The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products

Abstract. A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite and model products of soil moisture and soil temperature. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) consists of three regional scale in-situ reference networks, including the Naqu network in a cold semiarid climate, the Maqu network in a cold humid climate and the Ngari network in a cold arid climate. These networks provide a representative coverage of the different climate and land surface hydrometeorological conditions on the Tibetan plateau. In this paper the details of the Tibet-Obs are reported. To demonstrate the uniqueness of the Tibet-Obs in quantifying and explaining soil moisture uncertainties in existing coarse satellite products, an analysis is carried out to assess the reliability of several satellite products for the Naqu and the Maqu network areas. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

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A plateau scale soil moisture and soil temperature observatory for quantifying uncertainties in coarse resolution satellite products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-243-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 243-276 ◽

Cited By ~ 1

Author(s):

Z. Su ◽

J. Wen ◽

L. Dente ◽

R. van der Velde ◽

L. Wang ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Soil Temperature ◽

In Situ Measurements ◽

Winter Period ◽

The Tibetan Plateau ◽

Monsoon Period ◽

Coarse Resolution ◽

Mean And Variance

Abstract. A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite products of soil moisture and soil temperature. The observatory consists of three regional networks across the Tibetan Plateau and provides reliable measurements of mean and variance in soil moisture and soil temperature of representative areas comparable in size to coarse satellite footprints. Using these in-situ measurements, a analysis is carried out to assess the reliability of several satellite products derived from AMSR-E and ASCAT data by three retrieval algorithms (henceforth the AMSR-E products, the ASCAT-L2 products and the ITC-model retrievals) for the first time. For the cold semiarid Naqu area, AMSR-E and ASCAT-L2 products overestimate significantly the regional soil moisture in the monsoon seasons. The ITC-model retrievals are closer to the in-situ measurements but the dynamics in the retrieved time series needs further investigation. The use of these datasets is therefore not recommended for cold semiarid conditions on the Tibetan Plateau. For the cold humid Maqu network area AMSR-E and ASCAT-L2 products have comparable accuracy as reported by previous studies in the humid monsoon period. AMSR-E products significantly overestimate and ASCAT-L2 products underestimate the soil moisture in the winter period. The ITC-model retrievals underestimate the soil moisture in general. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

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

Remote Sensing ◽

10.3390/rs12030509 ◽

2020 ◽

Vol 12 (3) ◽

pp. 509 ◽

Cited By ~ 6

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.

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Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009–2019) surface soil moisture dataset

10.5194/essd-2020-209 ◽

2020 ◽

Author(s):

Pei Zhang ◽

Donghai Zheng ◽

Rogier van der Velde ◽

Jun Wen ◽

Yijian Zeng ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Current Model ◽

Regional Scale ◽

In Situ Measurements ◽

The Tibetan Plateau ◽

Model Based ◽

Network Area

Abstract. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) was established ten years ago, which has been widely used to calibrate/validate satellite- and model-based soil moisture (SM) products for their applications to the Tibetan Plateau (TP). This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface SM dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs that consists of three regional-scale SM monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected by multiple SM monitoring sites of the three networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks. Comparisons between four spatial upscaling methods, i.e. arithmetic averaging, Voronoi diagram, time stability and apparent thermal inertia, show that the arithmetic average of the monitoring sites with long-term (i.e. ≥ six years) continuous measurements are found to be most suitable to produce the upscaled SM records. Trend analysis of the 10-year upscaled SM records using the Mann-Kendall method shows that the Maqu network area in the eastern part of the TP is drying while the Shiquanhe network area in the west is getting wet that generally follow the change of precipitation. To further demonstrate the uniqueness of the upscaled SM records in validating existing SM products for long term period (~ 10 years), comparisons are conducted to evaluate the reliability of three reanalysis datasets for the Maqu and Shiquanhe network areas. It is found that current model-based SM products still show deficiencies in representing the trend and variation of measured SM dynamics in the Tibetan grassland (i.e. Maqu) and desert ecosystems (i.e. Shiquanhe) that dominate the landscape of the TP. The dataset would be also valuable for calibrating/validating long-term satellite-based SM products, evaluation of SM upscaling methods, development of data fusion methods, and quantifying the coupling strength between precipitation and SM at 10-year scale. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/uuid:21220b23-ff36-4ca9-a08f-ccd53782e834 (Zhang et al., 2020).

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Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009–2019) surface soil moisture dataset

Earth System Science Data ◽

10.5194/essd-13-3075-2021 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3075-3102

Author(s):

Pei Zhang ◽

Donghai Zheng ◽

Rogier van der Velde ◽

Jun Wen ◽

Yijian Zeng ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Current Model ◽

Regional Scale ◽

In Situ Measurements ◽

The Tibetan Plateau ◽

Monitoring Networks ◽

Model Based

Abstract. The Tibetan Plateau observatory (Tibet-Obs) of plateau scale soil moisture and soil temperature was established 10 years ago and has been widely used to calibrate/validate satellite- and model-based soil moisture (SM) products for their applications to the Tibetan Plateau (TP). This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface SM dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs that consists of three regional-scale SM monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15 min in situ measurements collected by multiple SM monitoring sites of the three networks and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks. Comparisons between four spatial upscaling methods – i.e. arithmetic averaging, Voronoi diagrams, time stability, and apparent thermal inertia – show that the arithmetic average of the monitoring sites with long-term (i.e. ≥ 6-year) continuous measurements is found to be most suitable to produce the upscaled SM records. Trend analysis of the 10-year upscaled SM records indicates that the Shiquanhe network in the western part of the TP is getting wet, while there is no significant trend found for the Maqu network in the east. To further demonstrate the uniqueness of the upscaled SM records in validating existing SM products for a long-term period (∼10 years), the reliability of three reanalysis datasets is evaluated for the Maqu and Shiquanhe networks. It is found that current model-based SM products still show deficiencies in representing the measured SM dynamics in the Tibetan grassland (i.e. Maqu) and desert ecosystems (i.e. Shiquanhe). The dataset would also be valuable for calibrating/validating long-term satellite-based SM products, evaluation of SM upscaling methods, development of data fusion methods, and quantifying the coupling of SM and precipitation at a 10-year scale. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/12763700.v7 (Zhang et al., 2020).

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The evaluation of AMSR-E soil moisture data in atmospheric modeling using a suitable time series iteration to derive land surface fluxes over the Tibetan Plateau

10.5194/egusphere-egu2020-2546 ◽

2020 ◽

Author(s):

Weiqiang Ma ◽

Yaoming Ma ◽

Yizhe Han ◽

Wei Hu ◽

Lei Zhong

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Surface Fluxes ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Default Model ◽

Model Configuration

Firstly, based on the difference of model and in-situ observations, a serious of sensitive experiments were done by using WRF. In order to use remote sensing products, a land-atmosphere model was initialized by ingesting AMSR-E RS products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations.Secondly, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in-situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.All of the different methods will clarify the land surface heating field in complex plateau, it also can affect atmospheric cycle over the Tibetan Plateau even all of the global atmospheric cycle pattern.

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

Remote Sensing ◽

10.3390/rs12030455 ◽

2020 ◽

Vol 12 (3) ◽

pp. 455 ◽

Cited By ~ 5

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.

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

10.5194/egusphere-egu2020-20846 ◽

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

Abstract: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 &#8220;The third pole of the world&#8221; and &#8220;Asia&#8217;s water towers&#8221;, 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&#8211;2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25&#176; 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 R2 larger than 0.65, and RMSE less than 0.08 cm3 cm-3 and Bias less than 0.07 cm3 cm-3 at 0.25&#176; and 1&#176; spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial&#160;and&#160;temporal&#160;characteristics and trend of SM over TP were analyzed based on this dataset.Keywords: Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau

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Determination of land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau by using the MODIS and in-situ data

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-19617-2011 ◽

2011 ◽

Vol 11 (7) ◽

pp. 19617-19638 ◽

Cited By ~ 1

Author(s):

Y. Ma ◽

L. Zhong ◽

B. Wang ◽

W. Ma ◽

X. Chen ◽

...

Keyword(s):

Heat Flux ◽

Tibetan Plateau ◽

Land Surface ◽

Surface Heat ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Surface Heat Fluxes ◽

In Situ Data ◽

Plateau Area

Abstract. In this study, a parameterization methodology based on MODIS (Moderate Resolution Imaging Spectroradiometer) and in-situ data is proposed and tested for deriving the regional surface reflectance, surface temperature, net radiation flux, soil heat flux, sensible heat flux and latent heat flux over heterogeneous landscape. As a case study, the methodology was applied to the Tibetan Plateau area. Four images of MODIS data (30 January 2007, 15 April 2007, 1 August 2007 and 25 October 2007) were used in this study for the comparison among winter, spring, summer and autumn. The derived results were also validated by using the "ground truth" measured in the stations of the Tibetan Observation and Research Platform (TORP). The results show that the derived surface variables (surface reflectance and surface temperature) and surface heat fluxes (net radiation flux, soil heat flux, sensible heat flux and latent heat flux) in four different seasons over the Tibetan Plateau area are in good accordance with the land surface status. These parameters show a wide range due to the strong contrast of surface features over the Tibetan Plateau. Also, the estimated land surface variables and surface heat fluxes are in good agreement with the ground measurements, and all their absolute percent difference (APD) is less than 10 % in the validation sites. It is therefore concluded that the proposed methodology is successful for the retrieval of land surface variables and surface heat fluxes using the MODIS and in-situ data over the Tibetan Plateau area. The shortage and further improvement of the methodology were also discussed.

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The evaluation of land surface parameters for model to derive land surface fluxes in the Tibetan Plateau

10.5194/egusphere-egu21-1568 ◽

2021 ◽

Author(s):

Weiqiang Ma ◽

Yaoming Ma ◽

Yizhe Han ◽

Wei Hu ◽

Lei Zhong ◽

...

Keyword(s):

Tibetan Plateau ◽

Land Surface ◽

Atmospheric Model ◽

Surface Fluxes ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Default Model ◽

Model Configuration

Firstly, based on the difference of model and in-situ observations, a serious of sensitive experiments were done by using WRF. In order to use remote sensing products, a land-atmosphere model was initialized by ingesting land surface parameters, such as AMSR-E RS products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations.Secondly, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in-situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.All of the different methods will clarify the land surface heating field in complex plateau, it also can affect atmospheric cycle over the Tibetan Plateau even all of the global atmospheric cycle pattern.

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A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

10.5194/ismc2021-26 ◽

2021 ◽

Author(s):

Jingyi Huang ◽

Ankur Desai ◽

Jun Zhu ◽

Alfred Hartemink ◽

Paul Stoy ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

Field Scale ◽

Land Surface Parameters ◽

Global Surface

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m&#8722;3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08&#8211;0.10 m3 m&#8722;3). The global model was then applied to map SSM dynamics at 30&#8211;100m across a field-scale network (TERENO-W&#252;stebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (&#8220;spiking&#8221;), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.

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