scholarly journals Evaluation of Remotely Sensed and Reanalysis Soil Moisture Against In Situ Observations on the Himalayan-Tibetan Plateau

2018 ◽  
Author(s):  
Qiang Zhang ◽  
Keke Fan ◽  
Vijay P. Singh ◽  
Peng Sun ◽  
Peijun Shi
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Remotely Sensed ◽  
In Situ Observations

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

Interannual Variations and Trends in Remotely Sensed and Modeled Soil Moisture in China

2018 ◽  
Vol 19 (5) ◽  
pp. 831-847 ◽  
Author(s):  
Binghao Jia ◽  
Jianguo Liu ◽  
Zhenghui Xie ◽  
Chunxiang Shi
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Surface Soil ◽  
Central China ◽  
The Tibetan Plateau ◽  
Interannual Variations ◽  
Surface Soil Moisture ◽  
Community Land Model ◽  
In Situ Observations

Abstract In this study, a microwave-based multisatellite merged product released from the European Space Agency’s Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%–88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation.

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

2012 ◽  
Vol 118 ◽  
pp. 215-226 ◽  
Author(s):  
Clement Albergel ◽  
Patricia de Rosnay ◽  
Claire Gruhier ◽  
Joaquin Muñoz-Sabater ◽  
Stefan Hasenauer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Remotely Sensed ◽  
In Situ Observations

scholarly journals Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

2019 ◽  
Vol 11 (16) ◽  
pp. 1875 ◽  
Author(s):  
Burak Bulut ◽  
M. Tugrul Yilmaz ◽  
Mehdi H. Afshar ◽  
A. Ünal Şorman ◽  
İsmail Yücel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Soil Type ◽  
Vegetation Cover ◽  
Remotely Sensed ◽  
Mean Square ◽  
Climate Regime ◽  
Model Based ◽  
In Situ Observations

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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