Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4473-2022
© Author(s) 2022. 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-14-4473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Oct 2022
Data description paper |
| 06 Oct 2022
| 06 Oct 2022
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
Qiang Zhang
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, China
School of Geodesy and Geomatics, Wuhan University, China
Taoyong Jin
CORRESPONDING AUTHOR
School of Geodesy and Geomatics, Wuhan University, China
Meiping Song
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, China
Fujun Sun
CASIC Research Institute of Intelligent Decision Engineering, Beijing, China
Related authors
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.
Lianjun Yang, Taoyong Jin, and Weiping Jiang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-350, https://doi.org/10.5194/essd-2024-350, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
Storm surges (SSs) cause massive loss of life and property in coastal areas each year. High spatial resolution and long-term SS records are the basis for assessing such events. However, tide gauges can provide limited SS information due to sparse and uneven distributions. Based on artificial intelligence technology and tide gauges, a high spatial coverage SS dataset was generated for period from 1940 to 2020, which can provide possible alternative support for deepening our understanding of SSs.
Guodong Chen, Weiping Jiang, Zhijie Zhang, Taoyong Jin, and Dawei Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-3030, https://doi.org/10.5194/egusphere-2023-3030, 2024
Preprint archived
Short summary
Short summary
This paper attempts to determine Arctic mean sea surface and sea level change by combining ICESat-2 and CryoSat-2 data. Our results show that the SSH flag identified in ATL07 may be too strict, resulting in a small number of lead identifications. Combining the two missions can obtain sea surface height with higher accuracy and coverage in the Arctic. The results are helpful for the study of sea level, sea ice, and the accuracy of ICESat-2 and CryoSat-2 data in the Arctic.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
Short summary
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Jiasheng Shi, Taoyong Jin, Mao Zhou, Xiangcheng Wan, and Weiping Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1018, https://doi.org/10.5194/egusphere-2022-1018, 2022
Preprint withdrawn
Short summary
Short summary
SWOT has significant potential for detecting mesoscale eddies, but the detecting method, which is used for nadir altimeters, may be not optimal. We propose to improve the method based on the spatial and temporal features of SWOT, to reduce the long-wavelength errors and enhance the high spatial features. The accuracy of gridded results are improved especially when the number of observations is limited. The reconstruction and detected temporal scales of mesoscale eddy variations is also enhanced.
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.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Siyu Tan, and Liangpei Zhang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1004, https://doi.org/10.5194/acp-2020-1004, 2020
Revised manuscript not accepted
Short summary
Short summary
Estimating ambient PM2.5 and PM10 considering their precursors and chemical compositions instead of AOD products; Both remote sensing (Sentinel-5P) and assimilated data (GEOS-FP) are adopted; Sample-based Cross-Validation R2s and RMSEs are 0.93 (0.9) and 8.982 (17.604) μg/m3 for PM2.5 (PM10), respectively; Achieving better performance compared to the baseline (AOD-based) in different cases (e.g., overall and seasonal).
C. Zhou, J. Li, H. Shen, and Q. Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 101–107, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, 2020
Tongwen Li, Chengyue Zhang, Huanfeng Shen, Qiangqiang Yuan, and Liangpei Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 143–147, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, 2018
Zhiwei Li, Huanfeng Shen, Yancong Wei, Qing Cheng, and Qiangqiang Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 149–152, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, 2018
X. Meng, H. Shen, Q. Yuan, H. Li, and L. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 831–835, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, 2017
Related subject area
Domain: ESSD – Land | Subject: Pedology
An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022
Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
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
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
Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Şerban, and Tao Zhan
Earth Syst. Sci. Data, 16, 5009–5026, https://doi.org/10.5194/essd-16-5009-2024, https://doi.org/10.5194/essd-16-5009-2024, 2024
Short summary
Short summary
In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous ground hydrothermal regime and soil nutrient content observation system has been gradually established in Northeast China. The integrated dataset includes soil moisture content, soil organic carbon, total nitrogen, total phosphorus, total potassium, ground temperatures at depths of 0–20 m, and active layer thickness from 2016 to 2022.
Niels H. Batjes, Luis Calisto, and Luis M. de Sousa
Earth Syst. Sci. Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, https://doi.org/10.5194/essd-16-4735-2024, 2024
Short summary
Short summary
Soils are an important provider of ecosystem services. This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at a broad scale. The underpinning soil profiles were shared by a wide range of data providers. Special attention was paid to the standardisation of soil property definitions, analytical method descriptions and property values. We present three measures to assess "fitness for intended use" of the standardised data.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
Short summary
Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi
Earth Syst. Sci. Data, 16, 2367–2383, https://doi.org/10.5194/essd-16-2367-2024, https://doi.org/10.5194/essd-16-2367-2024, 2024
Short summary
Short summary
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
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.
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.
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.
Cited articles
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017.
Berg, P., Almén, F., and Bozhinova, D.: HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time, Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, 2021.
Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., Baatz, R., Boorman, D., Duygu, M. B., Eguibar-Galán, M. A., Fersch, B., Franke, T., Geris, J., González Sanchis, M., Kerr, Y., Korf, T., Mengistu, Z., Mialon, A., Nasta, P., Nitychoruk, J., Pisinaras, V., Rasche, D., Rosolem, R., Said, H., Schattan, P., Zreda, M., Achleitner, S., Albentosa-Hernández, E., Akyürek, Z., Blume, T., del Campo, A., Canone, D., Dimitrova-Petrova, K., Evans, J. G., Ferraris, S., Frances, F., Gisolo, D., Güntner, A., Herrmann, F., Iwema, J., Jensen, K. H., Kunstmann, H., Lidón, A., Looms, M. C., Oswald, S., Panagopoulos, A., Patil, A., Power, D., Rebmann, C., Romano, N., Scheiffele, L., Seneviratne, S., Weltin, G., and Vereecken, H.: COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors, Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, 2022.
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data,
J. Geophys. Res.-Atmos., 119, 5128–5141, https://doi.org/10.1002/2014JD021489, 2014a.
Brocca, L., Zucco, G., Mittelbach, H., Moramarco, T., and Seneviratne, S. I.: Absolute versus temporal anomaly and percent of saturation soil moisture spatial variability for six networks worldwide, Water Resour. Res., 50, 5560–5576, https://doi.org/10.1002/2014WR015684, 2014b.
Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., and Fernández-Prieto, D.: How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products, Int. J. Appl. Earth Obs., 73C, 752–766, https://doi.org/10.1016/j.jag.2018.08.023, 2018.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations, Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019.
Chen, Y., Feng, X., and Fu, B.: An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018, Earth Syst. Sci. Data, 13, 1–31, https://doi.org/10.5194/essd-13-1-2021, 2021.
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, 1–7, https://doi.org/10.1029/2012GL052988, 2012.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
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, 1–21, https://doi.org/10.2136/vzj2012.0097, 2013.
Draper, C., Walker, J., Steinle, P., De Jeu, R., and Holmes, T. R: An evaluation of AMSR–E derived soil moisture over Australia,
Remote Sens. Environ., 113, 703–710, https://doi.org/10.1016/j.rse.2008.11.011, 2009.
Enenkel, M., Reimer, C., Dorigo, W., Wagner, W., Pfeil, I., Parinussa, R., and De Jeu, R.: Combining satellite observations to develop a global soil moisture product for near-real-time applications, Hydrol. Earth Syst. Sci., 20, 4191–4208, https://doi.org/10.5194/hess-20-4191-2016, 2016.
Fan, Y. and van den Dool, H.: Climate prediction center global monthly soil moisture data set at 0.5° resolution for 1948 to present, J. Geophys. Res., 109, D10102, https://doi.org/10.1029/2003JD004345, 2004.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet, J. C., 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.
Guevara, M., Taufer, M., and Vargas, R.: Gap-free global annual soil moisture: 15 km grids for 1991–2018, Earth Syst. Sci. Data, 13, 1711–1735, https://doi.org/10.5194/essd-13-1711-2021, 2021.
Long, D., Shen, Y., and Sun, A.: Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data, Remote Sens. Environ., 155, 145–160, https://doi.org/10.1016/j.rse.2014.08.006, 2014.
Long, D., Bai, L., and Yan, L.: Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution, Remote Sens. Environ., 233, 111364, https://doi.org/10.1016/j.rse.2019.111364, 2019.
Long, D., Yang W. T., Scanlon, B. R., Zhao, J. S., Liu, D. G., Burek, P., Pan, Y., You, L. Z., and Wada, Y.: South-to-North water diversion stabilizing Beijing’s groundwater levels, Nat. Commun., 11, 1863‒-1880, https://doi.org/10.1038/s41467-020-17428-6, 2020.
Massari, C., Brocca, L., Pellarin, T., Abramowitz, G., Filippucci, P., Ciabatta, L., Maggioni, V., Kerr, Y., and Fernandez Prieto, D.: A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products, Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, 2020.
McColl, K. A., Alemohammad, S. H., and Akbar, R.: The global distribution and dynamics of surface soil moisture, Nat. Geosci., 10, 100–104, https://doi.org/10.1038/NGEO2868, 2017.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., and Guo, Z.: A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, 2021.
Nepal, S., Pradhananga, S., Shrestha, N. K., Kralisch, S., Shrestha, J. P., and Fink, M.: Space–time variability in soil moisture droughts in the Himalayan region, Hydrol. Earth Syst. Sci., 25, 1761–1783, https://doi.org/10.5194/hess-25-1761-2021, 2021.
Njoku, E., Jackson, T., Lakshmi, V., Chan, T., and Nghiem, S.: Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41, 215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
Pellarin, T., Tran, T., Cohard, J.-M., Galle, S., Laurent, J.-P., de Rosnay, P., and Vischel, T.: Soil moisture mapping over West Africa with a 30-min temporal resolution using AMSR-E observations and a satellite-based rainfall product, Hydrol. Earth Syst. Sci., 13, 1887–1896, https://doi.org/10.5194/hess-13-1887-2009, 2009.
Rebel, K. T., de Jeu, R. A. M., Ciais, P., Viovy, N., Piao, S. L., Kiely, G., and Dolman, A. J.: A global analysis of soil moisture derived from satellite observations and a land surface model, Hydrol. Earth Syst. Sci., 16, 833–847, https://doi.org/10.5194/hess-16-833-2012, 2012.
Schaffitel, A., Schuetz, T., and Weiler, M.: A distributed soil moisture, temperature and infiltrometer dataset for permeable pavements and green spaces, Earth Syst. Sci. Data, 12, 501–517, https://doi.org/10.5194/essd-12-501-2020, 2020.
Seneviratne, S., Corti, T., Davin, E., Hirschi, M., Jaeger, E., Lehner, I., and Teuling, A.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Shi, J., Chen, K. S., Li, Q., Jackson, T. J., O'Neill, P. E., and Tsang, L.: A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer, IEEE T. Geosci. Remote, 40, 2674–2686, https://doi.org/10.1109/TGRS.2002.807003, 2002.
Shi, J., Jiang, L., Zhang, L., Chen, K. S., Wigneron, J. P., Chanzy, A., and Jackson, T. J.: Physically based estimation of bare-surface soil moisture with the passive radiometers, IEEE T. Geosci. Remote, 44, 3145–3153, https://doi.org/10.1109/TGRS.2006.876706, 2006.
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.
Škrk, N., Serrano-Notivoli, R., Čufar, K., Merela, M., Črepinšek, Z., Kajfež Bogataj, L., and de Luis, M.: SLOCLIM: a high-resolution daily gridded precipitation and temperature dataset for Slovenia, Earth Syst. Sci. Data, 13, 3577–3592, https://doi.org/10.5194/essd-13-3577-2021, 2021.
Sun, L. and Fu, Y.: A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner, Earth Syst. Sci. Data, 13, 2293–2306, https://doi.org/10.5194/essd-13-2293-2021, 2021.
Todd-Brown, K. E. O., Abramoff, R. Z., Beem-Miller, J., Blair, H. K., Earl, S., Frederick, K. J., Fuka, D. R., Guevara Santamaria, M., Harden, J. W., Heckman, K., Heran, L. J., Holmquist, J. R., Hoyt, A. M., Klinges, D. H., LeBauer, D. S., Malhotra, A., McClelland, S. C., Nave, L. E., Rocci, K. S., Schaeffer, S. M., Stoner, S., van Gestel, N., von Fromm, S. F., and Younger, M. L.: Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential, Biogeosciences, 19, 3505–3522, https://doi.org/10.5194/bg-19-3505-2022, 2022.
Walker, J., Willgoose, G., and Kalma, J.: In situ measurement of soil moisture: a comparison of techniques, J. Hydrol., 293, 85–99, https://doi.org/10.1016/j.jhydrol.2004.01.008, 2004.
Wang, M., Wigneron, J. P., Sun, R., Fan, L., Frappart, F., Tao, S., and Ciais, P.: A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations, Int. J. Appl. Earth Obs., 105, 102609, https://doi.org/10.1016/j.jag.2021.102609, 2021.
Wang, Q., Ding, X., Tong, X., and Atkinson, P. M.: Spatio-temporal spectral unmixing of time-series images, Remote Sens. Environ., 259, 112407, https://doi.org/10.1016/j.rse.2021.112407, 2021.
Wang, Q., Wang, L., Zhu, X., Ge, Y., Tong, X., and Atkinson, P. M.: Remote sensing image gap filling based on spatial-spectral random forest,
Science of Remote Sensing, 5, 100048, https://doi.org/10.1016/j.srs.2022.100048, 2022.
Wang, Y., Mao, J., Jin, M., Hoffman, F. M., Shi, X., Wullschleger, S. D., and Dai, Y.: Development of observation-based global multilayer soil moisture products for 1970 to 2016, Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, 2021.
Wigneron, J. P., Olioso, A., Calvet, J. C., and Bertuzzi, P.: Estimating root zone soil moisture from surface soil moisture data and soil‐vegetation‐atmosphere transfer modeling, Water Resour. Res., 35, 3735–3745, https://doi.org/10.1029/1999WR900258, 1999.
Wigneron, J. P., Calvet, J. C., Pellarin, T., Van de Griend, A. A., Berger, M., and Ferrazzoli, P.: Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans, Remote Sens. Environ., 85, 489–506, https://doi.org/10.1016/S0034-4257(03)00051-8, 2013.
Xiao, Y., Su, X., Yuan, Q., Liu, D., Shen, H., and Zhang, L.: Satellite video super-resolution via multiscale deformable convolution alignment and temporal grouping projection, IEEE T. Geosci. Remote, 60, 1–19, https://doi.org/10.1109/TGRS.2021.3107352, 2022a.
Xiao, Y., Yuan, Q., He, J., Zhang, Q., Sun, J., Su, X., Wu, J., and Zhang, L.: Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer, Int. J. Appl. Earth Obs., 108, 102731, https://doi.org/10.1016/j.jag.2022.102731, 2022b.
Yuan, Q., Zhang, Q., Li, J., Shen, H., and Zhang, L.: Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network, IEEE T. Geosci. Remote, 57, 1205–1218, https://doi.org/10.1109/TGRS.2018.2865197, 2019.
Zeng, J., Li, Z., Chen, Q., Bi, H. Y., Qiu, J. X., and Zou, P. F.: Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations, Remote Sens. Environ., 163, 91–110, https://doi.org/10.1016/j.rse.2015.03.008, 2015a.
Zeng, J., Li, Z., Chen, Q., and Bi, H.: Method for soil moisture and surface temperature estimation in the Tibetan Plateau using spaceborne radiometer observations, IEEE Geosci. Remote S., 12, 97–101, https://doi.org/10.1109/LGRS.2014.2326890, 2015b.
Zeng, J., Chen, K., Cui, C., and Bai, X.: A physically based soil moisture index from passive microwave brightness temperatures for soil moisture variation monitoring, IEEE T. Geosci. Remote, 58, 2782–2795, https://doi.org/10.1109/TGRS.2019.2955542, 2020.
Zhang, P., Zheng, D., van der Velde, R., Wen, J., Zeng, Y., Wang, X., Wang, Z., Chen, J., and Su, Z.: Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009–2019) surface soil moisture dataset, Earth Syst. Sci. Data, 13, 3075–3102, https://doi.org/10.5194/essd-13-3075-2021, 2021.
Zhang, Q., Yuan, Q., Zeng, C., Li, X., and Wei, Y.: Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network, IEEE T. Geosci. Remote, 56, 4274–4288, https://doi.org/10.1109/TGRS.2018.2810208, 2018a.
Zhang, Q., Yuan, Q., Li, J., Yang, Z., and Ma, X.: Learning a dilated residual network for SAR image despeckling, Remote Sens., 196, 1–18, https://doi.org/10.3390/rs10020196, 2018b.
Zhang, Q., Yuan, Q., Li, J., Li, Z., Shen, H., and Zhang, L.: Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning, ISPRS J. Photogramm., 162, 148–160, https://doi.org/10.1016/j.isprsjprs.2020.02.008, 2020.
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021a.
Zhang, Q., Yuan, Q., Li, Z., Sun, F., and Zhang, L.: Combined deep prior with low-rank tensor SVD for thick cloud removal in multitemporal images, ISPRS J. Photogramm., 176, 125–137, https://doi.org/10.1016/j.isprsjprs.2020.04.010, 2021b.
Zhang, Q., Yuan, Q., and Jin, T.: SGD-SM 2.0, Zenodo [data set], https://doi.org/10.5281/zenodo.6041561, 2022.
Zhan, W., Pan, M., Wanders, N., and Wood, E. F.: Correction of real-time satellite precipitation with satellite soil moisture observations, Hydrol. Earth Syst. Sci., 19, 4275–4291, https://doi.org/10.5194/hess-19-4275-2015, 2015.
Zhang, Q., Yuan, Q., Li, J., Liu, X., Shen, H., and Zhang, L.: Hybrid noise removal in hyperspectral imagery with spatial-spectral gradient network, IEEE T. Geosci. Remote, 57, 7317–7329, https://doi.org/10.1109/TGRS.2019.2912909, 2019.
Zhao, T., Shi, J., Entekhabi, D., Jackson, T. J., Hu, L., Peng, Z., Yao, P., Li, S., and Kang, C. S.: Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm, Remote Sens. Environ., 257, 112321, https://doi.org/10.1016/j.rse.2021.112321, 2021.
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.
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0...
Altmetrics
Final-revised paper
Preprint