Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-395-2023
© Author(s) 2023. 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-15-395-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
Fei Cheng
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Zhao Zhang
CORRESPONDING AUTHOR
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
School of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, China
Huimin Zhuang
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Jichong Han
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Yuchuan Luo
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Juan Cao
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Liangliang Zhang
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Jing Zhang
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
Jialu Xu
School of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, China
Fulu Tao
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographical Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Climate Impacts Group, Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland
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Related subject area
Domain: ESSD – Land | Subject: Pedology
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
An integrated dataset of ground hydrothermal regimes and soil nutrients monitored during 2016–2022 in some previously burned areas in hemiboreal forests in Northeast China
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)
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
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
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A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
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
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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.
Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Șerban, and Tao Zhan
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In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous observation system has been gradually established for ground hydrothermal regimes and soil nutrient contents 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.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi
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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.
Niels Hindrik Batjes, Luis Calisto, and Luis Moreira de Sousa
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.:
TerraClimate, a high-resolution global dataset of monthly climate and
climatic water balance from 1958–2015, Sci. Data, 5, 170191,
https://doi.org/10.1038/sdata.2017.191, 2018.
Ahmad, S., Kalra, A., and Stephen, H.: Estimating soil moisture using remote
sensing data: A machine learning approach, Adv. Water Resour., 33,
69–80, https://doi.org/10.1016/j.advwatres.2009.10.008, 2010.
Ahmed, A. A. M., Deo, R. C., Raj, N., Ghahramani, A., Feng, Q., Yin, Z., and
Yang, L.: Deep Learning Forecasts of Soil Moisture: Convolutional Neural
Network and Gated Recurrent Unit Models Coupled with Satellite-Derived
MODIS, Observations and Synoptic-Scale Climate Index Data, Remote Sensing,
13, 554, https://doi.org/10.3390/rs13040554, 2021.
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.
Amazirh, A., Merlin, O., Er-Raki, S., Gao, Q., Rivalland, V., Malbeteau, Y.,
Khabba, S., and Escorihuela, M. J.: Retrieving surface soil moisture at high
spatio-temporal resolution from a synergy between Sentinel-1 radar and
Landsat thermal data: A study case over bare soil, Remote Sens.
Environ., 211, 321–337, https://doi.org/10.1016/j.rse.2018.04.013, 2018.
Birba, D. E.: A Comparative study of data splitting algorithms for machine learning model selection, Dissertation, KTH Royal Institute of Technology, Stockholm, Sweden, 1–19, 2020.
Bogena, H. R., Huisman, J. A., Oberdörster, C., and Vereecken, H.:
Evaluation of a low-cost soil water content sensor for wireless network
applications, J. Hydrol., 344, 32–42,
https://doi.org/10.1016/j.jhydrol.2007.06.032, 2007.
Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001.
Brownlee, J.: Machine learning mastery with python, Mach. Learn. Mastery Pty Ltd., 527, 100–120, 2016.
Chakrabarti, S., Bongiovanni, T., Judge, J., Zotarelli, L., and Bayer, C.:
Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop
Yield in Agricultural Regions, IEEE J. Sel. Top. Appl. Earth Observations
Remote Sensing, 7, 3867–3879, https://doi.org/10.1109/JSTARS.2014.2315999,
2014.
Chen, L. and Dirmeyer, P. A.: Global observed and modelled impacts of
irrigation on surface temperature, Int. J. Climatol., 39, 2587–2600,
https://doi.org/10.1002/joc.5973, 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.
Cheng, F., Zhang, Z., Zhuang, H., Han, J., Luo, Y., Cao, J.,
Zhang, L., Zhang, J., Tao, F., and Xu, J.: ChinaCropSM1km: a fine
1km daily Soil Moisture dataset for dryland wheat and maize across China
during 1993–2018 (wheat0–10cm), Zenodo [data set], https://doi.org/10.5281/zenodo.6834530,
2022a.
Cheng, F., Zhang, Z., Zhuang, H., Han, J., Luo, Y., Cao, J., Zhang,
L., Zhang, J., Tao, F., and Xu, J.: ChinaCropSM1km: a fine
1km daily Soil Moisture dataset for dryland wheat and maize across China
during 1993–2018 (wheat10–20cm), Zenodo [data set], https://doi.org/10.5281/zenodo.6822591,
2022b.
Cheng, F., Zhang, Z., Zhuang, H., Han, J., Luo, Y., Cao, J.,
Zhang, L., Zhang, J., Tao, F., and Xu, J.: ChinaCropSM1km: a fine
1km daily Soil Moisture dataset for dryland wheat and maize across China
during 1993–2018 (maize0–10cm), Zenodo [data set], https://doi.org/10.5281/zenodo.6822581,
2022c.
Cheng, F., Zhang, Z., Zhuang, H., Han, J., Luo, Y., Cao, J.,
Zhang, L., Zhang, J., Tao, F., and Xu, J.: ChinaCropSM1km: a fine
1km daily Soil Moisture dataset for dryland wheat and maize across China
during 1993–2018 (maize10–20cm), Zenodo [data set], https://doi.org/10.5281/zenodo.6820166,
2022d.
Collow, T. W., Robock, A., Basara, J. B., and Illston, B. G.: Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations, Geophys. Res., 117, D09113, https://doi.org/10.1029/2011JD017095, 2012.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera, R., De Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products, Rev. Geophys., 50, 1–20, https://doi.org/10.1029/2011RG000372, 2012.
Danielsson, P.-E.: Euclidean distance mapping, Computer Graphics and Image
Processing, 14, 227–248, https://doi.org/10.1016/0146-664X(80)90054-4,
1980.
Díaz-Uriarte, R. and Alvarez de Andrés, S.: Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7, 1–13, https://doi.org/10.1186/1471-2105-7-3, 2006.
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., 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.
Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., and Jacob, R.: Modeling agriculture in the Community Land Model, Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, 2013.
Eagleman, J. R. and Lin, W. C.: Remote sensing of soil moisture by a 21-cm
passive radiometer, J. Geophys. Res., 81, 3660–3666,
https://doi.org/10.1029/JC081i021p03660, 1976.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP)
Mission, Proc. IEEE, 98, 704–716,
https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Famiglietti, J. S., Ryu, D., Berg, A. A., Rodell, M., and Jackson, T. J.: Field observations of soil moisture variability across scales, Water Resour. Res., 44, W01423, https://doi.org/10.1029/2006WR005804, 2008.
Fang, B., Lakshmi, V., Bindlish, R., Jackson, T. J., and Liu, P.-W.:
Evaluation and validation of a high spatial resolution satellite soil
moisture product over the Continental United States, J. Hydrol.,
588, 125043, https://doi.org/10.1016/j.jhydrol.2020.125043, 2020.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett.,
27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Food and Agriculture Organization Corporate Statistical Database (FAOSTAT):
FAO online database, Crops and livestock products http://www.fao.org/faostat/en/#data/QCL (last access: 15 October 2021),
2019.
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.
Guevara, M. and Vargas, R.: Downscaling satellite soil moisture using
geomorphometry and machine learning, PLoS ONE, 14, e0219639,
https://doi.org/10.1371/journal.pone.0219639, 2019.
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.
Hengl, T. and Gupta, S.: Soil water content (volumetric %) for 33 kPa and
1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (v0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.2629589,
2019.
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M.
G., Shepherd, K. D., Sila, A., MacMillan, R. A., Mendes de Jesus, J.,
Tamene, L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m
Resolution: Random Forests Significantly Improve Current Predictions, PLoS
ONE, 10, e0125814, https://doi.org/10.1371/journal.pone.0125814, 2015.
Huang, S., Krysanova, V., Zhai, J., and Su, B.: Impact of Intensive
Irrigation Activities on River Discharge Under Agricultural Scenarios in the
Semi-Arid Aksu River Basin, Northwest China, Water Resour. Manage., 29,
945–959, https://doi.org/10.1007/s11269-014-0853-2, 2015.
Hutengs, C. and Vohland, M.: Downscaling land surface temperatures at
regional scales with random forest regression, Remote Sens. Environ., 178, 127–141, https://doi.org/10.1016/j.rse.2016.03.006, 2016.
Im, J., Park, S., Rhee, J., Baik, J., and Choi, M.: Downscaling
of AMSR-E soil moisture with MODIS products using machine learning approaches, Environ. Earth Sci., 75, 1120,
https://doi.org/10.1007/s12665-016-5917-6, 2016.
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.
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K.,
Butler, E. E., Timlin, D. J., Shim, K.-M., Gerber, J. S., Reddy, V. R., and
Kim, S.-H.: Random Forests for Global and Regional Crop Yield Predictions,
PLoS ONE, 11, e0156571, https://doi.org/10.1371/journal.pone.0156571, 2016.
Karrou, M., Oweis, T., El-Enein, R. A., and Sherif, M.: Yield and water productivity of maize and wheat under deficit and raised bed irrigation practices in Egypt, Afr. J. Agric. Res., 7, 1755–1760, https://academicjournals.org/journal/AJAR/article-abstract/5EA3C6D39463 (last access: 10 October 2022), 2012.
Lacava, T., Matgen, P., Brocca, L., Bittelli, M., Pergola, N., Moramarco,
T., and Tramutoli, V.: A First Assessment of the SMOS Soil Moisture Product
With In Situ and Modeled Data in Italy and Luxembourg, IEEE Trans. Geosci.
Remote, 50, 1612–1622, https://doi.org/10.1109/TGRS.2012.2186819,
2012.
Lagomarsino, D., Tofani, V., Segoni, S., Catani, F., and Casagli, N.: A Tool
for Classification and Regression Using Random Forest Methodology:
Applications to Landslide Susceptibility Mapping and Soil Thickness
Modeling, Environ. Model Assess., 22, 201–214,
https://doi.org/10.1007/s10666-016-9538-y, 2017.
Lawston, P. M., Santanello, J. A., and Kumar, S. V.: Irrigation Signals
Detected From SMAP Soil Moisture Retrievals: Irrigation Signals Detected
From SMAP, Geophys. Res. Lett., 44, 11860–11867,
https://doi.org/10.1002/2017GL075733, 2017.
Li, H., Robock, A., Liu, S., Mo, X., and Viterbo, P.: Evaluation of
Reanalysis Soil Moisture Simulations Using Updated Chinese Soil Moisture
Observations, J. Hydrometeorol., 6, 180–193,
https://doi.org/10.1175/JHM416.1, 2005.
Li, Z., Zhang, Z., and Zhang, L.: Improving regional wheat drought risk
assessment for insurance application by integrating scenario-driven crop
model, machine learning, and satellite data, Agr. Syst., 191,
103141, https://doi.org/10.1016/j.agsy.2021.103141, 2021.
Liu, Y., Yang, Y., and Yue, X.: Evaluation of Satellite-Based Soil Moisture
Products over Four Different Continental In-Situ Measurements, Remote
Sensing, 10, 1161, https://doi.org/10.3390/rs10071161, 2018.
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, 2011.
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.
Llamas, R. M., Guevara, M., Rorabaugh, D., Taufer, M., and Vargas, R.:
Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on
Geostatistical Techniques and Multiple Regression, Remote Sensing, 12, 665,
https://doi.org/10.3390/rs12040665, 2020.
Loew, A., Stacke, T., Dorigo, W., de Jeu, R., and Hagemann, S.: Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies, Hydrol. Earth Syst. Sci., 17, 3523–3542, https://doi.org/10.5194/hess-17-3523-2013, 2013.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020a.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.:
Identifying the spatiotemporal changes of annual harvesting areas for three
staple crops in China by integrating multi-data sources, Environ. Res.
Lett., 15, 074003, https://doi.org/10.1088/1748-9326/ab80f0, 2020b.
Mallick, K., Bhattacharya, B. K., and Patel, N. K.: Estimating volumetric
surface moisture content for cropped soils using a soil wetness index based
on surface temperature and NDVI, Agr. Forest Meteorol., 149,
1327–1342, https://doi.org/10.1016/j.agrformet.2009.03.004, 2009.
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.
Mohanty, B. P., Cosh, M. H., Lakshmi, V., and Montzka, C.: Soil Moisture
Remote Sensing: State-of-the-Science, Vadose Zone J., 16,
vzj2016.10.0105, https://doi.org/10.2136/vzj2016.10.0105, 2017.
O, S. and Orth, R.: Global soil moisture from in-situ measurements using
machine learning – SoMo.ml, arXiv [preprint], https://doi.org/10.48550/arxiv.2010.02374, 5 October
2020.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial
downscaling of satellite remotely sensed soil moisture: Downscale
Satellite-Based Soil Moisture, Rev. Geophys., 55, 341–366,
https://doi.org/10.1002/2016RG000543, 2017.
Petropoulos, G. P., Ireland, G., and Barrett, B.: Surface soil moisture
retrievals from remote sensing: Current status, products & future trends,
Phys. Chem. Earth. Parts A/B/C, 83–84, 36–56,
https://doi.org/10.1016/j.pce.2015.02.009, 2015.
Prasad, A. K., Chai, L., Singh, R. P., and Kafatos, M.: Crop yield
estimation model for Iowa using remote sensing and surface parameters,
Int. J. Appl. Earth Obs., 8,
26–33, https://doi.org/10.1016/j.jag.2005.06.002, 2006.
Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A., and Dorigo, W.:
Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture
Multisatellite Climate Data Record, IEEE Trans. Geosci. Remote, 59,
2845–2862, https://doi.org/10.1109/TGRS.2020.3012896, 2021.
Qin, J., Yang, K., Lu, N., Chen, Y., Zhao, L., and Han, M.: Spatial
upscaling of in-situ soil moisture measurements based on MODIS-derived
apparent thermal inertia, Remote Sens. Environ., 138, 1–9,
https://doi.org/10.1016/j.rse.2013.07.003, 2013.
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., 48, 17–27,
https://doi.org/10.1016/j.jag.2015.11.012, 2016a.
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., 48, 17–27,
https://doi.org/10.1016/j.jag.2015.11.012, 2016b.
Rasmussen, C. E.: Gaussian Processes in Machine Learning, in: Advanced
Lectures on Machine Learning, vol. 3176, edited by: Bousquet, O., von
Luxburg, U., and Rätsch, G., Springer Berlin Heidelberg, Berlin,
Heidelberg, 63–71, https://doi.org/10.1007/978-3-540-28650-9_4, 2004.
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.
Schmugge, T. J., Kustas, W. P., Ritchie, J. C., Jackson, T. J., and Rango,
A.: Remote sensing in hydrology, Adv. Water Resour., 25,
1367–1385, https://doi.org/10.1016/S0309-1708(02)00065-9, 2002.
Sheffield, J.: A simulated soil moisture based drought analysis for the
United States, J. Geophys. Res., 109, D24108,
https://doi.org/10.1029/2004JD005182, 2004.
Shen, Y., Zhang, Y., R. Scanlon, B., Lei, H., Yang, D., and Yang, F.:
Energy/water budgets and productivity of the typical croplands irrigated
with groundwater and surface water in the North China Plain, Agr. Forest Meteorol., 181, 133–142,
https://doi.org/10.1016/j.agrformet.2013.07.013, 2013.
Srivastava, P. K., Han, D., Ramirez, M. R., and Islam, T.: Machine Learning
Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land
Surface Temperature for Hydrological Application, Water Resour. Manage., 27,
3127–3144, https://doi.org/10.1007/s11269-013-0337-9, 2013.
Su, C.-H., Zhang, J., Gruber, A., Parinussa, R., Ryu, D., Crow, W. T., and
Wagner, W.: Error decomposition of nine passive and active microwave
satellite soil moisture data sets over Australia, Remote Sens. Environ., 182, 128–140, https://doi.org/10.1016/j.rse.2016.05.008, 2016.
Sun, C., Bian, Y., Zhou, T., and Pan, J.: Using of Multi-Source and
Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the
Subtropical Agriculture Region, Sensors, 19, 2401,
https://doi.org/10.3390/s19102401, 2019.
Tao, F., Yokozawa, M., Hayashi, Y., and Lin, E.: Changes in agricultural
water demands and soil moisture in China over the last half-century and
their effects on agricultural production, Agr. Forest Meteorol., 118, 251–261, https://doi.org/10.1016/S0168-1923(03)00107-2,
2003.
Thornthwaite, C. W.: An Approach toward a Rational Classification of
Climate, Geogr. Rev., 38, 55–94, https://doi.org/10.2307/210739, 1948.
Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan,
S., and Wood, E. F.: Combining hyper-resolution land surface modeling with
SMAP brightness temperatures to obtain 30-m soil moisture estimates, Remote Sens. Environ., 242, 111740,
https://doi.org/10.1016/j.rse.2020.111740, 2020.
Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., and Ertl, M.: Fusion of Active and Passive Microwave Observations to Create an Essential Climate Variable Data Record on Soil Moisture, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 315–321, https://doi.org/10.5194/isprsannals-I-7-315-2012, 2012.
Walker, J. P., Willgoose, G. R., and Kalma, J. D.: 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, C., Wang, Z.-H., and Yang, J.: Urban water capacity: Irrigation for
heat mitigation, Computers, Environment and Urban Systems, 78, 101397,
https://doi.org/10.1016/j.compenvurbsys.2019.101397, 2019.
Wang, L. and Qu, J. J.: Satellite remote sensing applications for surface
soil moisture monitoring: A review, Front. Earth Sci. China, 3, 237–247,
https://doi.org/10.1007/s11707-009-0023-7, 2009.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44,
https://doi.org/10.1016/j.rse.2019.02.022, 2019.
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,
2003.
Wu, B. and Li, Q.: Crop planting and type proportion method for crop acreage
estimation of complex agricultural landscapes, Int. J.
Appl. Earth Obs., 16, 101–112,
https://doi.org/10.1016/j.jag.2011.12.006, 2012.
Wu, B., Ma, Z., and Yan, N.: Agricultural drought mitigating indices derived
from the changes in drought characteristics, Remote Sens. Environ.,
244, 111813, https://doi.org/10.1016/j.rse.2020.111813, 2020.
Yilmaz, M. T., Crow, W. T., Anderson, M. C., and Hain, C.: An objective methodology for merging satellite‐and model‐based soil moisture products, Water Resour. Res., 48, W11502, https://doi.org/10.1029/2011WR011682, 2012.
Yin, X. G., Jabloun, M., Olesen, J. E., Öztürk, I., Wang, M., and
Chen, F.: Effects of climatic factors, drought risk and irrigation
requirement on maize yield in the Northeast Farming Region of China, J.
Agric. Sci., 154, 1171–1189, https://doi.org/10.1017/S0021859616000150,
2016.
Zeng, J., Li, Z., Chen, Q., Bi, H., Qiu, J., and Zou, P.: 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, 2015.
Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., and Si, Y.: A
Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests
and XGboost, IEEE Access, 6, 21020–21031,
https://doi.org/10.1109/ACCESS.2018.2818678, 2018.
Zhang, Q., Sun, P., Li, J., Singh, V. P., and Liu, J.: Spatiotemporal
properties of droughts and related impacts on agriculture in Xinjiang,
China: Spatiotemporal properties of droughts and related impacts, Int. J.
Climatol., 35, 1254–1266, https://doi.org/10.1002/joc.4052, 2015.
Zhang, Q., Shi, R., Singh, V. P., Xu, C.-Y., Yu, H., Fan, K., and Wu, Z.:
Droughts across China: Drought factors, prediction and impacts, Sci.
Total Environ., 803, 150018,
https://doi.org/10.1016/j.scitotenv.2021.150018, 2022.
Zhang, Z., Li, Z., Chen, Y., Zhang, L., and Tao, F.: Improving regional
wheat yields estimations by multi-step-assimilating of a crop model with
multi-source data, Agr. Forest Meteorol., 290, 107993,
https://doi.org/10.1016/j.agrformet.2020.107993, 2020.
Zhu, X., Li, Y., Li, M., Pan, Y., and Shi, P.: Agricultural irrigation in
China, J. Soil Water Conserv., 68, 147A–154A,
https://doi.org/10.2489/jswc.68.6.147A, 2013.
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.
We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China...
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