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
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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.
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2023-3030, https://doi.org/10.5194/egusphere-2023-3030, 2024
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Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
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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.
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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.
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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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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
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...
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