Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2613-2022
© 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-14-2613-2022
© 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
| 
08 Jun 2022
Data description paper |
| 08 Jun 2022
| 08 Jun 2022
A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019
Peilin Song
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
now at: School of Electronic Science and Engineering, Xi'an
Jiaotong University, Xi'an, 710049, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing
100190, China
Tianjie Zhao
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
Bing Tong
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
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Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
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Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2471, https://doi.org/10.5194/egusphere-2025-2471, 2025
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By analyzing global CloudSat data, we identified that most liquid cloud profiles have triangle-shaped or steadily decreasing structures, and we developed a new method using pattern recognition, fitting techniques, and machine learning to accurately estimate these profiles. This research advances our understanding of cloud life cycle and improves the ability to characterize cloud profiles, which is crucial for enhancing weather forecast and climate change research.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Rongkang Yang, Peng Zhang, Jianping Guo, Jincheng Wang, Die Xiao, Jianping Du, Zhongliang Sun, Shijun Liu, Sijie Chen, and Anfan Huang
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Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
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The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
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The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
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Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
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Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
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A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
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The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
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Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
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This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
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This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
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This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
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To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
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The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
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This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
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Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
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Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
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This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo
Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, https://doi.org/10.5194/essd-13-3239-2021, 2021
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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.
Yuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
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This study developed an analytical ecohydrological model that considers three aspects of vegetation response to eCO2 (i.e., stomatal response, LAI response, and rooting depth response) to detect the impact of eCO2 on continental runoff over the past 3 decades globally. Our findings suggest a minor role of eCO2 on the global runoff changes, yet highlight the negative runoff–eCO2 response in semiarid and arid regions which may further threaten the limited water resource there.
Mengmeng Cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, and Zijin Yuan
Earth Syst. Sci. Data, 13, 2111–2134, https://doi.org/10.5194/essd-13-2111-2021, https://doi.org/10.5194/essd-13-2111-2021, 2021
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We constructed a temperature depth and observation time correction model to eliminate the sampling depth and temporal differences among different data. Then, we proposed a reconstructed spatial model that filters and removes missing pixels and low-quality pixels contaminated by clouds from raw SST images and retrieves real sea surface temperatures under cloud coverage based on multisource data to generate a high-quality unified global SST product with long-term spatiotemporal continuity.
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021, https://doi.org/10.5194/acp-21-6199-2021, 2021
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A convective cloud identification process is developed using geostationary satellite data from Himawari-8.
Convective cloud fraction is generally larger before noon and smaller in the afternoon under polluted conditions, but megacities and complex topography can influence the pattern.
A robust relationship between convective cloud and aerosol loading is found. This pattern varies with terrain height and is modulated by varying thermodynamic, dynamical, and humidity conditions during the day.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
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Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Cited articles
Albergel, C., de Rosnay, P., Gruhier, C., Munoz-Sabater, J., 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.
Brunsdon, C., Fotheringham, A. S., and Charlton, M. E.: Geographically
weighted regression: A method for exploring spatial nonstationarity, Geogr.
Anal., 28, 281–298, 1996.
Busch, F. A., Niemann, J. D., and Coleman, M.: Evaluation of an empirical
orthogonal function-based method to downscale soil moisture patterns based
on topographical attributes, Hydrol. Process., 26, 2696–2709, 2012.
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. Rev., 9,
161–173, 1994.
Champagne, C., McNairn, H., and Berg, A. A.: Monitoring agricultural soil
moisture extremes in Canada using passive microwave remote sensing, Remote
Sens. Environ., 115, 2434–2444, 2011.
Chauhan, N. S., Miller, S., and Ardanuy, P.: Spaceborne soil moisture
estimation at high resolution: a microwave-optical/IR synergistic approach,
Int. J. Remote Sens., 24, 4599–4622, https://doi.org/10.1080/0143116031000156837, 2003.
Chen, Y., Yuan, H., Yang, Y., and Sun, R.: Sub-daily soil moisture estimate
using dynamic Bayesian model averaging, J. Hydrol., 590, 125445, https://doi.org/10.1016/j.jhydrol.2020.125445, 2020.
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.
Das, N., Entekhabi, D., Dunbar, R. S., Kim, S., Yueh, S., Colliander, A., O'Neill, P. E., Jackson, T., Jagdhuber, T., Chen, F., Crow, W. T., Walke, J., Berg, A., Bosch, D., Caldwell, T., and Cosh, M.: SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km
EASE-Grid Soil Moisture, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ASB0EQO2LYJV, 2020.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A., Yueh, S., Jagdhuber, T., Chen, F., Crow, W., O'Neill, P. E., Walker, J. P., Berg, A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H., Lopez-Baeza, E., and Thibeault, M.: The SMAP and Copernicus Sentinel 1A/B
microwave active-passive high resolution surface soil moisture product,
Remote Sens. Environ., 233, 111380, https://doi.org/10.1016/j.rse.2019.111380, 2019.
den Besten, N., Steele-Dunne, S., de Jeu, R., and van der Zaag, P.: Towards
Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated
Agriculture, Remote Sens., 13, 2929, https://doi.org/10.3390/rs13152929, 2021.
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.
Dowling, T. P. F., Song, P., Jong, M. C. D., Merbold, L., Wooster, M. J.,
Huang, J., and Zhang, Y.: An Improved Cloud Gap-Filling Method for Longwave
Infrared Land Surface Temperatures through Introducing Passive Microwave
Techniques, Remote Sens., 13, 3522, https://doi.org/10.3390/rs13173522, 2021.
Du, J. Y., Kimball, J. S., and Jones, L. A.: Passive microwave remote
sensing of soil moisture based on dynamic vegetation scattering properties
for AMSR-E, IEEE T. Geosci. Remote, 54, 597–608, 2016.
Duan, S. and Li, Z.: Spatial Downscaling of MODIS Land Surface Temperatures
Using Geographically Weighted Regression: Case Study in Northern China, IEEE
T. Geosci. Remote, 54, 6458–6469, https://doi.org/10.1109/TGRS.2016.2585198, 2016.
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance
Metrics for Soil Moisture Retrievals and Application Requirements, J.
Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010jhm1223.1, 2010a.
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, 2010b.
Entekhabi, D., Das, N., Kim, S., Jagdhuber, T., Piles, M., Yueh, S., Colliander, A., Lopez-baeza, E., and Martínez-Fernández, J.: High-Resolution Enhanced Product based on SMAP Active-Passive Approach and Sentinel 1A Radar Data, AGU Fall Meeting Abstracts, San Francisco, USA, https://ui.adsabs.harvard.edu/abs/2016AGUFM.H24C..08E/abstract (last access: 2 April 2021), 2016.
Fang, B. and Lakshmi, V.: Passive Microwave Soil Moisture Downscaling Using
Vegetation and Surface Temperatures, Vadose Zone J., 12, 1712–1717, 2013.
Fang, B., Lakshmi, V., Bindlish, R., Jackson, T. J., Cosh, M., and Basara,
J.: Passive Microwave Soil Moisture Downscaling Using Vegetation Index and
Skin Surface Temperature, Vadose Zone J., 12, 1712–1717, 2013.
Fang, B., Lakshmi, V., Bindlish, R., and Jackson, T.: AMSR2 Soil Moisture
Downscaling Using Temperature and Vegetation Data, Remote Sens., 10, 1575, https://doi.org/10.3390/rs10101575, 2018.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Fujii, H., Koike, T., and Imaoka, K.: Improvement of the AMSR-E Algorithm
for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage
Dataset Derived from MODIS Data, Journal of the Remote Sensing Society of
Japan, 29, 282–292, 2009.
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, 1–19, https://doi.org/10.1007/s12665-016-5917-6,
2016.
Ines, A. V. M., Das, N. N., Hansen, J. W., and Njoku, E. G.: Assimilation of
remotely sensed soil moisture and vegetation with a crop simulation model
for maize yield prediction, Remote Sens. Environ., 138, 149–164,
https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Jiménez, C., Prigent, C., Ermida, S. L., and Moncet, J. L.: Inversion of
AMSR-E observations for land surface temperature estimation: 1. Methodology
and evaluation with station temperature, J. Geophys. Res.-Atmos., 122, 3330–3347, https://doi.org/10.1002/2016jd026144, 2017.
Jing, Z. and Zhang, X.: A soil moisture assimilation scheme using
satellite-retrieved skin temperature in meso-scale weather forecast model,
Atmos. Res., 95, 333–352, 2010.
Jones, L. A., Kimball, J. S., Podest, E., McDonald, K. C., Chan, S. K., and
Njoku, E. G.: A method for deriving land surface moisture, vegetation
optical depth, and open water fraction from AMSR-E, IEEE IGARSS 2009, Cape
Town, South Africa, 2009, III-916-III-919, https://doi.org/10.1109/IGARSS.2009.5417921,
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A. J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land
evapotranspiration trend due to limited moisture supply, Nature, 467,
951–954, https://doi.org/10.1038/nature09396, 2010.
Kim, J. and Hogue, T. S.: Improving spatial soil moisture representation
through integration of AMSR-E and MODIS products, IEEE T. Geosci. Remote, 50, 446–460, https://doi.org/10.1109/TGRS.2011.2161318,
2012.
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 (written in
Japanese), Annual Journal of Hydraulic Engineering, 48, 217–222 2004.
Komatsu, T. S.: Toward a Robust Phenomenological Expression of Evaporation
Efficiency for Unsaturated Soil Surfaces, J. Appl. Meteorol.,
42, 1330–1334, https://doi.org/10.1175/1520-0450(2003)042<1330:Tarpeo>2.0.Co;2, 2003.
Kong, D., Zhang, Y., Gu, X., and Wang, D.: A robust method for
reconstructing global MODIS EVI time series on the Google Earth Engine,
Isprs J. Photogramm., 155, 13–24, 2019.
Koster, R. D., Mahanama, S., Livneh, B., Lettenmaier, D. P., and Reichle, R.
H.: Skill in streamflow forecasts derived from large-scale estimates of soil
moisture and snow, Nat. Geosci., 3, 613–616, 2010.
Liu, S., Li, X., Che, T., Xu, Z., Zhang, Y., and Tan, J.: Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Yakou station, 2018), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270781, 2019.
Liu, S., Xiao, Q., Xu, Z., and Bai, J.: Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2018). National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.271094, 2021.
Ma, Y.: A long-term dataset of integrated land-atmosphere interaction observations on the Tibetan Plateau (2005–2016), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270910, 2020.
Malbéteau, Y., Merlin, O., Molero, B., Rüdiger, C., and Bacon, S.:
DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture
using localized in situ measurements: Application to SMOS and AMSR-E data in
Southeastern Australia, Int. J. Appl. Earth Obs., 45, 221–234, https://doi.org/10.1016/j.jag.2015.10.002, 2016.
Meesters, A. G. C. A., De Jeu, R. A. M., and Owe, M.: Analytical derivation
of the vegetation optical depth from the microwave polarization difference
index, IEEE Geosci. Remote Sens. Lett., 2, 121–123, 2005.
Mendoza, P. A., Mizukami, N., Ikeda, K., Clark, M. P., Gutmann, E. D., Arnold, J. R., Brekke, L. D., and Rajagopalan, B.: Effects of different regional climate
model resolution and forcing scales on projected hydrologic changes, J.
Hydrol., 541, 1003–1019, https://doi.org/10.1016/j.jhydrol.2016.08.010, 2016.
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.
Merlin, O., Chehbouni, A. G., Kerr, Y. H., Njoku, E. G., and Entekhabi, D.:
A combined modeling and multipectral/multiresolution remote sensing approach
for disaggregation of surface soil moisture: Application to SMOS
configuration, IEEE T. Geosci. Remote, 43, 2036–2050, https://doi.org/10.1109/TGRS.2005.853192, 2005.
Merlin, O., Walker, J. P., Chehbouni, A., and Kerr, Y.: Towards
deterministic downscaling of SMOS soil moisture using MODIS derived soil
evaporative efficiency, Remote Sens. Environ., 112, 3935–3946, https://doi.org/10.1016/j.rse.2008.06.012, 2008.
Merlin, O., Al Bitar, A., Walker, J. P., and Kerr, Y.: An improved algorithm
for disaggregating microwave-derived soil moisture based on red,
near-infrared and thermal-infrared data, Remote Sens. Environ., 114,
2305–2316, https://doi.org/10.1016/j.rse.2010.05.007, 2010.
Merlin, O., Escorihuela, M. J., Mayoral, M. A., Hagolle, O., Al Bitar, A.,
and Kerr, Y.: Self-calibrated evaporation-based disaggregation of SMOS soil
moisture: An evaluation study at 3 km and 100 m resolution in Catalunya,
Spain, Remote Sens. Environ., 130, 25–38, https://doi.org/10.1016/j.rse.2012.11.008, 2013.
Merlin, O., Malbeteau, Y., Notfi, Y., Bacon, S., Er-Raki, S., Khabba, S.,
and Jarlan, L.: Performance Metrics for Soil Moisture Downscaling Methods:
Application to DISPATCH Data in Central Morocco, Remote Sens., 7, 3783–3807,
https://doi.org/10.3390/rs70403783, 2015.
Molero, B., Merlin, O., Malbéteau, Y., Al Bitar, A., Cabot, F., Stefan, V., 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.
Montaldo, N., Albertson, J. D., Mancini, M., and Kiely, G.: Robust
simulation of root zone soil moisture with assimilation of surface soil
moisture data, Water Resour. Res., 37, 2889–2900, https://doi.org/10.1029/2000WR000209, 2001.
O'Neill, P. E., Bindlish, R., Chan, S., Chaubell, J., Colliander, A., Njoku,
E., and Jackson, T.: SMAP Algorithm Theoretical Basis Document: Level 2 &
3 Soil Moisture (Passive) Data Products, Revision G., Jet Propulsion
Laboratory, Pasadena, CA, https://nsidc.org/sites/nsidc.org/files/technical-references/L2_SM_P_ATBD_rev_G_final_Oct2021.pdf (last access: 5 May 2021), 2021a.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell, J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 8, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021b.
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture
and vegetation optical depth retrieval using the microwave polarization
difference index, IEEE T. Geosci. Remote, 39, 1643–1654, 2001.
Pan, H., Chen, Z., Wit, A. D., and Ren, J.: Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation, Sensors, 19, 3161, https://doi.org/10.3390/s19143161, 2019.
Peng, J., Loew, A., Zhang, S. Q., Wang, J., and Niesel, J.: Spatial
downscaling of satellite soil moisture data using a vegetation temperature
condition index, IEEE T. Geosci. Remote, 54, 558–566, https://doi.org/10.1109/TGRS.2015.2462074, 2016.
Piles, M., Entekhabi, D., and Camps, A.: A Change Detection Algorithm for
Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer
Observations, IEEE T. Geosci. Remote, 47, 4125–4131,
https://doi.org/10.1109/TGRS.2009.2022088, 2009.
Sabaghy, S., Walker, J. P., Renzullo, L. J., Akbar, R., Chan, S., Chaubell, J., Das, N., Dunbar, R. S., Entekhabi, D., Gevaert, A., Jackson, T. J., Loew, A., Merlin, O., Moghaddam, M., Peng, J., Peng, J., Piepmeier, J., Rüdiger, C., Stefan, V., Wu, X., Ye, N., and Yueh, S.: Comprehensive analysis of alternative downscaled soil
moisture products, Remote Sens. Environ., 239, 111586, https://doi.org/10.1016/j.rse.2019.111586, 2020.
Sanchez-Ruiz, S., Piles, M., Sanchez, N., Martinez-Fernandez, J.,
Vall-Ilossera, M., and Camps, A.: Combining SMOS with visible and
near/shortwave/thermal infrared satellite data for high resolution soil
moisture estimates, J. Hydrol., 516, 273–283, https://doi.org/10.1016/j.jhydrol.2013.12.047,
2014.
Scaini, A., Sanchez, N., Vicente-Serrano, S. M., and Martinez-Fernandez, J.:
SMOS-derived soil moisture anomalies and drought indices: a comparative
analysis using in situ measurements, Hydrol. Process., 29, 373–383,
https://doi.org/10.1002/hyp.10150, 2015.
Song, P. and Zhang, Y.: An improved non-linear inter-calibration method on
different radiometers for enhancing coverage of daily LST estimates in low
latitudes, Remote Sens. Environ., 264, 112626, https://doi.org/10.1016/j.rse.2021.112626, 2021a.
Song, P. and Zhang, Y.: Daily all weather surface soil moisture data set
with 1 km resolution in China (2003–2019), National Tibetan Plateau Data
Center [data set], https://doi.org/10.11888/Hydro.tpdc.271762, 2021b.
Song, P., Mansaray, L. R., Huang, J., and Huang, W.: Mapping paddy rice
agriculture over China using AMSR-E time series data, Isprs J. Photogramm.,
144, 469–482, https://doi.org/10.1016/j.isprsjprs.2018.08.015, 2018.
Song, P., Huang, J., and Mansaray, L. R.: An improved surface soil moisture
downscaling approach over cloudy areas based on geographically weighted
regression, Agr. Forest Meteorol., 275, 146–158,
https://doi.org/10.1016/j.agrformet.2019.05.022, 2019a.
Song, P., Huang, J., Mansaray, L. R., Wen, H., Wu, H., Liu, Z., and Wang, X.: An Improved Soil Moisture Retrieval Algorithm Based on the Land Parameter Retrieval Model for Water-
Land Mixed Pixels Using AMSR-E Data, IEEE T. Geosci. Remote, 57, 7643–7657, https://doi.org/10.1109/TGRS.2019.2915346,
2019b.
Song, P., Zhang, Y., and Tian, J.: Improving Surface Soil Moisture Estimates
in Humid Regions by an Enhanced Remote Sensing Technique, Geophys. Res. Lett.,
48, e2020GL091459, https://doi.org/10.1029/2020GL091459, 2021.
Stefan, V. G., Merlin, O., Escorihuela, M.-J., Molero, B., and Er-Raki, S.:
Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method
of SMOS Soil Moisture Data, Remote Sens., 12, 1671, https://doi.org/10.3390/rs12101671, 2020.
Sui, D. Z.: Tobler's First Law of Geography: A Big Idea for a Small World?,
Ann. Assoc. Am. Geogr., 94, 269–277, https://doi.org/10.1111/j.1467-8306.2004.09402003.x, 2004.
Ulaby, F. T. and Wilson, E. A.: Microwave Attenuation Properties of
Vegetation Canopies, IEEE T. Geosci. Remote, GE-23, 746–753,
https://doi.org/10.1109/TGRS.1985.289393, 1985.
Vergopolan, N., Xiong, S., Estes, L., Wanders, N., Chaney, N. W., Wood, E. F., Konar, M., Caylor, K., Beck, H. E., Gatti, N., Evans, T., and Sheffield, J.: Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields, Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, 2021.
Verstraeten, W. W., Veroustraete, F., van der Sande, C. J., Grootaers, I.,
and Feyen, J.: Soil moisture retrieval using thermal inertia, determined
with visible and thermal spaceborne data, validated for European forests,
Remote Sens. Environ., 101, 299–314, 2006.
Walker, J. P. and Houser, P. R.: A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations, J. Geophys. Res.-Atmos., 106, 11761–11774, https://doi.org/10.1029/2001jd900149, 2001.
Wang, K. and Liang, S.: Evaluation of ASTER and MODIS land surface
temperature and emissivity products using long-term surface longwave
radiation observations at SURFRAD sites, Remote Sens. Environ., 113,
1556–1565, https://doi.org/10.1016/j.rse.2009.03.009, 2009.
Wang, L. and Qu, J. J.: NMDI: A normalized multi-band drought index for
monitoring soil and vegetation moisture with satellite remote sensing,
Geophys. Res. Lett., 34, L20405, https://doi.org/10.1029/2007GL031021, 2007.
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, 2019.
Wu, D., Liang, H., Cao, T., Yang, D., Zhou, W., and Wu, X.: Construction of
operation monitoring system of automatic soil moisture observation network
in China, Meteorol. Sci. Technol., 42, 278–282, 2014
Yang, G., Sun, W. W., Shen, H. F., Meng, X. C., and Li, J. L.: An Integrated
Method for Reconstructing Daily MODIS Land Surface Temperature Data, IEEE J.
Sel. Top. Appl. Earth Observ. Remote Sens., 12, 1026–1040, 2019.
Yao, P., Lu, H., Shi, J., Zhao, T., Yang, K., Cosh, M. H., Gianotti, D. J. S., and Entekhabi, D.: A long term global daily soil moisture dataset derived from AMSR-E and
AMSR2 (2002–2019), Sci. Data, 8, 143, https://doi.org/10.1038/s41597-021-00925-8,
2021.
Zeng, Y., Feng, Z., and Xiang, N.: Assessment of soil moisture using Landsat
ETM+ temperature/vegetation index in semiarid environment, IEEE
International Geoscience & Remote Sensing Symposium, Piscataway NJ, 4306,
4306–4309, https://doi.org/10.1109/IGARSS.2004.1370089, 2004.
Zhang, J., Zhou, Z., Yao, F., Yang, L., and Hao, C.: Validating the Modified
Perpendicular Drought Index in the North China Region Using In Situ Soil
Moisture Measurement, IEEE Geosci. Remote Sens. Lett., 12,
542–546, 2014.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., Mcvicar, T. R., Zhang, Q.,
and Yang, Y.: Coupled estimation of 500 m and 8-day resolution global
evapotranspiration and gross primary production in 2002–2017, Remote Sens.
Environ., 222, 165–182, 2019.
Zhang, Y. Q., Chiew, F. H. S., Liu, C. M., Tang, Q. H., Xia, J., Tian, J., Kong, D. D., and Li, C. C.: Can Remotely Sensed Actual Evapotranspiration Facilitate
Hydrological Prediction in Ungauged Regions Without Runoff Calibration?,
Water Resour. Res., 56, 2020.
Zheng, J. Y., Lu, H. S., Crow, W. T., Zhao, T. J., Merlin, O., Rodriguez-Fernandez, N., Shi, J. C., Zhu, Y. H., Su, J. B., Chuen, S. A. K., Wang, X. Y., and Gou, Q. Q.: Soil moisture downscaling using
multiple modes of the DISPATCH algorithm in a semi-humid/humid region, Int. J.
Appl. Earth Obs., 104, 102530, https://doi.org/10.1016/j.jag.2021.102530, 2021.
Zhou, S., Williams, A. P., Lintner, B., Berg, A. M., and Gentine, P.: Soil
moisture–atmosphere feedbacks mitigate declining water availability in
drylands, Nat. Clim. Change, 11, 38–44, 2021.
Zhu, Z. and Shi, C.: Simulation and Evaluation of CLDAS and GLDAS Soil
Moisture Data in China (written in Chinese), Sci. Technol.
Eng., 32, 138–144, 2014.
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Soil moisture information is crucial for understanding the earth surface, but currently...
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