1,126
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method

, , , & ORCID Icon
Article: 2290337 | Received 10 Jul 2023, Accepted 28 Nov 2023, Published online: 06 Dec 2023

References

  • Abowarda, A. S., L. Bai, C. Zhang, D. Long, X. Li, Q. Huang, and Z. Sun. 2021. “Generating Surface Soil Moisture at 30 M Spatial Resolution Using Both Data Fusion and Machine Learning Toward Better Water Resources Management at the Field Scale.” Remote Sensing of Environment 255:112301. https://doi.org/10.1016/j.rse.2021.112301
  • Carlson, T. N., E. M. Perry, and T. J. Schmugge. 1990. “Remote Estimation of Soil Moisture Availability and Fractional Vegetation Cover for Agricultural Fields.” Agricultural and Forest Meteorology 52:45–23. https://doi.org/10.1016/0168-1923(90)90100-K
  • Chawla, I., L. Karthikeyan, and A. K. Mishra. 2020. “A Review of Remote Sensing Applications for Water Security: Quantity, Quality, and Extremes.” Canadian Journal of Fisheries and Aquatic Sciences 585:124826. https://doi.org/10.1016/j.jhydrol.2020.124826
  • Chen, X., S. An, D. W. Inouye, and M. D. Schwartz. 2015. “Temperature and Snowfall Trigger Alpine Vegetation Green-Up on the World’s Roof.” Global Change Biology 21:3635–3646. https://doi.org/10.1111/gcb.12954 10
  • Chen, C.-F., N.-T. Son, L.-Y. Chang, and C.-C. Chen. 2011. “Monitoring of Soil Moisture Variability in Relation to Rice Cropping Systems in the Vietnamese Mekong Delta Using MODIS Data.” Applied Geography 31:463–475. https://doi.org/10.1016/j.apgeog.2010.10.002
  • Das, N. N., D. Entekhabi, E. G. Njoku, J. J. C. Shi, J. T. Johnson, and A. Colliander. 2014. “Tests of the SMAP Combined Radar and Radiometer Algorithm Using Airborne Field Campaign Observations and Simulated Data.” IEEE Transactions on Geoscience & Remote Sensing 52 (4): 2018–2028. https://doi.org/10.1109/TGRS.2013.2257605.
  • Dorigo, W., I. Himmelbauer, D. Aberer, L. Schremmer, I. Petrakovic, L. Zappa, W. Preimesberger, et al. 2021. “The International Soil Moisture Network: Serving Earth System Science for Over a Decade, Hydrol.” Earth System Science 25 (11): 5749-5804, 10.5194/hess-25-5749–2021. https://doi.org/10.5194/hess-25-5749-2021.
  • Dorigo, W., W. Wagner, C. Albergel, F. Albrecht, G. Balsamo, L. Brocca, D. Chung, et al. 2017. “ESA CCI Soil Moisture for Improved Earth System Understanding: State-Of-The Art and Future Directions.” Remote Sensing of Environment 203:185–215. https://doi.org/10.1016/j.rse.2017.07.001
  • Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, et al. 2011. “The International Soil Moisture Network: A Data Hosting Facility for Global in situ Soil Moisture Measurements, Hydrol.” Earth System Science 15 (5): 1675-1698, 10.5194/hess-15-1675–2011. https://doi.org/10.5194/hess-15-1675-2011.
  • Entekhabi, D., E. G. Njoku, P. E. O. Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, et al. 2010. The Soil Moisture Active Passive (SMAP) Mission, Proceedings of the IEEE 98 (5): 704–716. https://doi.org/10.1109/JPROC.2010.2043918
  • Fensholt, R., and I. Sandholt. 2003. “Derivation of a Shortwave Infrared Water Stress Index from MODIS Near- and Shortwave Infrared Data in a Semiarid Environment.” Remote Sensing of Environment 87:111–121. https://doi.org/10.1016/j.rse.2003.07.002
  • Gao, B.-C. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
  • Guillod, B. P., B. Orlowsky, D. G. Miralles, A. J. Teuling, and S. I. Seneviratne. 2015. “Reconciling Spatial and Temporal Soil Moisture Effects on Afternoon Rainfall.” Nature Communications 6:6443. https://doi.org/10.1038/ncomms7443
  • Han, Q., Y. Zeng, L. Zhang, C. Wang, E. Prikaziuk, Z. Niu, and B. Su. 2023. “Global Long Term Daily 1 km Surface Soil Moisture Dataset with Physics Informed Machine Learning.” Scientific Data 10 (1): 10.1038/s41597-023-02011–7. https://doi.org/10.1038/s41597-023-02011-7.
  • He, H., and M. Dyck. 2013. “Application of Multiphase Dielectric Mixing Models for Understanding the Effective Dielectric Permittivity of Frozen Soils.” Vadose Zone Journal 12 (1): 1–22. https://doi.org/10.2136/vzj2012.0060
  • He Shaoyang, Z. Y. 2022. “Evapotranspiration and Gross Primary Production dataset(2000.02.26-2020.12.31), National Tibetan Plateau/Third Pole Environment Data Center [Dataset].” PML-V2(china). https://doi.org/10.11888/Terre.tpdc.272389.
  • He, S., Y. Zhang, N. Ma, J. Tian, D. Kong, and C. Liu. 2022. “A Daily and 500m Coupled Evapotranspiration and Gross Primary Production Product Across China During 2000–2020.” Earth System Science Data 14 (12): 5463-5488, 10.5194/essd-14-5463–2022. https://doi.org/10.5194/essd-14-5463-2022.
  • He, K., X. Zhang, S. Ren, and J. Sun: Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016, 770–778, https://doi.org/10.1109/CVPR.2016.90
  • Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
  • Hong, Z., W. Zhang, C. Yu, D. Zhang, L. Li, and L. Meng. 2018. “SWCTI: Surface Water Content Temperature Index for Assessment of Surface Soil Moisture Status, 10.3390/s18092875.” Sensors 18 (9): 2875. https://doi.org/10.3390/s18092875.
  • Huete, A. R., H. Q. Liu, K. Batchily, and W. van Leeuwen. 1997. “A Comparison of Vegetation Indices Over a Global Set of TM Images for EOS-MODIS.” Remote Sensing of Environment 59 (3): 440–451. https://doi.org/10.1016/S0034-4257(96)00112-5.
  • Jin, Y., Y. Ge, Y. Liu, Y. Chen, H. Zhang, and G. B. Heuvelink. 2021. “M.: A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14:1025–1037. https://doi.org/10.1109/JSTARS.2020.3035386.
  • Karamouz, M., R. S. Alipour, M. Roohinia, and M. Fereshtehpour. 2022. “A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling with Geostatistically Based Use of Ancillary Data.” Water Resources Research 58:e2022WR031946. https://doi.org/10.1029/2022WR031946.
  • Karthikeyan, L., and A. K. Mishra. 2021. “Multi-Layer High-Resolution Soil Moisture Estimation Using Machine Learning Over the United States.” Remote Sensing of Environment 266:112706. https://doi.org/10.1016/j.rse.2021.112706.
  • Koster, R. D., P. A. Dirmeyer, Z. Guo, G. Bonan, E. Chan, P. Cox, C. T. Gordon, et al. 2004. “Regions of Strong Coupling Between Soil Moisture and Precipitation.” Science 305:1138–1140. https://doi.org/10.1126/science.1100217.
  • LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation Applied to Handwritten Zip Code Recognition.” Neural Computation 1:541–551. https://doi.org/10.1162/neco.1989.1.4.541.
  • Leng, P., Z. Yang, Q.-Y. Yan, G.-F. Shang, X. Zhang, X.-J. Han, and Z.-L. Li. 2023. “A Framework for Estimating All-Weather Fine Resolution Soil Moisture from the Integration of Physics-Based and Machine Learning-Based Algorithms.” Computers and Electronics in Agriculture 206:107673. https://doi.org/10.1016/j.compag.2023.107673.
  • Li, Q., G. Shi, W. Shangguan, V. Nourani, J. Li, L. Li, F. Huang, et al. 2022. “A 1km Daily Soil Moisture Dataset Over China Using in situ Measurement and Machine Learning.” Earth System Science Data 14 (12): 5267–5286. https://doi.org/10.5194/essd-14-5267-2022.
  • Liu, J., L. Chai, J. Dong, D. Zheng, J. P. Wigneron, S. Liu, J. Zhou, et al. 2021. “Uncertainty Analysis of Eleven Multisource Soil Moisture Products in the Third Pole Environment Based on the Three-Corned Hat Method.” Remote Sensing of Environment 255:112225. https://doi.org/10.1016/j.rse.2020.112225.
  • Liu, Y. Y., W. A. Dorigo, R. M. Parinussa, R. A. M. de Jeu, W. Wagner, M. F. McCabe, J. P. Evans, and A. I. J. M. van Dijk. 2012. “Trend-Preserving Blending of Passive and Active Microwave Soil Moisture Retrievals.” Remote Sensing of Environment 123:280–297. https://doi.org/10.1016/j.rse.2012.03.014.
  • Liu, F., H. Wu, Y. Zhao, D. Li, J.-L. Yang, X. Song, Z. Shi, A. X. Zhu, and G.-L. Zhang. 2022. “Mapping High Resolution National Soil Information Grids of China.” Science Bulletin 67:328–340. https://doi.org/10.1016/j.scib.2021.10.013.
  • Liu, F., G.-L. Zhang, X. Song, D. Li, Y. Zhao, J. Yang, H. Wu, and F. Yang. 2020. “High-Resolution and Three-Dimensional Mapping of Soil Texture of China.” Geoderma 361:114061. https://doi.org/10.1016/j.geoderma.2019.114061.
  • Li, X., J.-P. Wigneron, L. Fan, F. Frappart, S. H. Yueh, A. Colliander, A. Ebtehaj, et al. 2022. “A New SMAP Soil Moisture and Vegetation Optical Depth Product (SMAP-IB): Algorithm, Assessment and Inter-Comparison.” Remote Sensing of Environment 271:112921. https://doi.org/10.1016/j.rse.2022.112921.
  • Li, X., J.-P. Wigneron, F. Frappart, G. D. Lannoy, L. Fan, T. Zhao, L. Gao, et al. 2022c. “The First Global Soil Moisture and Vegetation Optical Depth Product Retrieved from Fused SMOS and SMAP L-Band Observations.” Remote Sensing of Environment 282:113272. 10.1016/j.rse.2022.113272.
  • Long, D., L. Bai, L. Yan, C. Zhang, W. Yang, H. Lei, J. Quan, X. Meng, and C. Shi. 2019. “Generation of Spatially Complete and Daily Continuous Surface Soil Moisture of High Spatial Resolution.” Remote Sensing of Environment 233:111364. https://doi.org/10.1016/j.rse.2019.111364.
  • Long, D., L. Yan, L. Bai, C. Zhang, X. Li, H. Lei, H. Yang, et al. 2020. “Generation of MODIS-Like Land Surface Temperatures Under All-Weather Conditions Based on a Data Fusion Approach.” Remote Sensing of Environment 246:111863. https://doi.org/10.1016/j.rse.2020.111863.
  • Lu, L., G.-P. Luo, and J.-Y. Wang. 2014. “Development of an ATI-NDVI Method for Estimation of Soil Moisture from MODIS Data.” International Journal of Remote Sensing 35:3797–3815. https://doi.org/10.1080/01431161.2014.919677.
  • Ma, H., X. Li, J. Zeng, X. Zhang, J. Dong, N. Chen, L. Fan, et al. 2023. “An Assessment of L-Band Surface Soil Moisture Products from SMOS and SMAP in the Tropical Areas.” Remote Sensing of Environment 284:113344. https://doi.org/10.1016/j.rse.2022.113344.
  • Ma, H., J. Zeng, N. Chen, X. Zhang, M. H. Cosh, and W. Wang. 2019. “Satellite Surface Soil Moisture from SMAP, SMOS, AMSR2 and ESA CCI: A Comprehensive Assessment Using Global Ground-Based Observations.” Remote Sensing of Environment 231:111215. https://doi.org/10.1016/j.rse.2019.111215.
  • Ma, H., J. Zeng, X. Zhang, P. Fu, D. Zheng, J.-P. Wigneron, N. Chen, and D. Niyogi. 2021. “Evaluation of Six Satellite- and Model-Based Surface Soil Temperature Datasets Using Global Ground-Based Observations.” Remote Sensing of Environment 264:112605. https://doi.org/10.1016/j.rse.2021.112605.
  • Meng, X., R. Li, L. Luan, S. Lyu, T. Zhang, Y. Ao, B. Han, L. Zhao, and Y. Ma. 2018. “Detecting Hydrological Consistency Between Soil Moisture and Precipitation and Changes of Soil Moisture in Summer Over the Tibetan Plateau.” Climate Dynamics 51 (11–12): 4157-4168, 10.1007/s00382-017-3646–5. https://doi.org/10.1007/s00382-017-3646-5.
  • Meng, X., K. Mao, F. Meng, J. Shi, J. Zeng, X. Shen, Y. Cui, L. Jiang, and Z. Guo. 2021. “A fine-resolution soil moisture dataset for China in 2002–2018.” Earth System Science Data 13 (7): 3239-3261, 10.5194/essd-13-3239–2021. https://doi.org/10.5194/essd-13-3239-2021.
  • Merlin, O., C. Rudiger, A. A. Bitar, P. Richaume, J. P. Walker, and Y. H. Kerr. 2012. “Disaggregation of SMOS Soil Moisture in Southeastern Australia.” IEEE Transactions on Geoscience & Remote Sensing 50:1556–1571. https://doi.org/10.1109/TGRS.2011.2175000.
  • Ming, W., X. Ji, M. Zhang, Y. Li, C. Liu, Y. Wang, and J. Li. 2022. “A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data, 10.3390/rs14071744.” Remote Sensing 14 (7): 1744. https://doi.org/10.3390/rs14071744.
  • Parinussa, R. M., V. Lakshmi, F. M. Johnson, and A. Sharma. 2016. “A New Framework for Monitoring Flood Inundation Using Readily Available Satellite Data.” Geophysical Research Letters 43:2599–2605. https://doi.org/10.1002/2016GL068192.
  • Peng, J., J. Niesel, and A. Loew. 2015. “Evaluation of Soil Moisture Downscaling Using a Simple Thermal-Based Proxy – the REMEDHUS Network (Spain) Example, Hydrol.” Earth System Science 19 (12): 4765–4782. https://doi.org/10.5194/hess-19-4765-2015.
  • Piles, M., D. Entekhabi, and A. Camps. 2009. “A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture from SMAP Radar and Radiometer Observations.” IEEE Transactions on Geoscience & Remote Sensing 47:4125–4131. https://doi.org/10.1109/TGRS.2009.2022088.
  • Qin, Q., C. Jin, N. Zhang, and X. Yang: An Two-Dimensional Spectral Space Based Model for Drought Monitoring and Its Re-Examination, 2010 IEEE International Geoscience and Remote Sensing Symposium, 25-30 July 2010, 3869–3872, https://doi.org/10.1109/IGARSS.2010.5649710
  • Qu, Y., Z. Zhu, C. Montzka, L. Chai, S. Liu, Y. Ge, J. Liu, et al. 2021. “Inter-Comparison of Several Soil Moisture Downscaling Methods Over the Qinghai-Tibet Plateau.” China, Journal of Hydrology 592:125616. https://doi.org/10.1016/j.jhydrol.2020.125616.
  • Rao, P., Y. Wang, F. Wang, Y. Liu, X. Wang, and Z. Wang. 2022. “Daily Soil Moisture Mapping at 1km Resolution Based on SMAP Data for Desertification Areas in Northern China.” Earth System Science Data 14 (7): 3053-3073, 10.5194/essd-14-3053–2022. https://doi.org/10.5194/essd-14-3053-2022.
  • Reul, N., S. A. Grodsky, M. Arias, J. Boutin, R. Catany, B. Chapron, F. D’Amico, et al. 2020. “Sea Surface Salinity Estimates from Spaceborne L-Band Radiometers: An Overview of the First Decade of Observation (2010–2019.” Remote Sensing of Environment 242:111769. https://doi.org/10.1016/j.rse.2020.111769.
  • Sabaghy, S., J. P. Walker, L. J. Renzullo, and T. J. Jackson. 2018. “Spatially Enhanced Passive Microwave Derived Soil Moisture: Capabilities and Opportunities.” Remote Sensing of Environment 209:551–580. https://doi.org/10.1016/j.rse.2018.02.065.
  • Sadeghi, M., S. B. Jones, and W. Philpot. 2015. “D.: A Linear Physically-Based Model for Remote Sensing of Soil Moisture Using Short Wave Infrared Bands.” Remote Sensing of Environment 164:66–76. https://doi.org/10.1016/j.rse.2015.04.007.
  • Sandholt, I., K. Rasmussen, and J. Andersen. 2002. “A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status.” Remote Sensing of Environment 79:213–224. https://doi.org/10.1016/S0034-4257(01)00274-7.
  • Senanayake, I. P., I. Y. Yeo, J. P. Walker, and G. R. Willgoose. 2021. “Estimating Catchment Scale Soil Moisture at a High Spatial Resolution: Integrating Remote Sensing and Machine Learning.” Science of the Total Environment 776:145924. https://doi.org/10.1016/j.scitotenv.2021.145924.
  • Shangguan, Y., X. Min, and Z. Shi. 2023a. “Gap Filling of the ESA CCI Soil Moisture Data Using a Spatiotemporal Attention-Based Residual Deep Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16:5344–5354. https://doi.org/10.1109/JSTARS.2023.3284841.
  • Shangguan, Y., X. Min, and Z. Shi. 2023b. “Inter-Comparison and Integration of Different Soil Moisture Downscaling Methods Over the Qinghai-Tibet Plateau.” Canadian Journal of Fisheries and Aquatic Sciences 617:129014. https://doi.org/10.1016/j.jhydrol.2022.129014.
  • Song, P., Y. Zhang, J. Guo, J. Shi, T. Zhao, and B. Tong. 2022. “A 1km Daily Surface Soil Moisture Dataset of Enhanced Coverage Under All-Weather Conditions Over China in 2003–2019.” Earth System Science Data 14 (6): 2613-2637, 10.5194/essd-14-2613–2022. https://doi.org/10.5194/essd-14-2613-2022.
  • Sørensen, R., U. Zinko, and J. Seibert. 2006. “On the Calculation of the Topographic Wetness Index: Evaluation of Different Methods Based on Field Observations, Hydrol.” Earth System Science 10 (1): 101-112, 10.5194/hess-10-101–2006. https://doi.org/10.5194/hess-10-101-2006.
  • Sun, H., and Q. Xu: Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China, 10.3390/rs13142848, 2021.
  • Tavella, P., and A. Premoli. 1994. “Estimating the Instabilities of N Clocks by Measuring Differences of Their Readings.” Metrologia 30 (5): 479–486. https://doi.org/10.1088/0026-1394/30/5/003.
  • Taylor, C. M., A. Gounou, F. Guichard, P. P. Harris, R. J. Ellis, F. Couvreux, and M. De Kauwe. 2011. “Frequency of Sahelian Storm Initiation Enhanced Over Mesoscale Soil-Moisture Patterns.” Nature Geoscience 4:430–433. https://doi.org/10.1038/ngeo1173.
  • van der Vliet, M., R. van der Schalie, N. Rodriguez-Fernandez, A. Colliander, R. de Jeu, W. Preimesberger, T. Scanlon, and W. Dorigo. 2020. “Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records, 10.3390/rs12203439.”
  • Van Deventer, A. P., A. D. Ward, P. M. Gowda, and J. G. Lyon. 1997. “Using thematic mapper data to identify contrasting soil plains and tillage practices.” Photogrammetric Engineering and Remote Sensing 63:87–93.
  • Wang, L., and J. J. Qu. 2007. “NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing.” Geophysical Research Letters 34. https://doi.org/10.1029/2007GL031021.
  • Wang, J., and D. Xu. 2021. “Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction Over the Qinghai–Tibet Plateau, China.” China 13 (24): 5156. https://doi.org/10.3390/rs13245156.
  • Wan, Z., P. Wang, and X. Li. 2004. “Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index Products for Monitoring Drought in the Southern Great Plains, USA.” International Journal of Remote Sensing 25:61–72. https://doi.org/10.1080/0143116031000115328.
  • Wei, Z., Y. Meng, W. Zhang, J. Peng, and L. Meng. 2019. “Downscaling SMAP Soil Moisture Estimation with Gradient Boosting Decision Tree Regression Over the Tibetan Plateau.” Remote Sensing of Environment 225:30–44. https://doi.org/10.1016/j.rse.2019.02.022.
  • Wu, K., D. Ryu, L. Nie, and H. Shu. 2021. “Time-Variant Error Characterization of SMAP and ASCAT Soil Moisture Using Triple Collocation Analysis.” Remote Sensing of Environment 256:112324. https://doi.org/10.1016/j.rse.2021.112324.
  • Wu, S., T. Zhao, J. Pan, H. Xue, L. Zhao, and J. Shi. 2022. “Improvement in Modeling Soil Dielectric Properties During Freeze-Thaw Transitions.” IEEE Geoscience & Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2022.3154291.
  • Wu, Z., J. Zhou, H. He, Q. Lin, X. Wu, and Z. Xu. 2018. “An Advanced Error Correction Methodology for Merging in-Situ Observed and Model-Based Soil Moisture.” Canadian Journal of Fisheries and Aquatic Sciences 566:150–163. https://doi.org/10.1016/j.jhydrol.2018.09.018.
  • Yang, G., W. Sun, H. Shen, X. Meng, and J. Li. 2019. “An Integrated Method for Reconstructing Daily MODIS Land Surface Temperature Data.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 12:1026–1040. https://doi.org/10.1109/JSTARS.2019.2896455.
  • Yue, J., J. Tian, Q. Tian, K. Xu, and N. Xu. 2019. “Development of Soil Moisture Indices from Differences in Water Absorption Between Shortwave-Infrared Bands.” Isprs Journal of Photogrammetry & Remote Sensing 154:216–230. https://doi.org/10.1016/j.isprsjprs.2019.06.012.
  • Zarco-Tejada, P. J., C. A. Rueda, and S. L. Ustin. 2003. “Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods.” Remote Sensing of Environment 85:109–124. https://doi.org/10.1016/S0034-4257(02)00197-9.
  • Zeng, C., D. Long, H. Shen, P. Wu, Y. Cui, and Y. Hong. 2018. “A Two-Step Framework for Reconstructing Remotely Sensed Land Surface Temperatures Contaminated by Cloud.” Isprs Journal of Photogrammetry & Remote Sensing 141:30–45. https://doi.org/10.1016/j.isprsjprs.2018.04.005.
  • Zhang Ganlin, L. I. U. F. 2021. “Basic Soil Property Dataset of High-Resolution China Soil Information Grids (2010-2018).” National Tibetan Plateau/Third Pole Environment Data Center dataset. https://doi.org/10.11666/00073.ver1.db.
  • Zhang, N., Y. Hong, Q. Qin, and L. Liu. 2013. “VSDI: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing.” International Journal of Remote Sensing 34:4585–4609. https://doi.org/10.1080/01431161.2013.779046.
  • Zhang, Y., D. Kong, R. Gan, F. H. S. Chiew, T. R. McVicar, Q. Zhang, and Y. Yang. 2019. “Coupled Estimation of 500 m and 8-Day Resolution Global Evapotranspiration and Gross Primary Production in 2002–2017.” Remote Sensing of Environment 222:165–182. https://doi.org/10.1016/j.rse.2018.12.031.
  • Zhang, Y., S. Liang, Z. Zhu, H. Ma, and T. He. 2022. “Soil moisture content retrieval from Landsat 8 data using ensemble learning.” Isprs Journal of Photogrammetry & Remote Sensing 185:32–47. https://doi.org/10.1016/j.isprsjprs.2022.01.005.
  • Zhang, X., J. Li, Q. Qin, Y. Han, X. Zhang, L. Wang, and J. Guan. 2009. “Comparison and Application of Several Drought Monitoring Models in Ningxia, China.” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 25:18-23, 10.3969/j.issn.1002–6819.2009.08.004.
  • Zhang, L., Y. Liu, L. Ren, A. J. Teuling, X. Zhang, S. Jiang, X. Yang, L. Wei, F. Zhong, and L. Zheng. 2021. “Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology.” Science of the Total Environment 782:146602. https://doi.org/10.1016/j.scitotenv.2021.146602.
  • Zhao, H., J. Li, Q. Yuan, L. Lin, L. Yue, and H. Xu. 2022. “Downscaling of Soil Moisture Products Using Deep Learning: Comparison and Analysis on Tibetan Plateau.” Canadian Journal of Fisheries and Aquatic Sciences 607:127570. https://doi.org/10.1016/j.jhydrol.2022.127570.
  • Zhao, W., N. Sánchez, H. Lu, and A. Li. 2018. “A Spatial Downscaling Approach for the SMAP Passive Surface Soil Moisture Product Using Random Forest Regression.” Canadian Journal of Fisheries and Aquatic Sciences 563:1009–1024. https://doi.org/10.1016/j.jhydrol.2018.06.081.
  • Zhou, J., W. T. Crow, Z. Wu, J. Dong, H. He, and H. Feng. 2021. “A Triple Collocation-Based 2D Soil Moisture Merging Methodology Considering Spatial and Temporal Non-Stationary Errors.” Remote Sensing of Environment 263:112509. https://doi.org/10.1016/j.rse.2021.112509.