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

Zeng, Jiangyuan; Li, Zhen; Chen, Quan; Bi, Haibo; Qiu, Jianxiu; Zou, Pengfei

doi:10.1016/j.rse.2015.03.008

Cited by 327 publications

(256 citation statements)

References 107 publications

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“…For the approximately simultaneous acquisitions of multisensor SAR data, the soil dielectric constant is equivalent, and the incidence angle is a known parameter. erefore, there are five unknown dielectric constants in observation (7).…”

Section: Developed Alpha Modelmentioning

confidence: 99%

“…Synthetic aperture radar (SAR) is one of the most promising techniques for measuring surface soil moisture at moderate-to-high spatial resolution required by hydrological, meteorological, ecological, and agricultural applications [1][2][3]. However, accurate soil moisture retrieval from SAR data is still a challenging task due to the fact that the radar backscatter is influenced by multiple parameters such as soil dielectric constant (related to soil moisture), surface roughness, and vegetation conditions [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Zhang

Tang

Gao

et al. 2018

Advances in Meteorology

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e objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. e Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. e solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. is research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. e disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. e optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. e agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm 3 /cm 3 for both methods. e optimized Alpha model shows an obvious improvement for soil moisture retrieval. e results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

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Section: Developed Alpha Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Zhang

Tang

Gao

et al. 2018

Advances in Meteorology

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“…The ECV SM product has been validated across different regions using in-situ, model and SAR-derived soil moisture datasets in previous studies (e.g. Albergel et al, 2013b;Loew et al, 2013;Dorigo et al, 2015;Pratola et al, 2014;Zeng et al, 2015) where good agreement between the datasets was generally found. For example, Zeng et al (2015) found the ECV SM product to be highly related to in-situ data from two different soil moisture networks at the Tibetan Plateau, with the highest R values (0.70 -0.85) and smallest ubRMSD values (0.034 -0.042m 3 m -3 ) compared to six other satellite derived soil moisture products (AMSR-E (NASA product), AMSR-E (JAXA product), AMSR-E (LPRM product), AMSR-2, ASCAT, and SMOS).…”

Section: Ecv Soil Moisture Observationsmentioning

confidence: 99%

“…Since the late 1970s, coarse resolution (25 -50km) soil moisture products derived from past and present microwave radiometers (Advanced Microwave Scanning Radiometer (AMSR-E) (Njoku et al, 2003) and WindSat (Li et al, 2010)) and scatterometers (European Remote Sensing satellites (ERS) scatterometer (SCAT) (Wagner a (Bartalis et al, 2007;Naeimi et al, 2009)) have been available on an operational basis. Data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) (Kerr et al, 2012;Mecklenburg et al, 2012) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) (Entekhabi et al, 2010) dedicated soil moisture missions are strengthening this record of observations and further facilitating the study of long term soil moisture behaviour (please see Petropoulos et al (2015) and Zeng et al (2015) for further details of available satellite derived soil moisture products). With the availability of these products, it is necessary to validate them using independently derived soil moisture observations obtained through in-situ monitoring, models, or with different satellite sensors (van Doninck et al, 2012;Ochsner et al, 2013;Al-Yaari et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Barrett¹,

Pratola²,

Gruber³

et al. 2016

Satellite Soil Moisture Retrieval

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The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology and climatic modelling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), using finer spatial resolution Synthetic Aperture Radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in-situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse scale CCI Essential Climate Variable (ECV) soil moisture product (ECV SM) in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture datasets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture datasets at the Finnish sites. Overall, the two different satellite derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse scale ECV SM product to track soil moisture variability in time.

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“…In the past few decades, several research groups have successfully retrieved soil moisture products from multiple remote sensing sensors (Draper et al, 2009;Entekhabi et al, 2010;Mecklenburg et al, 2012). However, these retrieval methods perform poorly for estimating soil moisture values in cold regions such as the Tibetan Plateau (TP) area (Chen et al, 2013b;Su et al, 2011;Zeng et al, 2015), and only the value of a few centimeters depth can be derived, which is limited for many applications. Ground measurements are the most accurate soil moisture estimates, and huge efforts have been conducted to establish long-term and largescale soil moisture observation networks in several regions of the world (Dorigo et al, 2011;Yang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

Sun

Chen

et al. 2016

Ecological Modelling

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a b s t r a c tMost current land surface models (LSMs) show poor performance in soil water and temperature simulations for cold regions because of the inappropriate model structures and ill-parameterizations. This study aims to explore how to parameterize soil organic carbon (SOC) in term of soil moisture simulations using the Community Land Model version 4.0 (CLM4.0) model and the Dynamic Land Model version 1.0 (DLM1.0) model. Firstly, we discuss the sensitivities of modeled soil moisture to SOC based on observations. The SOC parameterizations in both CLM4.0 and DLM1.0 were proved poor against the measured SOC through sensitivity analysis for the Heihe River watershed area (cold but with high SOC). A SOC parameterization was developed and tested based on multiple year observations. Our findings are twofold: (i) the soil organic parameterizations in CLM4.0 and DLM1.0 are inappropriate for simulating soil moisture in cold regions with high SOC, and the modified SOC parameterization significantly improves the soil moisture simulations; (ii) the impacts of SOC on soil moisture vary significantly with seasons, which is largest for JJA (June, July and August) followed by SON (September, October and November), MAM (March, April and May) and DJF (December, January and February). This study highlights that the impacts of SOC should be fully considered for LSMs soil moisture simulations, especially for cold areas with high SOC.

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

Cited by 327 publications

References 107 publications

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

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