A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau

Cui, Yaokui; Zeng, Chao; Chen, Xi; Fan, Wenjie; Liu, Haijiang; Liu, Yuan; Xiong, Wentao; Sun, Cong; Luo, Zhaohua

doi:10.1109/jstars.2020.3043336

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Land surface temperature (LST) has a deep influence in the study of water balance, land surface energy and land surface processes at regional and global scales [1,2], and it plays an essential role in various fields, such as monitoring soil moisture, evapotranspiration, drought assessment, urban climate change, hydrological cycle research and disease transmission [3][4][5][6][7]. Since the 1970s, the extraction of LST from airborne thermal infrared data has attracted much attention [8].…”

Section: Introductionmentioning

confidence: 99%

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Xiao

Zhao

et al. 2021

Remote Sensing

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Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

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Section: Introductionmentioning

confidence: 99%

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Xiao

Zhao

et al. 2021

Remote Sensing

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“…Many scholars have conducted long time series soil moisture products reconstruction in the QTP. For example, the RFSM soil moisture product of QTP that based on random forest method had high accuracy by comparing RFSM with the in situ soil moisture observation (R = 0.75, RMSE = 0.06 m 3 /m 3 , and bias = −0.03 m 3 /m 3 ) [28]; a new fused soil moisture product in QTP using GRNN to train essential climate variables (ECV) and Fengyun (FY) SM had acceptable accuracy by comparing it with original ECV and FY SM (R = 0.809, RMSE = 0.081 cm 3 /cm 3 , and bias = 0.050 cm 3 /cm 3 ) [31]; and a global soil moisture product (NNsm) utilizing NN to train AMSR-E/AMSR-2 and SMOS obtained high-accuracy NNsm data by comparing the data with the in situ soil moisture (R = 0.52, RMSE = 0.084 m 3 /m 3 and Bias = −0.002 m 3 /m 3 for the all land grid cells of global) [59]. Compared with the previous soil moisture products mentioned above, the R and BIAS of the ANNSM in this study were acceptable, but the RMSE was relatively low.…”

Section: Comparison Of Different Reconstruction Productsmentioning

confidence: 99%

“…It usually takes the most credible soil moisture products as the standard reference dataset and trains other data to obtain the non-linear function that can be applied to achieve more precise prediction. Previous studies have used machine learning algorithms, such as Neural Network (NN) [25], General Regression Neural Network (GRNN) [26], convolutional neural network (CNN) [27], Random Forest (RF) [28], etc., and adopted auxiliary data, including of the Normalized Difference Vegetation Index (NDVI) [29], Microwave Vegetation Index (MVI) [30], Land Surface temperature (LST) [31], Leaf Area Index (LAI) [32], Albedo, etc., to train multi-source soil moisture data to obtain longer homogeneous time series products. The Artificial Neural Network (ANN) is an effective approach to establish a nonlinear model and widely applied in microwave remote sensing soil moisture retrieval [24,33,34].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Wang

2021

Remote Sensing

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Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

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Deploying an energy efficient, secure & high-speed sidechain-based TinyML model for soil quality monitoring and management in agriculture

Bhattacharya,

Pandey

2024

Expert Systems with Applications

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A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau

Cited by 10 publications

References 35 publications

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Deploying an energy efficient, secure & high-speed sidechain-based TinyML model for soil quality monitoring and management in agriculture

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