Articles | Volume 13, issue 7
https://doi.org/10.5194/essd-13-3239-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3239-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
07 Jul 2021
Data description paper |
| 07 Jul 2021
| 07 Jul 2021
A fine-resolution soil moisture dataset for China in 2002–2018
Xiangjin Meng
School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Hulunbeir Grassland Ecosystem Research station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Fei Meng
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250100, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
State Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
Jiangyuan Zeng
State Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
Xinyi Shen
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Yaokui Cui
School of Earth and Space Sciences, Peking University, Beijing, China, 100871
Lingmei Jiang
State Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
Zhonghua Guo
School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
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Cited articles
Afshar, M. and Yilmaz, M.:
The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products,
Remote Sens. Environ.,
196, 224–23, https://doi.org/10.1016/j.rse.2017.05.017, 2017.
Albergel, C., Rosnay, P. D., Gruhier, C., Muño-Sabater, J. S., Hasenauer, S., Isaksen, L., Kerr, Y., and Wagner. W.:
Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations,
Remote Sens. Environ.,
118, 215–226, https://doi.org/10.1016/j.rse.2011.11.017, 2012.
Albergel, C., Dorigo, W., Balsamo, G., Muñoz-Sabatera, J., de Rosnay, P., Isaksen, L., and Wagner, W.:
Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses,
Remote Sens. Environ.,
138, 77–89, https://doi.org/10.1016/j.rse.2013.07.009, 2013.
Al-Yaari, A., Wigneron J. P., Dorigo, W., Colliander, A., Pellarin, T., Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y., and Lannoy, G.:
Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements,
Remote Sens. Environ.,
224, 289–303, https://doi.org/10.1016/j.rse.2019.02.008, 2019.
Bhagat, S. V.:
Space-borne passive microwave remote sensing of soil moisture: A review,
Recent Patents on Space Technology,
4, 119–150, https://doi.org/10.2174/221068710402150513123146#sthash.qA8fxZDt.dpuf, 2014.
Bindlish, R., Crow, W. T., and Jackson, T. J.:
Role of passive microwave remote sensing in improving flood forecasts,
IEEE Geosci. Remote S.,
1, 112–116, https://doi.org/10.1109/LGRS.2008.2002754, 2009.
Brocca, L., Melone, F., Moramarco, T., Wagner, W., and Albergel, C.:
Scaling and filtering approaches for the use of satellite soil moisture observations,
in: Remote Sensing of Energy Fluxes and Soil Moisture Content,
edited by: Petropoulos, G.,
Boca Raton, 411–426, ISBN:978-1-4665-0578-0, 2013.
Carlson, T. N., Gillies, R. R., and Perry, E. M.:
A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover,
Remote Sens. Reviews,
9, 161–173, https://doi.org/10.1080/02757259409532220, 1994.
Cashion, J., Lakshmi, V., Bosch, D., and Jackson, T. J.:
Microwave remote sensing of soil moisture: Evaluation of the TRMM microwave imager (TMI) satellite for the Little River Watershed Tifton,
J. Hydrol.,
307, 242–253, https://doi.org/10.1016/j.jhydrol.2004.10.019, 2005.
Chauhan, N. S., Miller, S., and Ardanuy, P.:
Space bore soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach,
Int. J. Remote Sens.,
22, 4599–4622, https://doi.org/10.1080/0143116031000156837, 2003.
Chen, C. F., Son, N. T., Chang, L. Y., and Chen, C. C.:
Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data,
Appl. Geogr.,
2, 463–475, https://doi.org/10.1016/j.apgeog.2010.10.002, 2011.
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.
Choi, M. and Hur, Y.:
A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products,
Remote Sens. Environ.,
124, 259–269, https://doi.org/10.1016/j.rse.2012.05.009, 2012.
Cong, D., Zhao, S., Chen, C., and Duan, Z.:
Characterization of droughts during 2001-2014 based on remote sensing: a case study of Northeast China,
Ecol. Inform.,
39, 56–67, https://doi.org/10.1016/j.ecoinf.2017.03.005, 2017.
Crow, W. T. and Zhan, X.:
Continental-scale evaluation of remotely sensed soil moisture products,
IEEE Geosci. Remote S.,
3, 451–455, https://doi.org/10.1109/LGRS.2007.896533, 2007.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera, R., 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, 2002.
Cui, C. Y., Xu, J., Zeng, J. Y., Chen, K. S., Bai, X. J., Lu, H., Chen, Q., and Zhao, T. J.:
Soil moisture mapping from satellites: an intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales,
Remote Sens.-Basel,
33, 1–19, https://doi.org/10.3390/rs10010033, 2018.
Dorigo, W., Gruber, A., De Jeu, R., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R., 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.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y., Miralles, D., and Lecomte, P.:
ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions,
Remote Sens. Environ.,
15, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
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.
Draper, C. S., Walker, J. P., and Steinle, P. J.:
An evaluation of AMSR-E derived soil moisture over Australia,
Remote Sens. Environ.,
4, 703–710, https://doi.org/10.1016/j.rse.2008.11.011, 2009.
Fernandez-Moran, R., Al-Yaari, A. A., Mialon, A., Mahmoodi, A., Al Bitar, G., De Lannoy, N., Rodriguez-Fernandez, E., Lopez-Baeza, Y., Kerr, and Wigneron, J. P.:
SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product,
Remote Sens.-Basel,
9, 1–21, https://doi.org/10.3390/rs9050457, 2017.
Franz, T. E., Zreda, M., Rosolem, R., and Ferre, T. P. A.: A universal calibration function for determination of soil moisture with cosmic-ray neutrons, Hydrol. Earth Syst. Sci., 17, 453–460, https://doi.org/10.5194/hess-17-453-2013, 2013.
González-Zamora, A., Sánchez, N., Martínez-Fernández, J., Gumuzzio, A., Piles, M., and Olmedo, E.:
Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain),
Phys. Chem. Earth,
83–84, 123–136, https://doi.org/10.1016/j.pce.2015.05.009, 2015.
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.
Guillod, B. P., Orlowsky, B., Miralles, D. G., Teuling, A. J., and Seneviratne, S. I.:
Reconciling spatial and temporal soil moisture effects on afternoon rainfall,
Nat. Commun.,
6, 1–6, https://doi.org/10.1038/ncomms7443, 2015.
Han, E., Merwade, V., and Heathman, G. C.:
Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model,
J. Hydrol.,
416–417, 98–117, https://doi.org/10.1016/j.jhydrol.2011.11.039, 2012.
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.,
15, 1120, 1–19, https://doi.org/10.1007/s12665-016-5917-6, 2016.
Jin, Y., Ge, Y., Wang, J., Chen, Y., Heuvelink, G. B. M., and Atkinson, P. M.:
Downscaling AMSR-2 soil moisture data with Geographically weighted area-to-area regression kriging,
IEEE T. Geosci. Remote,
56, 2362–2376, https://doi.org/10.1109/TGRS.2017.2778420, 2017.
Jing, W., Zhang, P., and Zhao, X.:
Reconstructing monthly ECV global soil moisture with an improved spatial resolution,
Water Resour. Manag.,
32, 1–21, https://doi.org/10.1007/s11269-018-1944-2, 2018.
Kang, C. S., Zhao, T., Shi, J., Cosh, M. H., Chen, Y., Starks, P. J., Collins, C. H., Wu, S., Sun, R., and Zheng, J.:
Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager,
IEEE T. Geosci. Remote,
5, 4018–4032, https://doi.org/10.1109/TGRS.2020.3019408, 2020.
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Bitar, A. A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Moalon, A., and Delwart, S.:
The SMOS soil moisture retrieval algorithm,
IEEE T. Geosci. Remote,
5, 1384–1403, https://doi.org/10.1109/TGRS.2012.2184548, 2012.
Kim, J. and Hogue, T. S.:
Improving spatial soil moisture representation through integration of AMSR-E and MODIS products,
IEEE T. Geosci. Remote,
2, 446–460, https://doi.org/10.1109/TGRS.2011.2161318, 2012.
Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., and Sharma, A.:
A global comparison of alternate AMSR2 soil moisture products: Why do they differ?,
Remote Sens. Environ.,
61, 43–62, https://doi.org/10.1016/j.rse.2015.02.002, 2015.
Koike, T., Nakamura, Y., Kaihotsu, I., Davva, G., Matsuura, N., Tamagawa, K., and Fujii, H.:
Development of an advanced microwave scanning radiometer (AMSR-E) algorithm of soil moisture and vegetation water content,
Proceedings of Hydraulic Engineering,
48, 217–222, https://doi.org/10.2208/prohe.48.217, 2004.
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 T. Geosci. Remote,
5, 1612–1622, https://doi.org/10.1109/TGRS.2012.2186819, 2012.
Liang, L., Sun, Q., Luo, X., Wang, J., Zhang, L., Deng, M., Di, L., and Liu, Z.:
Long-term spatial and temporal variations of vegetative drought based on vegetation condition index in China,
Ecosphere,
8, 1–15, https://doi.org/10.1002/ecs2.1919, 2017.
Liu, L., Liao, J., Chen, X., Zhou, G., and Shao, H.:
The microwave temperature vegetation drought index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010).
Remote Sens. Environ.,
199, 302–320, https://doi.org/10.1016/j.rse.2017.07.012, 2017.
Liu, Y. Y., van Dijk, A. I. J. M. V., de Jeu, R. A. M., and Holmes, T. R. H.:
An analysis of spatiotemporal variations of soil and vegetation moisture from a 29-year satellite-derived data set over mainland Australia,
Water Resour. Res.,
7, 4542–4548, https://doi.org/10.1029/2008WR007187, 2009.
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., Wager, W., McCabe, M. F., Evans, J. P., and van Dijik, 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.
Loew, A. and Schlenz, F.: A dynamic approach for evaluating coarse scale satellite soil moisture products, Hydrol. Earth Syst. Sci., 15, 75–90, https://doi.org/10.5194/hess-15-75-2011, 2011.
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.
Ma, H., Zeng J., Chen, N., Zhang, X., Cosh, M. H., and Wang,W.:
Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations.
Remote Sens. Environ.,
231, 1–14, https://doi.org/10.1016/j.rse.2019.111215, 2019.
Maltese, A., Capodici, F., Ciraolo, G., and La Loggia, G.:
Soil water content assessment: Critical issues concerning the operational application of the triangle method,
Sensors,
3, 6699–6718, https://doi.org/10.3390/s150306699, 2015.
Mao, K. B., Tang H. J., Zhang, L. X., Li, M. C., Y., and Zhao, D. Z.:
A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data,
Int. J. Remote Sens.,
10, 2905–2925, https://doi.org/10.1080/01431160701442104, 2008a.
Mao, K. B, Shi, J. C., Tang, H., Li, Z. L., Wang, X., and Chen, K. S.:
A neural network technique for separating land surface emissivity and temperature from aster imagery,
IEEE T. Geosci. Remote,
1, 200–208, https://doi.org/10.1109/TGRS.2007.907333, 2008b.
Mao, K. B., Gao, C. Y., Han, L. J., and Zhang, W.:
The drought monitoring in China by using AMSR-E data,
2010 Sec. IITA Inter. Confer. on Geosci. and Remote Sens., Qingdao, China, 28–31 August 2010,
181–184, https://doi.org/10.1109/IITA-GRS.2010.5603008, 2010.
Mao, K. B., Ma, Y., Xia, L., Tang, H. J., and Han, L, J.:
The monitoring analysis for the drought in China by using an improved MPI method,
J. Integr. Agr.,
6, 1048–1058, https://doi.org/10.1016/S2095-3119(12)60097-5, 2012,.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Liang, L., and Guo, Z.:
A fine-resolution soil moisture dataset for China in 2002 ∼ 2018 (Version 3.0) [Data set],
Zenodo,
https://doi.org/10.5281/zenodo.4738556, 2021a.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Liang, L., and Guo, Z.:
A fine-resolution soil moisture dataset for China in 2002 ∼ 2018 (Version 3.0) [Code],
Zenodo,
https://doi.org/10.5281/zenodo.4922393, 2021b.
Mohanty, B., Cosh, M., Lakshmi, V., and Montzka, C.:
Soil moisture remote sensing: state-of-the-science,
Vadose Zone J.,
1, 1–9, https://doi.org/10.2136/vzj2016.10.0105, 2017.
Molero, B., Merlin, O., Malbeteau Y, Malbéteaua, Y., Bitar, A. A., Kerr, Y., Bacon, S., Cosh, M. H., Bindlish, R., and Jackson, T. J.:
SMOS disaggregated soil moisture product at 1 km resolution: Processor overview and first validation results,
Remote Sens. Environ.,
180, 361–376, https://doi.org/10.1016/j.rse.2016.02.045, 2016.
Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A.:
Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index.
Remote Sens. Environ.,
49, 246–263, https://doi.org/10.1016/0034-4257(94)90020-5, 1994.
Moran, M. S., Peters-Lidard, C. D., Watts, J. M., and Mcelroy, S.:
Estimating soil moisture at the watershed scale with satellite-based radar and land surface models,
Can. J. Remote Sens.,
5, 805–826, https://doi.org/10.5589/m04-043, 2004.
Njoku, E. G. and Entekhabi, D.:
Passive microwave remote sensing of soil moisture,
J. Hydrol.,
184, 101–129, https://doi.org/10.1016/0022-1694(95)02970-2, 1996.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.:
Soil moisture retrieval from AMSR-E,
IEEE T. Geosci. Remote,
41, 215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
Owe, M. and Van de Griend, A. A.:
Comparison of soil moisture penetration depths for several bare soils at two microwave frequencies and implications for remote sensing,
Water Resour. Res.,
34, 2319–2327, https://doi.org/10.1029/2007JD009199, 1998.
Peng, J., Loew, A., Zhang, S., Wang, J., and Niesel, J.:
Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index,
IEEE T. Geosci. Remote,
1, 558–566, https://doi.org/10.1109/TGRS.2015.2462074, 2015.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.:
A review of spatial downscaling of satellite remotely sensed 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,
83, 36–56, https://doi.org/10.1016/j.pce.2015.02.009, 2015.
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 T. Geosci. Remote,
4, 2845–2862, https://doi.org/10.1109/TGRS.2020.3012896, 2021.
Rahimzadeh-Bajgiran, P., Omasa, K., and Shimizu, Y.:
Comparative evaluation of the vegetation dryness index (VDI), the temperature vegetation dryness index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran,
ISPRS J. Photogramm.,
68, 1–12, https://doi.org/10.1016/j.isprsjprs.2011.10.009, 2012.
Rüdiger, Christoph, Calvet, J. C., Gruhier, C., Holmes, T. R. H., De Jeu, R. A. M., and Wagner, W.:
An inter comparison of ERS-scat and AMSR-E soil moisture observations with model simulations over France,
J. Hydrol.,
2, 431–447, https://doi.org/10.1175/2008JHM997.1, 2009.
Sandholt, I., Andersen, J., and Rasmussen, K.:
A simple interpretation of the surface temperature/vegetation index space for assessment of soil moisture status,
Remote Sens. Environ.,
2, 213–224, https://doi.org/10.1016/S0034-4257(01)00274-7, 2002.
Schmugge, T., Gloersen, P., Wilheit, T., and Geiger, F.:
Remote sensing of soil moisture with microwave radiometers,
J. Geophys. Res.,
2, 317—323, 1974.
Seneviratne, S., Corti T., Davin, E., Hirschi, M., Jaeger, E., Lehner, I., Orlowsky, B., and Teuling, A.:
Investigating soil moisture-climate interactions in a changing climate: A review,
Earth-Sci. Rev.,
3–4, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Shen, X., Mao, K., Qin, Q., Hong, Y., and Zhang, G.:
Bare surface soil moisture estimation using double-angle and dual-polarization L-band radar data,
IEEE T. Geosci. Remote,
7, 3931–3942, https://doi.org/10.1109/TGRS.2012.2228209, 2013.
Shi, J., Du, Y., Du, J. Y., Jiang, L. M., Chai, L. N., Mao, K. B., Xu, P., Ni, W., J., Xiong, C., Liu, Q., Liu, C. Z., Guo, P., Cui, Q., Li, Y. Q., Chen, J., Wang, A., Q., Luo, H., J., and Wang., Y., H.:
Progresses on microwave remote sensing of land surface parameters,
Sci. China Earth Sci.,
7, 1052–1078, https://doi.org/10.1007/s11430-012-4444-x, 2012.
Shi, J., Jiang, L., Zhang, L., Chen, K., Wigneron, J., Chanzy, A., and Jackson, T.:
Physically based estimation of bare-surface soil moisture with the passive radiometers,
IEEE T. Geosci. Remote,
11, 3145–3153, https://doi.org/10.1109/TGRS.2006.876706, 2006.
Srivastava, P.:
Satellite soil moisture: review of theory and applications in water resources,
Water Resour. Manag.,
10, 3161–3176, https://doi.org/10.1007/s11269-017-1722-6, 2017.
Srivastava, P., Pandey, V., Suman, S., Gupta, M., and Islam, T.:
Chapter 2 – Available Data Sets and Satellites for Terrestrial Soil Moisture Estimation,
in: Satellite Soil Moisture Retrieval,
edited by: Srivastava, P. K., Petropoulos G. P., and Kerr, Y. K.,
Elsevier, 29–44, 2016.
Su, Z., Dorigo, W., Fernández-Prieto, D., Van Helvoirt, M., Hungershoefer, K., de Jeu, R., Parinussa, R., Timmermans, J., Roebeling, R., Schröder, M., Schulz, J., Van der Tol, C., Stammes, P., Wagner, W., Wang, L., Wang, P., and Wolters, E.: Earth observation Water Cycle Multi-Mission Observation Strategy (WACMOS), Hydrol. Earth Syst. Sci. Discuss., 7, 7899–7956, https://doi.org/10.5194/hessd-7-7899-2010, 2010.
Taylor, C. M., Gounou, A., Guichard, F., Harris, P. P., Ellis, R. J., Couvreux, F., and Kauwe, M.:
Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns,
Nat. Geosci.,
7, 430–433, https://doi.org/10.1038/ngeo1173, 2011.
Wang, J., Ling. Z., Yang, W., and Zeng, H.:
Improving spatial representation of soil moisture by integration of microwave observations and the temperature–vegetation–drought index derived from MODIS products,
ISPRS J. Photogramm.,
113, 144–154, https://doi.org/10.1016/j.isprsjprs.2016.01.009, 2016.
Yan, J., Li, K., Wang, W., Zhang, D., and Zhou, G.:
Changes in dissolved organic carbon and total dissolved nitrogen fluxes across subtropical forest ecosystems at different successional stages,
Water Resour. Res.,
5, 3681–3694, https://doi.org/10.1002/2015WR016912, 2015.
Yan, Y. B., Mao, K. B., Shi, J., Piao, S. L., Shen, X. Y., Dozier, J., Liu, Y., Ren, H. L, and Bao, Q.:
Driving forces of land surface temperature anomalous changes in North America in 2002–2018,
Sci. Rep.,
10, 1–13, https://doi.org/10.1038/s41598-020-63701-5, 2020.
Yilmaz, M. T. and Crow, W. T.:
The optimality of potential rescaling approaches in land data assimilation,
J. Hydrol.,
2, 650–660, https://doi.org/10.1175/JHM-D-12-052.1, 2013.
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.
Zeng, J., Chen, K. S., 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,
4, 2782–2795, https://doi.org/10.1109/TGRS.2019.2955542, 2020.
Zhao, B., Mao, K., Cai, Y., Shi, J., Li, Z., Qin, Z., Meng, X., Shen, X., and Guo, Z.: A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017, Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, 2020.
Zhao, J. and Chen, C. K.:
China's geography,
Higher Education Press, Beijing, China, 2011.
Zhao, S., Cong, D., He, K., Yang, H., and Qin, Z.:
Spatial-temporal variation of drought in China from 1982 to 2010 based on a modified temperature vegetation drought index (mTVDI),
Sci. Rep.,
7, 17473, https://doi.org/10.1038/s41598-017-17810-3, 2017.
Zhao, W., Sánchez, N., Lu, H., and Li, A.:
A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression,
J. Hydrol.,
563, 1009–1024, https://doi.org/10.1016/j.jhydrol.2018.06.081, 2018.
Zhou, G., Wei, X., Luo, Y., Zhang, M., and Wang, C.:
Forest recovery and river discharge at the regional scale of Guangdong province,
Water Resour. Res.,
9, 5109–5115, https://doi.org/10.1029/2009WR008829, 2010.
Zwieback, S., Su, C. H., Gruber, A., Dorigo, W. A., and Wagner, W.:
The impact of quadratic nonlinear relations between soil moisture products on uncertainty estimates from triple collocation analysis and two quadratic extensions,
J. Hydrol.,
17, 1725–1743, https://doi.org/10.1175/JHM-D-15-0213.1, 2016.
Short summary
In order to improve the accuracy of China's regional agricultural drought monitoring and climate change research, we produced a long-term series of soil moisture products by constructing a time and depth correction model for three soil moisture products with the help of ground observation data. The spatial resolution is improved by building a spatial weight decomposition model, and validation indicates that the new product can meet application needs.
In order to improve the accuracy of China's regional agricultural drought monitoring and climate...
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