An Advanced Framework for Merging Remotely Sensed Soil Moisture Products at the Regional scale Supported by Error Structure Analysis: A Case Study on the Tibetan Plateau

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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

Remote Sensing of Environment ◽

10.1016/j.rse.2015.03.008 ◽

2015 ◽

Vol 163 ◽

pp. 91-110 ◽

Cited By ~ 147

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

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

Science China Earth Sciences ◽

10.1007/s11430-013-4700-8 ◽

2013 ◽

Vol 56 (12) ◽

pp. 2173-2185 ◽

Cited By ~ 17

Author(s):

Qiang Liu ◽

JinYang Du ◽

JianCheng Shi ◽

LingMei Jiang

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Tibetan Plateau ◽

Remotely Sensed ◽

The Tibetan Plateau

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

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2303-2011 ◽

2011 ◽

Vol 15 (7) ◽

pp. 2303-2316 ◽

Cited By ~ 159

Author(s):

Z. Su ◽

J. Wen ◽

L. Dente ◽

R. van der Velde ◽

L. Wang ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Soil Temperature ◽

Land Surface ◽

Regional Scale ◽

The Tibetan Plateau ◽

Humid Climate ◽

Coarse Resolution ◽

Reference Networks

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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Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau

Remote Sensing ◽

10.3390/rs12183087 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3087

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Reference Data ◽

Regional Scale ◽

Thermal Inertia ◽

Mean Value ◽

The Tibetan Plateau ◽

Random Errors ◽

Error Structure ◽

The Mean

Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets.

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