A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Spatio Temporal

<p><strong>Abstract</strong>:</p><p>Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002–2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25° resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R<sup>2</sup> larger than 0.65, and RMSE less than 0.08 cm<sup>3</sup> cm<sup>-3</sup> and Bias less than 0.07 cm<sup>3</sup> cm<sup>-3 </sup>at 0.25° and 1° spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial and temporal characteristics and trend of SM over TP were analyzed based on this dataset.</p><p><strong>Keywords: </strong>Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau</p>

scholarly journals A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Hongjie Xie ◽  
Wei Wan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
Sufficient Information ◽  
The Tibetan Plateau ◽  
Significant Information ◽  
Spatio Temporal ◽  
General Regression

Abstract Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.

scholarly journals A Study on the Assessment of Multi-Source Satellite Soil Moisture Products and Reanalysis Data for the Tibetan Plateau

2019 ◽  
Vol 11 (10) ◽  
pp. 1196 ◽  
Author(s):  
Meilin Cheng ◽  
Lei Zhong ◽  
Yaoming Ma ◽  
Mijun Zou ◽  
Nan Ge ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
European Space Agency ◽  
Reanalysis Data ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Chinese Academy Of Sciences ◽  
Data Continuity ◽  
Academy Of Sciences

Soil moisture is a key variable in the process of land–atmosphere energy and water exchange. Currently, there are a large number of operational satellite-derived soil moisture products and reanalysis soil moisture products available. However, due to the lack of in situ soil moisture measurements over the Tibetan Plateau (TP), their accuracy and applicability are unclear. Based on the in situ measurements of the soil moisture observing networks established at Maqu, Naqu, Ali, and Shiquanhe (Sq) by the Institute of Tibetan Plateau Research, the Chinese Academy of Sciences, the Northwest Institute of Eco-Environmental Resources, the Chinese Academy of Sciences and the University of Twente over the TP, the accuracy and reliability of the European Space Agency Climate Change Initiative Soil Moisture version 4.4 (ESA CCI SM v4.4) soil moisture products and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) soil moisture product were evaluated. The spatiotemporal distributions and interannual variations of the soil moisture were analyzed. Further, the climatological soil moisture changing trends across the TP were explored. The results show that with regard to the whole plateau, the combined product performs the best (unbiased root-mean-square error (ubRMSE) = 0.043 m3/m3, R = 0.66), followed by the active product (ubRMSE = 0.048 m3/m3, R = 0.62), the passive product (ubRMSE = 0.06 m3/m3, R = 0.61), and the ERA5 soil moisture product (ubRMSE = 0.067 m3/m3, R = 0.52). Considering the good spatiotemporal data continuity of the ERA5 soil moisture product, the ERA5 soil moisture data from 1979 to 2018 were used to analyze the climatological soil moisture changing trend for the entire TP surface. It was found that there was an increasing trend of soil moisture across the TP, which was consistent with the overall trends of increasing precipitation and decreasing evaporation. Moreover, the shrinkage of the cryosphere in conjunction with the background TP warming presumably contribute to soil moisture change.

scholarly journals Uncertainty and Variation of Remotely Sensed Lake Ice Phenology across the Tibetan Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 1534 ◽  
Author(s):  
Linan Guo ◽  
Yanhong Wu ◽  
Hongxing Zheng ◽  
Bing Zhang ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Regional Climate ◽  
Local Environment ◽  
Remotely Sensed ◽  
The Tibetan Plateau ◽  
Remotely Sensed Data ◽  
Lake Ice ◽  
Reflectance Data ◽  
Ice Phenology

In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice.

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

scholarly journals Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

2020 ◽  
Vol 12 (3) ◽  
pp. 509 ◽  
Author(s):  
Ruodan Zhuang ◽  
Yijian Zeng ◽  
Salvatore Manfreda ◽  
Zhongbo Su
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
Root Zone ◽  
Cumulative Distribution ◽  
Analytical Relationship ◽  
The Tibetan Plateau ◽  
Climate Systems

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations

2015 ◽  
Vol 163 ◽  
pp. 91-110 ◽  
Author(s):  
Jiangyuan Zeng ◽  
Zhen Li ◽  
Quan Chen ◽  
Haiyun Bi ◽  
Jianxiu Qiu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Remotely Sensed ◽  
The Tibetan Plateau ◽  
In Situ Observations

Spatio-temporal variability of soil moisture and its effect on vegetation in a desertified aeolian riparian ecotone on the Tibetan Plateau, China

2013 ◽  
Vol 479 ◽  
pp. 215-225 ◽  
Author(s):  
Haidong Li ◽  
Weishou Shen ◽  
Changxin Zou ◽  
Jiang Jiang ◽  
Lina Fu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Temporal Variability ◽  
The Tibetan Plateau ◽  
Spatio Temporal

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

2013 ◽  
Vol 56 (12) ◽  
pp. 2173-2185 ◽  
Author(s):  
Qiang Liu ◽  
JinYang Du ◽  
JianCheng Shi ◽  
LingMei Jiang
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Tibetan Plateau ◽  
Remotely Sensed ◽  
The Tibetan Plateau

scholarly journals Some practical notes on the land surface modeling in the Tibetan Plateau

2009 ◽  
Vol 13 (5) ◽  
pp. 687-701 ◽  
Author(s):  
K. Yang ◽  
Y.-Y. Chen ◽  
J. Qin
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Soil Water ◽  
Land Surface ◽  
Heat Fluxes ◽  
The Tibetan Plateau ◽  
Sensible Heat ◽  
Central Plateau ◽  
Alpine Meadows ◽  
Common Land Model

Abstract. The Tibetan Plateau is a key region of land-atmosphere interactions, as it provides an elevated heat source to the middle-troposphere. The Plateau surfaces are typically characterized by alpine meadows and grasslands in the central and eastern part while by alpine deserts in the western part. This study evaluates performance of three state-of-the-art land surface models (LSMs) for the Plateau typical land surfaces. The LSMs of interest are SiB2 (the Simple Biosphere), CoLM (Common Land Model), and Noah. They are run at typical alpine meadow sites in the central Plateau and typical alpine desert sites in the western Plateau. The identified key processes and modeling issues are as follows. First, soil stratification is a typical phenomenon beneath the alpine meadows, with dense roots and soil organic matters within the topsoil, and it controls the profile of soil moisture in the central and eastern Plateau; all models, when using default parameters, significantly under-estimate the soil moisture within the topsoil. Second, a soil surface resistance controls the surface evaporation from the alpine deserts but it has not been reasonably modeled in LSMs; an advanced scheme for soil water flow is implemented in a LSM, based on which the soil resistance is determined from soil water content and meteorological conditions. Third, an excess resistance controls sensible heat fluxes from dry bare-soil or sparsely vegetated surfaces, and all LSMs significantly under-predict the ground-air temperature gradient, which would result in higher net radiation, lower soil heat fluxes and thus higher sensible heat fluxes in the models. A parameterization scheme for this resistance has been shown to be effective to remove these biases.

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