scholarly journals Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1401 ◽  
Author(s):  
Shaodan Chen ◽  
Dunxian She ◽  
Liping Zhang ◽  
Mengyao Guo ◽  
Xin Liu
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Land Surface ◽  
Environmental Variables ◽  
Random Forest Model ◽  
Important Indicator ◽  
Forest Model ◽  
Advantages And Disadvantages ◽  
Soil Moisture Data ◽  
Downscaling Method

Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.

scholarly journals Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

2020 ◽  
Vol 12 (12) ◽  
pp. 1986 ◽  
Author(s):  
Johanna Orellana-Alvear ◽  
Rolando Célleri ◽  
Rütger Rollenbeck ◽  
Paul Muñoz ◽  
Pablo Contreras ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Model Performance ◽  
Radar Data ◽  
Rain Gauge ◽  
Random Forest Model ◽  
Mountain Regions ◽  
Radar Rainfall ◽  
Forest Model ◽  
Rainfall Estimates

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.

UAS Based Soil Moisture Downscaling Using Random Forest Regression Model

2021 ◽  
Author(s):  
Ruodan Zhuang ◽  
Salvatore Manfreda ◽  
Yijian Zeng ◽  
Nunzio Romano ◽  
Eyal Ben Dor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Random Forest ◽  
Surface Temperature ◽  
Regression Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Features ◽  
Coarse Resolution ◽  
Soil Moisture Data

&lt;p&gt;Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.&lt;/p&gt;&lt;p&gt;The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.&lt;/p&gt;&lt;p&gt;The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.&lt;/p&gt;&lt;p&gt;It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.&lt;/p&gt;&lt;p&gt;This research is a part of EU COST-Action &amp;#8220;HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring&amp;#8221;.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS&lt;/p&gt;

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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