A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

Leng, Pei; Song, Xiaoning; Duan, Si‐Bo; Li, Zhao-Liang

doi:10.1016/j.jag.2016.07.004

Cited by 52 publications

(21 citation statements)

References 45 publications

(47 reference statements)

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“…We use simply the observed solar radiation at 12:00 local time as the daily maximum solar radiation input is most likely to decrease the uncertainties for SSM retrieval, especially over larger areas. As reported in previous study (Leng et al, 2016), the linear relationship between the constant term and daily maximum solar radiation probably vary with regions. Hence, accurate determination of the constant term over larger areas is also a crucial task of estimating continuous SSM dataset.…”

Section: Calibration Strategiessupporting

confidence: 74%

“…Obviously, because the model coefficients are obtained from numerous simulated data or through calibration processes, these time‐variable coefficients complicate the routine application of the SSM algorithm to generate SSM, especially for obtaining SSM over a long time series. Thus, to simplify the use of the model, a practical SSM retrieval algorithm under the assumption that all ellipse variables can be fixed, whereas the constant term varies daily and can only be determined using simple meteorological parameters in a prediction model, was developed recently (Leng et al, ). The new algorithm was written as follows:

SSM = \overset{n_{1}}{true} \times x_{0} + \overset{n_{2}}{true} \times y_{0} + \overset{n_{3}}{true} \times a + \overset{n_{4}}{true} \times θ + p \times S_{\max} + q,

…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, exploring the use of the geostationary satellite‐derived temporal information for estimating SSM to eliminate the dependence of SSM retrieval on knowledge of soil properties and ground SSM measurements has been paid increasing attention (Leng et al, ; Zhao & Li, ; Zhao et al, ). In a latest study (Leng, Song, Duan, & Li, ), a practical algorithm that uses diurnal parameters that describe the temporal evolution of land surface temperature (LST) and net surface shortwave radiation (NSSR) was developed to estimate SSM. Taking advantage of the temporal information derived from geostationary satellite data, this SSM retrieval algorithm has several advantages.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Calibration Strategiessupporting

confidence: 74%

SSM = \overset{n_{1}}{true} \times x_{0} + \overset{n_{2}}{true} \times y_{0} + \overset{n_{3}}{true} \times a + \overset{n_{4}}{true} \times θ + p \times S_{\max} + q,

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Due to the advantage of remote sensing observations, many efforts have been dedicated to determining the surface soil moisture for regional or even global applications [9][10][11][12][13][14]. Early field experiments demonstrated that passive microwave emissions and active microwave scatter are a function of the dielectric constant of the surface soil layer, and that the dielectric constant is related to the soil water content.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Sun

Huang

et al. 2017

Remote Sensing

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Abstract:The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm 3 ·cm −3 and 0.039 cm 3 ·cm −3 based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm.

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“…Wetlands also provide flood protection, protection from storm and wave damage, water quality improvements through the filtering of agricultural and industrial waste, and aquifer recharge [6][7][8]. Although wetlands play an important role in environmental processes and provide many ecological services for mankind, they are under increasing threats from climate change, dredge and fill operations, hydrologic variations, pollutant runoff, eutrophication, impoundments, and land modification, as well as the growing load placed on wetlands by agriculture, aquaculture, forestry, and development [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Spatiotemporal Changes in Wetland Geomorphology Based on Type 2 Fuzzy Sets: A Case Study of Beidagang Wetland from 1975 to 2015

Huo

Guo

et al. 2017

Remote Sensing

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Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM*) approach to analyse the spatial and temporal changes in the geomorphology of the Beidagang wetland in North China from 1975 to 2015 based on long-term Landsat data. Unlike traditional type 1 fuzzy c-means methods, the IT2FCM* algorithm based on interval type-2 fuzzy set has an ability to better handle the spectral uncertainty. Four indexes were adopted to validate the separability of classes with the IT2FCM* algorithm. These four validity indexes showed that IT2FCM* obtained better results than traditional methods. Additionally, the accuracy of the classification results was assessed based on the confusion matrix and kappa coefficient, which were high for the analysis of wetland landscape changes. Based on the analysis of separability of classes with the IT2FCM* algorithm using four validity indexes, the classification results, and the membership value images, the long-term series of satellite datasets were processed using the IT2FCM* method, and the study area was classified into six classes. Because water resources and vegetation are two key wetland components, the water resource dynamics and vegetation dynamics, based on the normalized difference vegetation index (NDVI), were analysed in detail according to the spatiotemporal classification results. The results show that the changes in vegetation types have historically been associated with water resource variations and that water resources play an important role in the evolution of vegetation types.

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A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

Cited by 52 publications

References 45 publications

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

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

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Remote Sensing of Spatiotemporal Changes in Wetland Geomorphology Based on Type 2 Fuzzy Sets: A Case Study of Beidagang Wetland from 1975 to 2015

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