Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Leng, Pei; Song, Xiaoning; Li, Zhao-Liang; Ma, Jing; Zhou, Fengxia; Li, Shuang

doi:10.1080/01431161.2013.875237

Cited by 51 publications

(49 citation statements)

References 32 publications

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“…Within this context, the objective of the study presented in this paper, is to evaluate the effects of soil layer classification in the CoLM on simulated diurnal cycles of LST and NSSR and especially the impact on the associated SSM retrieval model by Leng et al [7]. Furthermore, this study aims to develop an optimal soil layer classification for CoLM while producing simulated data to develop the SSM retrieval model.…”

Section: Introductionmentioning

confidence: 99%

“…Associated models developed from LSM simulated data have been less reported on. In recent studies, Leng et al [7] and Zhao et al [9] reported on their SSM retrieval models that based on simulated land surface temperature (LST) and net surface shortwave radiation (NSSR) obtained with the Common Land Model (CoLM) and the Community Noah Land-surface Model, respectively. Analogous to the development of the SSM retrieval models, the two LSMs were used to produce simulated data with various underlying surfaces for each given atmospheric condition.…”

Section: Introductionmentioning

confidence: 99%

“…Another use of the LSM is to simulate data with various underlying surfaces and atmospheric conditions in case such observation are not available. Subsequently models for land surface variable retrieval are developed [7][8][9]. In these situations, the initial datasets of underlying surfaces, including the surface soil moisture (SSM), soil type and land cover type, etc., are used to generate simulated data on several discrete cloud-free days under given atmospheric conditions [7].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently models for land surface variable retrieval are developed [7][8][9]. In these situations, the initial datasets of underlying surfaces, including the surface soil moisture (SSM), soil type and land cover type, etc., are used to generate simulated data on several discrete cloud-free days under given atmospheric conditions [7]. Obviously, as only the atmospheric conditions of several individual cloud-free days are available in these cases, the spin-up process is usually not used to make the model state variables (e.g., soil moisture, surface temperature, latent heat, and net radiation) approach their equilibriums, hence, the simulated data are not only dependent on the representation of physical processes in the LSM, but also greatly affected by the soil layer classification in which the soil properties are specified [10].…”

Section: Introductionmentioning

confidence: 99%

“…Analogous to the development of the SSM retrieval models, the two LSMs were used to produce simulated data with various underlying surfaces for each given atmospheric condition. In their studies, the soil layers were reclassified to obtain a first layer with a depth of 5 cm to enable the association with LST responses [7][8][9]. Different LSMs were originally developed with different numbers of soil layers and soil depths [18,19], and changing the number of vertical layers of which the thickness of the individual layers can potentially influence simulated land surface states and fluxes through soil water [15].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model

Leng

Song

et al. 2013

Remote Sensing

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Land surface soil moisture (SSM) is crucial in research and applications in hydrology, ecology, and meteorology. A novel SSM retrieval model, based on the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR), has recently been reported. It suggests a promising avenue for the retrieval of regional SSM using LST and NSSR derived from geostationary satellites in a future development. As part of a further improvement of previous work, effects of soil layer classification in the Common Land Model (CoLM) on modeled LST, NSSR and the associated SSM retrieval model in particular, have been evaluated. To address this issue, the soil profile has been divided in to three layers, named upper layer (0-0.05 m), root layer (0.05-1.30 m) and bottom layer (1.30-2.50 m). By varying the number of soil layers with the three layer zones, nine different soil layer classifications have been performed in the CoLM to produce simulated data. Results indicate that (1) modeled SSM is less sensitive to soil layer classification while modeled LST and NSSR are sensitive, especially under wet conditions and (2) the simulated data based SSM retrieval model is stable for a fixed upper layer with varying classifications of root and bottom layers. It also concludes an optimal soil layer classification for the CoLM while producing simulated data to develop the SSM retrieval model. OPEN ACCESSRemote Sens. 2013, 5 5515

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model

Leng

Song

et al. 2013

Remote Sensing

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Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

et al. 2017

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Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

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A comparative approach of data-driven split-window algorithms and MODIS products for land surface temperature retrieval

Garajeh

Feizizadeh

2021

Appl Geomat

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The main objective of this paper is to compare eighteen data-driven split-window (SW) algorithms to derive land surface temperature (LST) and identify the most efficient techniques. We also aim to compare the results of the SW algorithms against the MODIS LST products (MOD11_L2/MYD11_L2). To archive these goals, the radiometric correction was accomplished to the MODIS satellite image, and then, the LST was derived using the SW algorithms for the year 2019. This LST was validated using RMSE, MSE, MAPE, and MAD based on the observed data in local meteorological stations. Validation analysis reveals that the Sobrino 1993 algorithm with a RMSE value of 1.79 and Qin algorithm with a RMSE value of 5.28 have respectively high and low accuracy to calculate LST in the study area. Furthermore, we compared the MODIS LST products against the local meteorological station data and achieved respectively the RMSE values of 13.

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Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Cited by 51 publications

References 32 publications

Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model

Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

A comparative approach of data-driven split-window algorithms and MODIS products for land surface temperature retrieval

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