Chernobyl) to spring 1997 are freely available for non-commercial use under Open Government Licence terms and conditions. doi: 10.5285/d0a6a8bf-68f0-4935-8b43-4e597c3bf251.
G. Hugelius, J. G. Bockheim, P. Camill, B. Elberling, G. Grosse, J. W. Harden, K. Johnson, T. Jorgenson, C. D. Koven, P. Kuhry, G. Michaelson, U. Mishra, J. Palmtag, C.-L. Ping, J. O'Donnell, L. Schirrmeister, E. A. G. Schuur, Y. Sheng, L. C. Smith, J. Strauss, and Z. Yu
Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, https://doi.org/10.5194/essd-5-393-2013, 2013
G. Hugelius, C. Tarnocai, G. Broll, J. G. Canadell, P. Kuhry, and D. K. Swanson
Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, https://doi.org/10.5194/essd-5-3-2013, 2013
Cited articles
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer,
S., Isaksen, L., Kerr, Y., and Wagner, W.: Evaluation of remotely sensed and
modelled soil moisture products using global ground-based in situ
observations, Remote Sens. Environ., 118, 215–226,
https://doi.org/10.1016/j.rse.2011.11.017, 2012.
Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P.,
Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and
Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote
Sensing, J. Hydrometeorol., 14, 1259–1277, https://doi.org/10.1175/JHM-D-12-0161.1,
2013.
Al-Yaari, A., Wigneron, J. P., Ducharne, A., Kerr, Y. H., Wagner, W., De
Lannoy, G., Reichle, R., Al Bitar, A., Dorigo, W., Richaume, P., and Mialon,
A.: Global-scale comparison of passive (SMOS) and active (ASCAT) satellite
based microwave soil moisture retrievals with soil moisture simulations
(MERRA-Land), Remote Sens. Environ., 152, 614–626,
https://doi.org/10.1016/j.rse.2014.07.013, 2014.
Al-Yaari, A., Wigneron, J. P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez,
N., van der Schalie, R., Al Bitar, A., Mialon, A., Richaume, P., Dolman, A.,
and Ducharne, A.: Testing regression equations to derive long-term global
soil moisture datasets from passive microwave observations, Remote Sens.
Environ., 180, 453–464, https://doi.org/10.1016/j.rse.2015.11.022, 2016.
Al-Yaari, A., Wigneron, J. P., Dorigo, W., Colliander, A., Pellarin, T.,
Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y.
H., and De Lannoy, G.: Assessment and inter-comparison of recently
developed/reprocessed microwave satellite soil moisture products using ISMN
ground-based measurements, Remote Sens. Environ., 224, 289–303,
https://doi.org/10.1016/j.rse.2019.02.008, 2019.
An, R., Zhang, L., Wang, Z., Quaye-Ballard, J. A., You, J., Shen, X., Gao,
W., Huang, L., Zhao, Y., and Ke, Z.: Validation of the ESA CCI soil moisture
product in China, Int. J. Appl. Earth Obs. Geoinf., 48, 28–36,
https://doi.org/10.1016/j.jag.2015.09.009, 2016.
Andreasen, M., Jensen, K. H., Desilets, D., Franz, T. E., Zreda, M., Bogena,
H. R., and Looms, M. C.: Status and Perspectives on the Cosmic-Ray Neutron
Method for Soil Moisture Estimation and Other Environmental Science
Applications, Vadose Zone J., 16, 1–11, https://doi.org/10.2136/vzj2017.04.0086,
2017.
Baldocchi, D., Tang, J., and Xu, L.: How switches and lags in biophysical
regulators affect spatial-temporal variation of soil respiration in an
oak-grass savanna, J. Geophys. Res.-Biogeo., 111, G02008,
https://doi.org/10.1029/2005JG000063, 2006.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P.,
and Smets, B.: GEOV1: LAI and FAPAR essential climate variables and FCOVER
global time series capitalizing over existing products. Part 1: Principles of
development and production, Remote Sens. Environ., 137, 299–309,
https://doi.org/10.1016/j.rse.2012.12.027, 2013.
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp,
H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the
METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401,
https://doi.org/10.1029/2007GL031088, 2007.
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-184, in review, 2020.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The Concept of Essential Climate Variables in Support of Climate
Research, Applications, and Policy, B. Am. Meteorol. Soc., 95,
1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014.
Brooks, E. B., Thomas, V. A., Wynne, R. H., and Coulston, J. W.: Fitting the
Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem
in Remote Sensing Analysis, IEEE T. Geosci. Remote, 50,
3340–3353, https://doi.org/10.1109/TGRS.2012.2183137, 2012.
Burgin, M., Colliander, A., Njoku, E., Chan, S., Cabot, F., Kerr, H. Y.,
Bindlish, R., Jackson, T., Entekhabi, D., and Yueh, S.: A Comparative Study
of the SMAP Passive Soil Moisture Product With Existing Satellite-Based Soil
Moisture Products, IEEE T. Geosci. Remote, 55, 2959–2971,
https://doi.org/10.1109/TGRS.2017.2656859, 2017.
Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., and Weiss, M.: GEOV1:
LAI, FAPAR essential climate variables and FCOVER global time series
capitalizing over existing products. Part 2: Validation and intercomparison
with reference products, Remote Sens. Environ., 137, 310–329,
https://doi.org/10.1016/j.rse.2013.02.030, 2013.
Cammalleri, C., Verger, A., Lacaze, R., and Vogt, J. V.: Harmonization of
GEOV2 fAPAR time series through MODIS data for global drought monitoring,
Int. J. Appl. Earth Obs. Geoinf., 80, 1–12,
https://doi.org/10.1016/j.jag.2019.03.017, 2019.
Chan, S. K., Bindlish, R., O'Neill, P., Jackson, T., Njoku, E., Dunbar, S.,
Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen,
F., Cosh, M. H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault,
M., Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D.,
Starks, P., Holifield Collins, C., Prueger, J., van der Velde, R., Asanuma,
J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T., and Kerr,
Y.: Development and assessment of the SMAP enhanced passive soil moisture
product, Remote Sens. Environ., 204, 931–941,
https://doi.org/10.1016/j.rse.2017.08.025, 2018.
Chen, F., Crow, W. T., Bindlish, R., Colliander, A., Burgin, M. S., Asanuma,
J., and Aida, K.: Global-scale evaluation of SMAP, SMOS and ASCAT soil
moisture products using triple collocation, Remote Sens. Environ., 214,
1–13, https://doi.org/10.1016/j.rse.2018.05.008, 2018.
Chen, Y.: A new dataset of satellite observation-based global surface soil
moisture covering 2003–2018, PANGAEA, https://doi.org/10.1594/PANGAEA.912597, 2020.
Chen, Y., Feng, X., Fu, B., Shi, W., Yin, L., and Lv, Y.: Recent Global
Cropland Water Consumption Constrained by Observations, Water Resour. Res.,
55, 3708–3738, https://doi.org/10.1029/2018WR023573, 2019.
Cheng, S., Huang, J., Ji, F., and Lin, L.: Uncertainties of soil moisture in
historical simulations and future projections, J. Geophys. Res.-Atmos., 122,
2239–2253, https://doi.org/10.1002/2016JD025871, 2017.
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S. B.,
Cosh, M. H., Dunbar, R. S., Dang, L., Pashaian, L., Asanuma, J., Aida, K.,
Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D.,
al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J.,
González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A.,
Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T.,
Prueger, J., Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M.,
Starks, P., Su, Z., Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W.,
Vreugdenhil, M., Walker, J. P., Wu, X., Monerris, A., O'Neill, P. E.,
Entekhabi, D., Njoku, E. G., and Yueh, S.: Validation of SMAP surface soil
moisture products with core validation sites, Remote Sens. Environ., 191,
215–231, https://doi.org/10.1016/j.rse.2017.01.021, 2017.
Dobson, M. C., Ulaby, F. T., Hallikainen, M. T., and El-rayes, M. A.:
Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models,
IEEE T. Geosci. Remote, GE-23, 35–46,
https://doi.org/10.1109/TGRS.1985.289498, 1985.
Dorigo, W., de Jeu, R., Chung, D., Parinussa, R., Liu, Y., Wagner, W., and
Fernández-Prieto, D.: Evaluating global trends (1988–2010) in
harmonized multi-satellite surface soil moisture, Geophys. Res. Lett., 39, L18405,
https://doi.org/10.1029/2012GL052988, 2012.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D.,
Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,
Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C.,
Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and
Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203, 185–215,
https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010.
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone J., 12,
https://doi.org/10.2136/vzj2012.0097, 2013.
Dorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew,
A., Albergel, C., Brocca, L., Chung, D., Parinussa, R. M., and Kidd, R.:
Evaluation of the ESA CCI soil moisture product using ground-based
observations, Remote Sens. Environ., 162, 380–395,
https://doi.org/10.1016/j.rse.2014.07.023, 2015.
Draper, C. S., Reichle, R. H., De Lannoy, G. J. M., and Liu, Q.:
Assimilation of passive and active microwave soil moisture retrievals,
Geophys. Res. Lett., 39, L04401, https://doi.org/10.1029/2011GL050655, 2012.
Du, Y., Ulaby, F. T., and Dobson, M. C.: Sensitivity to soil moisture by
active and passive microwave sensors, IEEE T. Geosci. Remote,
38, 105–114, https://doi.org/10.1109/36.823905, 2000.
Entekhabi, D., Njoku, E., O'neill, P., Kellogg, K. H., Crow, W., Edelstein,
W. N., Entin, J. K., Goodman, S. D., Jackson, T., Johnson, F. M., Kimball,
J., Piepmeier, J., Koster, R. D., Martin, E., McDonald, C. K., Moghaddam,
M., Moran, M. S., Reichle, R., Shi, J. C., Spencer, D., Thurman, S. W.,
Tsang, L., and Zyl, J. V.: The Soil Moisture Active Passive (SMAP) Mission,
Proc. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Etminan, A., Tabatabaeenejad, A., and Moghaddam, M.: Retrieving Root-Zone
Soil Moisture Profile From P-Band Radar via Hybrid Global and Local
Optimization, IEEE T. Geosci. Remote, 58, 1–9,
https://doi.org/10.1109/TGRS.2020.2965569, 2020.
Fan, X., Liu, Y., Gan, G., and Wu, G.: SMAP underestimates soil moisture in
vegetation-disturbed areas primarily as a result of biased surface
temperature data, Remote Sens. Environ., 247, 111914,
https://doi.org/10.1016/j.rse.2020.111914, 2020.
Feng, X., Li, J., Cheng, W., Fu, B., Wang, Y., Lü, Y., and Shao, M. A.:
Evaluation of AMSR-E retrieval by detecting soil moisture decrease following
massive dryland re-vegetation in the Loess Plateau, China, Remote Sens.
Environ., 196, 253–264, https://doi.org/10.1016/j.rse.2017.05.012, 2017.
Fernandez-Moran, R., Al-Yaari, A., Mialon, A., Mahmoodi, A., Al Bitar, A.,
De Lannoy, G., Rodriguez-Fernandez, N., Lopez-Baeza, E., Kerr, Y., and
Wigneron, J.-P.: SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation
Optical Depth Product, Remote Sens., 9, 457, https://doi.org/10.3390/rs9050457,
2017a.
Fernandez-Moran, R., Wigneron, J. P., De Lannoy, G., Lopez-Baeza, E.,
Parrens, M., Mialon, A., Mahmoodi, A., Al-Yaari, A., Bircher, S., Al Bitar,
A., Richaume, P., and Kerr, Y.: A new calibration of the effective
scattering albedo and soil roughness parameters in the SMOS SM retrieval
algorithm, Int. J. Appl. Earth Obs. Geoinf., 62, 27–38,
https://doi.org/10.1016/j.jag.2017.05.013, 2017b.
Fujii, H., Koike, T., and Imaoka, K.: Improvement of the AMSR-E Algorithm
for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage
Dataset Derived from MODIS Data, J. Meteorol. Soc. Japan, 29, 282–292,
https://doi.org/10.11440/rssj.29.282, 2009.
Gaiser, P. W., Germain, K. M. S., Twarog, E. M., Poe, G. A., Purdy, W.,
Richardson, D., Grossman, W., Jones, W. L., Spencer, D., Golba, G.,
Cleveland, J., Choy, L., Bevilacqua, R. M., and Chang, P. S.: The WindSat
spaceborne polarimetric microwave radiometer: sensor description and early
orbit performance, IEEE T. Geosci. Remote, 42, 2347–2361,
https://doi.org/10.1109/TGRS.2004.836867, 2004.
Gao, H., Wood, E. F., Jackson, T. J., Drusch, M., and Bindlish, R.: Using
TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States
from 1998 to 2002, J. Hydrometeorol., 7, 23–38,
https://doi.org/10.1175/JHM473.1, 2006.
Gao, H., Wood, E. F., Drusch, M., and McCabe, M. F.: Copula-Derived
Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil
Moisture into Land Surface Models, J. Hydrometeorol., 8, 413–429,
https://doi.org/10.1175/JHM570.1, 2007.
Gao, L., Sadeghi, M., and Ebtehaj, A.: Microwave retrievals of soil moisture
and vegetation optical depth with improved resolution using a combined
constrained inversion algorithm: Application for SMAP satellite, Remote
Sens. Environ., 239, 111662, https://doi.org/10.1016/j.rse.2020.111662,
2020.
Griend, A. A. V. D. and Wigneron, J.: On the measurement of microwave
vegetation properties: some guidelines for a protocol, IEEE T. Geosci. Remote, 42, 2277–2289, https://doi.org/10.1109/TGRS.2004.832243,
2004.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet,
J. C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi,
M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C.,
Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C.,
Scanlon, T., van der Schalie, R., Wigneron, J. P., and Wagner, W.:
Validation practices for satellite soil moisture retrievals: What are (the)
errors?, Remote Sens. Environ., 244, 111806,
https://doi.org/10.1016/j.rse.2020.111806, 2020.
Hahn, S., Reimer, C., Vreugdenhil, M., Melzer, T., and Wagner, W.: Dynamic
Characterization of the Incidence Angle Dependence of Backscatter Using
Metop ASCAT, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 10,
2348–2359, https://doi.org/10.1109/JSTARS.2016.2628523, 2017.
Hansen, M. and Song, X. P.: Vegetation Continuous Fields (VCF) Yearly Global
0.05 Deg, https://doi.org/10.5067/MEaSUREs/VCF/VCF5KYR.001, 2018.
Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K., and Reichstein, M.: Global spatiotemporal distribution of soil respiration modeled using a global database, Biogeosciences, 12, 4121–4132, https://doi.org/10.5194/bg-12-4121-2015, 2015.
Hermance, J. F., Jacob, R. W., Bradley, B. A., and Mustard, J. F.:
Extracting Phenological Signals From Multiyear AVHRR NDVI Time Series:
Framework for Applying High-Order Annual Splines With Roughness Damping,
IEEE T. Geosci. Remote, 45, 3264–3276,
https://doi.org/10.1109/TGRS.2007.903044, 2007.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019.
Holmes, T. R. H., De Jeu, R. A. M., Owe, M., and Dolman, A. J.: Land surface
temperature from Ka band (37 GHz) passive microwave observations, J.
Geophys. Res.-Atmos., 114, D04113, https://doi.org/10.1029/2008JD010257, 2009.
Howell, D. C., Rogier, M., Yzerbyt, V., and Bestgen, Y.: Statistical methods
in human sciences, Wadsworth, New York, 1998.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L.
G.: Overview of the radiometric and biophysical performance of the MODIS
vegetation indices, Remote Sens. Environ., 83, 195–213,
https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.:
GPM IMERG Final Precipitation L3 1 day 0.1 degree × 0.1 degree V06,
https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Jackson, T. J. and Schmugge, T. J.: Vegetation effects on the microwave
emission of soils, Remote Sens. Environ., 36, 203–212,
https://doi.org/10.1016/0034-4257(91)90057-D, 1991.
Jackson, T. J., Schmugge, T. J., and Wang, J. R.: Passive microwave sensing
of soil moisture under vegetation canopies, Water Resour. Res., 18,
1137–1142, https://doi.org/10.1029/WR018i004p01137, 1982.
Jiao, Q., Li, R., Wang, F., Mu, X., Li, P., and An, C.: Impacts of
Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based
on Remote Sensing Datasets, Remote Sens., 8, 156,
https://doi.org/10.3390/rs8020156, 2016.
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D. N., and Wood, E. F.: Four
decades of microwave satellite soil moisture observations: Part 1. A review
of retrieval algorithms, Adv. Water Resour., 109, 106–120,
https://doi.org/10.1016/j.advwatres.2017.09.006, 2017a.
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D. N., and Wood, E. F.: Four
decades of microwave satellite soil moisture observations: Part 2. Product
validation and inter-satellite comparisons, Adv. Water Resour., 109,
236–252, https://doi.org/10.1016/j.advwatres.2017.09.010, 2017b.
Kawanishi, T., Sezai, T., Ito, Y., Imaoka, K., Takeshima, T., Ishido, Y.,
Shibata, A., Miura, M., Inahata, H., and Spencer, R. W.: The Advanced
Microwave Scanning Radiometer for the Earth Observing System (AMSR-E),
NASDA's contribution to the EOS for global energy and water cycle studies,
IEEE T. Geosci. Remote, 41, 184–194,
https://doi.org/10.1109/TGRS.2002.808331, 2003.
Kerr, Y. H., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., and
Berger, M.: Soil moisture retrieval from space: the Soil Moisture and Ocean
Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735,
https://doi.org/10.1109/36.942551, 2001.
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B.,
Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart,
S., Al Bitar, A., Pellarin, T., Bindlish, R., Jackson, T. J., Rüdiger,
C., Waldteufel, P., Mecklenburg, S., and Wigneron, J. P.: Overview of SMOS
performance in terms of global soil moisture monitoring after six years in
operation, Remote Sens. Environ., 180, 40–63,
https://doi.org/10.1016/j.rse.2016.02.042, 2016.
Kim, H., Parinussa, R., Konings, A. G., Wagner, W., Cosh, M. H., Lakshmi,
V., Zohaib, M., and Choi, M.: Global-scale assessment and combination of
SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products, Remote
Sens. Environ., 204, 260–275, https://doi.org/10.1016/j.rse.2017.10.026,
2018.
Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., and Sharma, A.: A
global comparison of alternate AMSR2 soil moisture products: Why do they
differ?, Remote Sens. Environ., 161, 43–62,
https://doi.org/10.1016/j.rse.2015.02.002, 2015a.
Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., and Sharma, A.: A
framework for combining multiple soil moisture retrievals based on
maximizing temporal correlation, Geophys. Res. Lett., 42, 6662–6670,
https://doi.org/10.1002/2015GL064981, 2015b.
Koike, T., Nakamura, Y., Kaihotsu, I., Davaa, G., Matsuura, N., Tamagawa,
K., and Fujii, H.: Development of an advanced microwave scanning radiometer
(AMSR-E) algorithm for soil moisture and vegetation water content,
Proc. Hydraul. Eng., 48, 217–222,
https://doi.org/10.2208/prohe.48.217, 2004.
Kolassa, J., Reichle, R. H., Liu, Q., Alemohammad, S. H., Gentine, P., Aida,
K., Asanuma, J., Bircher, S., Caldwell, T., Colliander, A., Cosh, M.,
Holifield Collins, C., Jackson, T. J., Martínez-Fernández, J.,
McNairn, H., Pacheco, A., Thibeault, M., and Walker, J. P.: Estimating
surface soil moisture from SMAP observations using a Neural Network
technique, Remote Sens. Environ., 204, 43–59,
https://doi.org/10.1016/j.rse.2017.10.045, 2018.
Konings, A. G., Piles, M., Das, N., and Entekhabi, D.: L-band vegetation
optical depth and effective scattering albedo estimation from SMAP, Remote
Sens. Environ., 198, 460–470, https://doi.org/10.1016/j.rse.2017.06.037,
2017.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of
the Köppen-Geiger climate classification updated, Meteorol. Z., 15,
259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kozak, J. A., Ahuja, L. R., Green, T. R., and Ma, L.: Modelling crop canopy
and residue rainfall interception effects on soil hydrological components
for semi-arid agriculture, Hydrol. Process., 21, 229–241,
https://doi.org/10.1002/hyp.6235, 2010.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H., Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes, Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, 2015.
Lakhankar, T., Ghedira, H., Temimi, M., Azar, E. A., and Khanbilvardi, R.:
Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active
Microwave Remote Sensing Data, Remote Sens., 1, 80–91,
https://doi.org/10.3390/rs1020080, 2009.
Lei, F., Crow, W. T., Shen, H., Su, C.-H., Holmes, T. R. H., Parinussa, R.
M., and Wang, G.: Assessment of the impact of spatial heterogeneity on
microwave satellite soil moisture periodic error, Remote Sens. Environ.,
205, 85–99, https://doi.org/10.1016/j.rse.2017.11.002, 2018.
Leroux, D. J., Kerr, Y. H., Bitar, A. A., Bindlish, R., Jackson, T. J.,
Berthelot, B., and Portet, G.: Comparison Between SMOS, VUA, ASCAT, and
ECMWF Soil Moisture Products Over Four Watersheds in U.S, IEEE T. Geosci. Remote, 52, 1562–1571,
https://doi.org/10.1109/TGRS.2013.2252468, 2014a.
Leroux, D. J., Kerr, Y. H., Wood, E. F., Sahoo, A. K., Bindlish, R., and
Jackson, T. J.: An Approach to Constructing a Homogeneous Time Series of
Soil Moisture Using SMOS, IEEE T. Geosci. Remote, 52, 393–405,
https://doi.org/10.1109/TGRS.2013.2240691, 2014b.
Lievens, H., Martens, B., Verhoest, N. E. C., Hahn, S., Reichle, R. H., and
Miralles, D. G.: Assimilation of global radar backscatter and radiometer
brightness temperature observations to improve soil moisture and land
evaporation estimates, Remote Sens. Environ., 189, 194–210,
https://doi.org/10.1016/j.rse.2016.11.022, 2017.
Liu, Y. Y., de Jeu, R. A. M., McCabe, M. F., Evans, J. P., and van Dijk, A.
I. J. M.: Global long-term passive microwave satellite-based retrievals of
vegetation optical depth, Geophys. Res. Lett., 38, L18402,
https://doi.org/10.1029/2011GL048684, 2011a.
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, https://doi.org/10.5194/hess-15-425-2011, 2011b.
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W.,
McCabe, M. F., Evans, J. P., and van Dijk, A. I. J. M.: Trend-preserving
blending of passive and active microwave soil moisture retrievals, Remote
Sens. Environ., 123, 280–297, https://doi.org/10.1016/j.rse.2012.03.014,
2012.
Lorenz, C., Montzka, C., Jagdhuber, T., Laux, P., and Kunstmann, H.:
Long-Term and High-Resolution Global Time Series of Brightness Temperature
from Copula-Based Fusion of SMAP Enhanced and SMOS Data, Remote Sens., 10, 1842,
https://doi.org/10.3390/rs10111842, 2018.
Lu, Z., Chai, L., Ye, Q., and Zhang, T.: Reconstruction of time-series soil
moisture from AMSR2 and SMOS data by using recurrent nonlinear
autoregressive neural networks, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, 26–31 July 2015, 980–983,
https://doi.org/10.1109/IGARSS.2015.7325932, 2015.
Ma, H., Zeng, J., Chen, N., Zhang, X., Cosh, M. H., and Wang, W.: Satellite
surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive
assessment using global ground-based observations, Remote Sens. Environ.,
231, 111215, https://doi.org/10.1016/j.rse.2019.111215, 2019.
Martens, B., Miralles, D., Lievens, H., Fernández-Prieto, D., and
Verhoest, N. E. C.: Improving terrestrial evaporation estimates over
continental Australia through assimilation of SMOS soil moisture, Int. J.
Appl. Earth Obs. Geoinf., 48, 146–162,
https://doi.org/10.1016/j.jag.2015.09.012, 2016.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martínez-Fernández, J., González-Zamora, A., Sánchez, N.,
Gumuzzio, A., and Herrero-Jiménez, C. M.: Satellite soil moisture for
agricultural drought monitoring: Assessment of the SMOS derived Soil Water
Deficit Index, Remote Sens. Environ., 177, 277–286,
https://doi.org/10.1016/j.rse.2016.02.064, 2016.
McColl, K. A., Alemohammad, S. H., Akbar, R., Konings, A. G., Yueh, S., and
Entekhabi, D.: The global distribution and dynamics of surface soil
moisture, Nat. Geosci., 10, 100, https://doi.org/10.1038/ngeo2868, 2017.
Méndez-Barroso, L. A., Vivoni, E. R., Watts, C. J., and Rodríguez,
J. C.: Seasonal and interannual relations between precipitation, surface
soil moisture and vegetation dynamics in the North American monsoon region,
J. Hydrol., 377, 59–70, https://doi.org/10.1016/j.jhydrol.2009.08.009, 2009.
Merriam, R. A.: A note on the interception loss equation, Int. J. Digit.
Earth, 65, 3850–3851, https://doi.org/10.1029/jz065i011p03850, 1960.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Mladenova, I. E., Jackson, T. J., Njoku, E., Bindlish, R., Chan, S., Cosh,
M. H., Holmes, T. R. H., de Jeu, R. A. M., Jones, L., Kimball, J., Paloscia,
S., and Santi, E.: Remote monitoring of soil moisture using passive
microwave-based techniques – Theoretical basis and overview of selected
algorithms for AMSR-E, Remote Sens. Environ., 144, 197–213,
https://doi.org/10.1016/j.rse.2014.01.013, 2014.
Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., and Jackson, T. J.:
A model for microwave emission from vegetation-covered fields, J. Geophys.
Res.-Oceans, 87, 11229–11237, https://doi.org/10.1029/JC087iC13p11229, 1982.
Naithani, K. J., Baldwin, D. C., Gaines, K. P., Lin, H., and Eissenstat, D.
M.: Spatial Distribution of Tree Species Governs the Spatio-Temporal
Interaction of Leaf Area Index and Soil Moisture across a Forested
Landscape, PLoS One, 8, e58704,
https://doi.org/10.1371/journal.pone.0058704, 2013.
Neill, P. E. O., Podest, E., and Njoku, E. G.: Utilization of ancillary data
sets for SMAP algorithm development and product generation, 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, 24–29 July 2011, 2436–2439, https://doi.org/10.1109/IGARSS.2011.6049703, 2011.
Njoku, E. G. and Chan, T. K.: Vegetation and surface roughness effects on
AMSR-E land observations, Remote Sens. Environ., 100, 190–199,
https://doi.org/10.1016/j.rse.2005.10.017, 2006.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.:
Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41,
215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
Njoku, E. G., Ashcroft, P., Chan, T. K., and Li, L.: Global survey and
statistics of radio-frequency interference in AMSR-E land observations, IEEE T. Geosci. Remote, 43, 938–947,
https://doi.org/10.1109/TGRS.2004.837507, 2005.
Oliva, R., Daganzo, E., Kerr, Y. H., Mecklenburg, S., Nieto, S., Richaume,
P., and Gruhier, C.: SMOS Radio Frequency Interference Scenario: Status and
Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive
Band, IEEE T. Geosci. Remote, 50, 1427–1439,
https://doi.org/10.1109/TGRS.2012.2182775, 2012.
Owe, M., Jeu, R. D., and Walker, J.: A methodology for surface soil moisture
and vegetation optical depth retrieval using the microwave polarization
difference index, IEEE T. Geosci. Remote, 39, 1643–1654,
https://doi.org/10.1109/36.942542, 2001.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys. Res.-Earth
Surf., 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Panciera, R., Walker, J. P., Kalma, J. D., Kim, E. J., Saleh, K., and
Wigneron, J.-P.: Evaluation of the SMOS L-MEB passive microwave soil
moisture retrieval algorithm, Remote Sens. Environ., 113, 435–444,
https://doi.org/10.1016/j.rse.2008.10.010, 2009.
Parinussa, R. M., Holmes, T. R. H., Yilmaz, M. T., and Crow, W. T.: The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations, Hydrol. Earth Syst. Sci., 15, 3135–3151, https://doi.org/10.5194/hess-15-3135-2011, 2011.
Parinussa, R. M., Holmes, T. R. H., and Jeu, R. A. M. D.: Soil Moisture
Retrievals From the WindSat Spaceborne Polarimetric Microwave Radiometer,
IEEE T. Geosci. Remote, 50, 2683–2694,
https://doi.org/10.1109/TGRS.2011.2174643, 2012.
Parinussa, R. M., Holmes, T. R. H., Wanders, N., Dorigo, W. A., and de Jeu,
R. A. M.: A Preliminary Study toward Consistent Soil Moisture from AMSR2, J.
Hydrometeorol., 16, 932–947, https://doi.org/10.1175/JHM-D-13-0200.1, 2014.
Piles, M., Schalie, R. V. D., Gruber, A., Muñoz-Marí, J.,
Camps-Valls, G., Mateo-Sanchis, A., Dorigo, W., and Jeu, R. D.: Global
Estimation of Soil Moisture Persistence with L and C-Band Microwave Sensors, IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia,
22–27 July 2018, 8259–8262,
https://doi.org/10.1109/IGARSS.2018.8518161, 2018.
Pratola, C., Barrett, B., Gruber, A., and Dwyer, E.: Quality Assessment of
the CCI ECV Soil Moisture Product Using ENVISAT ASAR Wide Swath Data over
Spain, Ireland and Finland, Remote Sens., 7, 15388–15423, https://doi.org/10.3390/rs71115388, 2015.
Qiu, J., Gao, Q., Wang, S., and Su, Z.: Comparison of temporal trends from
multiple soil moisture data sets and precipitation: The implication of
irrigation on regional soil moisture trend, Int. J. Appl. Earth Obs.
Geoinf., 48, 17–27, https://doi.org/10.1016/j.jag.2015.11.012, 2016.
Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z.,
Jin, R., Li, X., Guo, Z., and Zheng, J.: Rebuilding a Microwave Soil
Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness
Temperature and SMAP over the Qinghai–Tibet Plateau, China, Remote Sens.,
11, 683, https://doi.org/10.3390/rs11060683, 2019.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Rodriguez-Fernandez, N., Aires, F., Richaume, P., Kerr, Y. H., Prigent, C.,
Kolassa, J., Cabot, F., Jiménez, C., Mahmoodi, A., and Drusch, M.: Soil
Moisture Retrieval Using Neural Networks: Application to SMOS, IEEE T. Geosci. Remote, 53, 5991–6007,
https://doi.org/10.1109/TGRS.2015.2430845, 2015.
Rodríguez-Fernández, J. N., Kerr, H. Y., Van der Schalie, R.,
Al-Yaari, A., Wigneron, J.-P., De Jeu, R., Richaume, P., Dutra, E., Mialon,
A., and Drusch, M.: Long Term Global Surface Soil Moisture Fields Using an
SMOS-Trained Neural Network Applied to AMSR-E Data, Remote Sens., 8, 959,
https://doi.org/10.3390/rs8110959, 2016.
Samaniego, L., Thober, S., Kumar, R., Wanders, N., Rakovec, O., Pan, M.,
Zink, M., Sheffield, J., Wood, E. F., and Marx, A.: Anthropogenic warming
exacerbates European soil moisture droughts, Nat. Clim. Chang., 8, 421–426,
https://doi.org/10.1038/s41558-018-0138-5, 2018.
Schroeder, R., McDonald, C. K., Chapman, D. B., Jensen, K., Podest, E.,
Tessler, D. Z., Bohn, J. T., and Zimmermann, R.: Development and Evaluation
of a Multi-Year Fractional Surface Water Data Set Derived from
Active/Passive Microwave Remote Sensing Data, Remote Sens., 7, 16688–16732,
https://doi.org/10.3390/rs71215843, 2015.
Shi, J., Jackson, T., Tao, J., Du, J., Bindlish, R., Lu, L., and Chen, K.
S.: Microwave vegetation indices for short vegetation covers from satellite
passive microwave sensor AMSR-E, Remote Sens. Environ., 112, 4285–4300,
https://doi.org/10.1016/j.rse.2008.07.015, 2008.
Stillman, S. and Zeng, X.: Evaluation of SMAP Soil Moisture Relative to Five
Other Satellite Products Using the Climate Reference Network Measurements
Over USA, IEEE T. Geosci. Remote, 56, 6296–6305,
https://doi.org/10.1109/TGRS.2018.2835316, 2018.
Stinchcombe, M. and White, H.: Universal approximation using feedforward networks
with non-sigmoid hidden layer activation functions,
International 1989 Joint Conference on Neural Networks, Washington, D.C., USA, 613–617, https://doi.org/10.1109/IJCNN.1989.118640, 1989.
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C.,
Seneviratne, S. I., and Peñuelas, J.: Drought impacts on terrestrial
primary production underestimated by satellite monitoring, Nat. Geosci., 12,
264–270, https://doi.org/10.1038/s41561-019-0318-6, 2019.
Ulaby, F. T., Batlivala, P. P., and Dobson, M. C.: Microwave Backscatter
Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part
I-Bare Soil, IEEE Trans. Geosci. Electronics, 16, 286–295,
https://doi.org/10.1109/TGE.1978.294586, 1978.
Van der Schalie, R., de Jeu, R. A. M., Kerr, Y. H., Wigneron, J. P.,
Rodríguez-Fernández, N. J., Al-Yaari, A., Parinussa, R. M.,
Mecklenburg, S., and Drusch, M.: The merging of radiative transfer based
surface soil moisture data from SMOS and AMSR-E, Remote Sens. Environ., 189,
180–193, https://doi.org/10.1016/j.rse.2016.11.026, 2017.
Van der Schalie, R., De Jeu, R., Parinussa, R.,
Rodríguez-Fernández, N., Kerr, Y., Al-Yaari, A., Wigneron, J.-P.,
and Drusch, M.: The Effect of Three Different Data Fusion Approaches on the
Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors,
Remote Sens., 10, 107, https://doi.org/10.3390/rs10010107, 2018.
Verger, A., Baret, F., and Weiss, M.: Near Real-Time Vegetation Monitoring
at Global Scale, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 7,
3473–3481, https://doi.org/10.1109/JSTARS.2014.2328632, 2014.
Verhoest, N. E. C., Berg, M. J. V. D., Martens, B., Lievens, H., Wood, E.
F., Pan, M., Kerr, Y. H., Bitar, A. A., Tomer, S. K., Drusch, M., Vernieuwe,
H., Baets, B. D., Walker, J. P., Dumedah, G., and Pauwels, V. R. N.:
Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With
Implicit Bias Correction, IEEE T. Geosci. Remote, 53, 3507–3521,
https://doi.org/10.1109/TGRS.2014.2378913, 2015.
Vreugdenhil, M., Dorigo, W. A., Wagner, W., Jeu, R. A. M. D., Hahn, S., and
Marle, M. J. E. V.: Analyzing the Vegetation Parameterization in the TU-Wien
ASCAT Soil Moisture Retrieval, IEEE T. Geosci. Remote, 54,
3513–3531, https://doi.org/10.1109/TGRS.2016.2519842, 2016.
Wagner, W., Lemoine, G., and Rott, H.: A Method for Estimating Soil Moisture
from ERS Scatterometer and Soil Data, Remote Sens. Environ., 70, 191–207,
https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
Wang, Y., Leng, P., Peng, J., Marzahn, P., and Ludwig, R.: Global
assessments of two blended microwave soil moisture products CCI and SMOPS
with in-situ measurements and reanalysis data, Int. J. Appl. Earth Obs.
Geoinf., 94, 102234, https://doi.org/10.1016/j.jag.2020.102234, 2021.
Wigneron, J., Calvet, J., Rosnay, P. d., Kerr, Y., Waldteufel, P., Saleh,
K., Escorihuela, M. J., and Kruszewski, A.: Soil moisture retrievals from
biangular L-band passive microwave observations, IEEE Geosci. Remote Sens.
Lett., 1, 277–281, https://doi.org/10.1109/LGRS.2004.834594, 2004.
Wigneron, J. P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M. J.,
Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J. C.,
Grant, J. P., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin,
T., and Schwank, M.: L-band Microwave Emission of the Biosphere (L-MEB)
Model: Description and calibration against experimental data sets over crop
fields, Remote Sens. Environ., 107, 639–655,
https://doi.org/10.1016/j.rse.2006.10.014, 2007.
Xiao, Z., Liang, S., Wang, J., Chen, P., Yin, X., Zhang, L., and Song, J.:
Use of General Regression Neural Networks for Generating the GLASS Leaf Area
Index Product From Time-Series MODIS Surface Reflectance, IEEE T. Geosci. Remote, 52, 209–223,
https://doi.org/10.1109/TGRS.2013.2237780, 2014.
Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., and Song, J.:
Long-Time-Series Global Land Surface Satellite Leaf Area Index Product
Derived From MODIS and AVHRR Surface Reflectance, IEEE T. Geosci. Remote, 54, 5301–5318, https://doi.org/10.1109/TGRS.2016.2560522, 2016.
Yang, H., Weng, F., Lv, L., Lu, N., Liu, G., Bai, M., Qian, Q., He, J., and
Xu, H.: The FengYun-3 Microwave Radiation Imager On-Orbit Verification, IEEE T. Geosci. Remote, 49, 4552–4560,
https://doi.org/10.1109/TGRS.2011.2148200, 2011.
Yang, J., Zhang, P., Lu, N., Yang, Z., Shi, J., and Dong, C.: Improvements
on global meteorological observations from the current Fengyun 3 satellites
and beyond, Int. J. Digit. Earth, 5, 251–265,
https://doi.org/10.1080/17538947.2012.658666, 2012.
Yao, P., Shi, J., Zhao, T., Lu, H., and Al-Yaari, A.: Rebuilding Long Time
Series Global Soil Moisture Products Using the Neural Network Adopting the
Microwave Vegetation Index, Remote Sens., 9, 35,
https://doi.org/10.3390/rs9010035, 2017.
Yao, P., Lu, H., Yue, S., Yang, F., Lyu, H., Yang, K., McColl, K. A.,
Gianotti, D., and ENTekhabi, D.: Estimating Surface Soil Moisture from AMSR2
Tb with Artificial Neural Network Method and SMAP Products, IGARSS 2019 -
2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama,
Japan, 28 July–2 August 2019, 6998–7001, https://doi.org/10.1109/IGARSS.2019.8898152, 2019.
Ye, N., Walker, J. P., Guerschman, J., Ryu, D., and Gurney, R. J.: Standing
water effect on soil moisture retrieval from L-band passive microwave
observations, Remote Sens. Environ., 169, 232–242,
https://doi.org/10.1016/j.rse.2015.08.013, 2015.
Ye, N., Walker, J. P., Yeo, I., Jackson, T. J., Kerr, Y., Kim, E., McGrath,
A., PopStefanija, I., Goodberlet, M., and Hills, J.: Toward P-Band Passive
Microwave Sensing of Soil Moisture, IEEE Geosci. Remote Sens. Lett., PP, 1–5,
https://doi.org/10.1109/LGRS.2020.2976204, 2020.
Yilmaz, M. T., Hunt, E. R., and Jackson, T. J.: Remote sensing of vegetation
water content from equivalent water thickness using satellite imagery,
Remote Sens. Environ., 112, 2514–2522,
https://doi.org/10.1016/j.rse.2007.11.014, 2008.
Yin, J., Zhan, X., Liu, J., and Schull, M.: An Intercomparison of Noah Model
Skills With Benefits of Assimilating SMOPS Blended and Individual Soil
Moisture Retrievals, Water Resour. Res., 55, 2572–2592,
https://doi.org/10.1029/2018WR024326, 2019.
Zhang, R., Kim, S., and Sharma, A.: A comprehensive validation of the SMAP
Enhanced Level-3 Soil Moisture product using ground measurements over varied
climates and landscapes, Remote Sens. Environ., 223, 82–94,
https://doi.org/10.1016/j.rse.2019.01.015, 2019.
Zhao, Y., Peth, S., Wang, X. Y., Lin, H., and Horn, R.: Controls of surface
soil moisture spatial patterns and their temporal stability in a semi-arid
steppe, Hydrol. Process., 24, 2507–2519, https://doi.org/10.1002/hyp.7665,
2010.
Short summary
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By calibrating and combining 11 different products derived from satellite observation, we developed a new global surface soil moisture dataset spanning from 2003 to 2018 with high accuracy. Using this new dataset, not only can the global long-term trends be derived, but also the seasonal variation and spatial distribution of surface soil moisture at different latitudes can be better studied.
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By...
Altmetrics
Final-revised paper
Preprint