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.
Data description paper
| 
23 Jan 2023
Data description paper |
| 23 Jan 2023
| 23 Jan 2023
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
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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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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