Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

Cui, Yaokui; Long, Di; Hong, Yang; Zeng, Chao; Zhou, Jie; Han, Zhuo Rachel; Liu, Ronghua; Wan, Wei

doi:10.1016/j.jhydrol.2016.10.005

Cited by 84 publications

(68 citation statements)

References 45 publications

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“…We used high-quality MODIS LST products, as determined from the quality control files while also removing images which had valid data less than 90% of the study area or when precipitation was greater than 1 mm d −1 . Gap-free NDVI products with a temporal resolution of 1-day were obtained using Harmonic Analysis of Time Series (HANTS) [27,36]. The LST and NDVI are two main parameters used in the Ts-VI triangle model.…”

Section: Datamentioning

confidence: 99%

“…The traditional machine learning method has been examined, with the goal of improving performance in filling gaps in flux tower observation-based regional ET and potential ET [24][25][26]. As a data-driven method, an artificial neural network has the potential to be used to obtain temporally continuous ET based on limited ET estimates from the Ts-VI triangle model [27]. Meanwhile, the deep neural network (DNN), characterized by having additional hidden layers, provides greater ability than a traditional shallow neural network [28] to find the complex relationship between ET and reference data.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Cui

Yao

et al. 2020

Remote Sensing

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Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Cui

Yao

et al. 2020

Remote Sensing

Self Cite

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“…These instruments include the Advanced Scatterometer (ASCAT), the Microwave Radiation Imager, and the Advanced Microwave Scanning Radiometer 2. ASCAT is an active microwave sensor on board Metop-A and Metop-B and works at frequency 5.255 GHz (Albergel et al, 2009;Y. Cui et al, 2016;Dorigo et al, 2010;Parinussa et al, 2015;Wagner et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Mousa

Hong

2020

Earth and Space Science

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The limited number of in situ stations of surface soil moisture (SM) in Africa creates a shortage in the validation of SM satellite products. Therefore, this study investigates the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and the H113 product from the Advanced Scatterometer (ASCAT) on the regional scale over Africa through these goals: (1) validate of satellite SM products against in situ stations and SM data from the ERA-Interim atmospheric reanalysis product, (2) study the spatiotemporal variability of satellite SM products on the regional scale, and (3) evaluate the regional scale error patterns and investigate regions where the assimilation of satellites SM data may add improvement to ERA-Interim. Standard statistical metrics, hovmöller diagrams, and the Triple Collocation (TC) model were used to achieve these goals. Land cover data, Normalized Difference Vegetation Index, and precipitation data were used to interpret results. The validation results based on statistical metrics and TC indicate that over the desert and shrub, passive products showed better performance than ASCAT, while over moderate vegetation areas (grassland), SMAP had the best among SM products. Over high densely vegetated regions, ASCAT showed a high comparatively performance than passive products. The potential regions for assimilation of satellite data sets were selected to be over savannas and grassland regions for ASCAT, and over shrub and grassland regions for SMAP. In particular, SMAP and ASCAT SM data sets are considered more stable than SMOS for data assimilation and capturing the spatial distribution of SM on the regional scale over Africa.

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“…Rodriguez-Fernandez et al [23] used a neural network (NN) to establish a model composed of L-band SMOS Tbs complemented with C-band advanced scatterometer (ASCAT) backscattering coefficients, as well as the moderate resolution imaging spectroradiometer (MODIS), the normalized difference vegetation index (NDVI), and a reference SM data set (European Centre For Medium-Range Weather Forecasts (ECMWF) model predictions). Cui et al [24] used the back-propagation neural network (BPNN) adopting MODIS products (LST, NDVI, and albedo), other auxiliary data (longitude, latitude, digital elevation model (DEM), and day of the year (DOY)) and the FY (FengYun)-3B/MWRI (microwave radiation imager) SM product to rebuild SM on the QTP. The results show that the R 2 (coefficient of determination) is greater than 0.56, root mean square error (RMSE) is under 0.1 cm 3 /cm 3 , and bias is under 0.07 cm 3 /cm 3 .…”

Section: Introductionmentioning

confidence: 99%

Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China

Zhu

Chai

et al. 2019

Remote Sensing

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Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

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Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

Cited by 84 publications

References 45 publications

Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China

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