scholarly journals A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data

2019 ◽  
Vol 11 (7) ◽  
pp. 767 ◽  
Author(s):  
Zhenwei Zhang ◽  
Qingyun Du
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Air Temperature ◽  
Urban Areas ◽  
Cross Validation ◽  
Predictive Performance ◽  
Regression Method ◽  
Model Parameters ◽  
Linear Regression Method ◽  
Bayesian Kriging

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1 . 23 K for Tmax and 1 . 20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1 . 21 K for Tmax and 1 . 27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time.

scholarly journals Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns

Urban Science ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 101 ◽  
Author(s):  
Lucille Alonso ◽  
Florent Renard
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Urban Areas ◽  
Land Development ◽  
Explanatory Variables ◽  
Dense Networks ◽  
Urban Heat ◽  
Derived Data ◽  
Computer Resources

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.

scholarly journals Crop switching reduces agricultural losses from climate change in the United States by half under RCP 8.5

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
James Rising ◽  
Naresh Devineni
Keyword(s):  
Climate Change ◽  
United States ◽  
Crop Production ◽  
Cross Validation ◽  
Climate Adaptation ◽  
The United States ◽  
Model Parameters ◽  
Economic Potential ◽  
Bayesian Hierarchical ◽  
Rcp 8.5

AbstractA key strategy for agriculture to adapt to climate change is by switching crops and relocating crop production. We develop an approach to estimate the economic potential of crop reallocation using a Bayesian hierarchical model of yields. We apply the model to six crops in the United States, and show that it outperforms traditional empirical models under cross-validation. The fitted model parameters provide evidence of considerable existing climate adaptation across counties. If crop locations are held constant in the future, total agriculture profits for the six crops will drop by 31% for the temperature patterns of 2070 under RCP 8.5. When crop lands are reallocated to avoid yield decreases and take advantage of yield increases, half of these losses are avoided (16% loss), but 57% of counties are allocated crops different from those currently planted. Our results provide a framework for identifying crop adaptation opportunities, but suggest limits to their potential.

Rapid and Simultaneous Determination of Three Active Components in Raw and Processed Root Samples of Scutellaria baicalensis by Near-infrared Spectroscopy

Planta Medica ◽  
2018 ◽  
Vol 85 (01) ◽  
pp. 72-80 ◽  
Author(s):  
Xue Jintao ◽  
Yang Quanwei ◽  
Li Chunyan ◽  
Jing Yun ◽  
Wang Shuangxi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Regression Method ◽  
Model Parameters ◽  
Linear Regression Method ◽  
Active Components ◽  
Treatment Methods ◽  
Pre Treatment

AbstractMotivated by the wide use of Scutellariae Radix (SR) in the food and pharmaceutical industries, a rapid and non-destructive near-infrared spectroscopy (NIRS) method was developed for the simultaneous analysis of three main active components in raw SR and SR processed by stir-frying with wine. From seven geographical areas, 58 samples were collected. The reference contents for the SR components baicalin, baicalein, and wogonin were determined by high-performance liquid chromatography. Two multivariate analysis methods, partial least-squares (PLS) regression as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method, were applied to the NIR data, and their results were compared. In the PLS model, different model parameters (i.e., 11 spectral pre-treatment methods), spectral region, and latent variables were investigated to optimize the calibration model; additionally, the ANN model was applied with five different spectral pre-treatment methods and six algorithms. For the optimal model parameters, the correlation coefficients of the calibration set for baicalin, baicalein, and wogonin were 0.9979, 0.9786, and 0.9773, respectively; the correlation coefficients of the prediction set were 0.9756, 0.9843, and 0.9592, respectively; the root mean square error of validation values were 0.215, 0.321, and 0.174, respectively. The optimal NIR models were then employed to analyze the effects of processing and geographical regions on analyte contents. The established NIR methods were robust, accurate, and reproducible. NIRS may be a promising approach for the routine screening and quality control of traditional Chinese medicines.

scholarly journals Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale

2017 ◽  
Vol 56 (3) ◽  
pp. 803-814 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Jing Tian ◽  
Renhua Zhang ◽  
Weizhen Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Air Temperature ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
The Other ◽  
Linear Regression Method ◽  
Estimation Errors ◽  
Remote Sensing Methods

AbstractSurface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods—the temperature–vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)—have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1°C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

scholarly journals Seroprevalence and Determinants Associated with Mumps Antibodies after 20 Years of MMR Vaccination in Urban Area of Shanghai, China

2018 ◽  
Vol 15 (10) ◽  
pp. 2089 ◽  
Author(s):  
Hong Pang ◽  
Yibiao Zhou ◽  
Wensui Zhao ◽  
Qingwu Jiang
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Age Groups ◽  
Dose Schedule ◽  
Regression Method ◽  
Antibody Concentration ◽  
Linear Regression Method ◽  
Mmr Vaccine ◽  
Increased Risk ◽  
Mumps Antibodies

A resurgence of the mumps epidemic in highly vaccinated populations has occurred in recent years in many countries. This study aimed to evaluate the seroprevalence to mumps in urban areas of Shanghai, where a measles-mumps-rubella (MMR) vaccination had been implemented for 20 years. Mumps IgG antibodies were tested in 2662 residual sera from all ages in an urban area of Shanghai. A linear regression method was performed to assess the persistence of mumps antibodies after MMR vaccination. A logistic regression method was used to analyze the variables associated with seronegative sera. The overall age- and gender-adjusted seroprevalence of mumps antibodies reached 90% (95% CI: 90.0–90.2). The antibody concentration declined significantly in the first eight years after the second dose of MMR. The multivariate analysis identified that males, age groups, especially 17–19 years and no dose of vaccination, as well as one dose of vaccination, as factors associated with an increased risk of seronegative sera. A high seroprevalence to mumps has been achieved in the urban areas of Shanghai. A declining antibody level of mumps after the second dose of MMR may put a potential risk of recurrence of mumps. The two-dose MMR vaccine schedule is superior to one-dose schedule for mumps control.

scholarly journals A Model for the Prediction of Tobacco Temperature and Oxygen Profiles in Warehouse Aging Process

2007 ◽  
Vol 22 (5) ◽  
pp. 358-364
Author(s):  
Y Zheng ◽  
J Chipley ◽  
A Dow ◽  
C Midgett
Keyword(s):  
Ambient Temperature ◽  
Temperature Profile ◽  
Aging Process ◽  
Consumption Rate ◽  
Regression Method ◽  
Model Parameters ◽  
Linear Regression Method ◽  
Proposed Model ◽  
Oxygen Profile ◽  
Oxygen Profiles

AbstractA mathematical model on the temperature and oxygen profiles for the tobacco warehouse aging process was formulated and solved by numeric analysis. The model parameters were obtained using the non-linear regression method by fitting several years measured temperatures to the model. The R square value between measured and calculated tobacco temperatures in warehouse aging process are all over 0.95. The proposed model can be used to predict the tobacco hogshead temperature profile at different time and positions with ambient temperature, tobacco moisture contents and pH. At the same time, the model also predicts the oxygen profile in the hogshead. The effects of the ambient temperature, pH, void fraction, the reaction active energy, oxygen diffusivity, and the oxygen consumption rate constant on the temperature profile were studied.

scholarly journals Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 437
Author(s):  
Chensi Weng ◽  
Lei Liu ◽  
Taichang Gao ◽  
Shuai Hu ◽  
Shulei Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Terahertz Radiation ◽  
Regression Method ◽  
Inversion Method ◽  
Linear Regression Method ◽  
Water Path ◽  
Passive Remote Sensing ◽  
Terahertz Band ◽  
Ice Water Path ◽  
Ice Water

Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 ± 1.1 GHz, 118.75 ± 3.0 GHz), two water vapor absorption channels (183.31 ± 1.0 GHz, 183.31 ± 7.0 GHz), and two window channels (243.20 ± 2.5 GHz, 874.4 ± 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m2, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.

scholarly journals Irregular urban Expansion and Its Effects on Air Temperature over Baghdad City using Remote Sensing Technique

2021 ◽  
pp. 2110-2121
Author(s):  
Ahmed F. Hassoon ◽  
Abdulrahman B. Ali
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Air Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Supervised Classification ◽  
Satellite Images ◽  
Urban Expansion ◽  
Maximum Temperature ◽  
The City

Heat island is known as the increases in air temperature through large and industrial cities compared to surrounding rural areas. In this study, remote sensing technology is used to monitor and track thermal variations within the city center of Baghdad through Landsat satellite images and for the period from 2000 to 2015. Several processors and treatments were applied on these images using GIS 10.6 and ERDAS 2014, such as image correction and extraction, supervised classification, and selection of training samples. Urban areas detection was resulted from the supervised classification linked to the temperature readings of the surface taken from the thermal bands of satellite images. The results showed that the surface temperature of the city of Baghdad increased by 8 degrees Celsius in 15 years. This is due to the increase in the expansion of the urban areas type of land use, where the human activity, especially after 2003, caused increased buildup area to about 198.41 km2. All these changes occurred at the expense of many green regions which were reduced, with the transformation of open and agricultural areas to residential, commercial, and industrial uses. Increases in surface temperature resulted increases in air temperature, where the minimum temperature showed larger increases relative to maximum temperature (about 1.44 and 0.76 ºC, respectively).

scholarly journals THERMAL REMOTE SENSING OF URBAN CLIMATES IN SOUTH AFRICA THROUGH THE MONO-WINDOW ALGORITHM

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 117-123
Author(s):  
A. Ngie
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Satellite Image ◽  
Direct Determination ◽  
Urban Environments ◽  
Thermal Remote Sensing ◽  
Heat Islands

Abstract. Urban Heat Island (UHI) is among some of the challenges plaguing urban environments. There is increase human population within urban environments especially in the developing world, which is a need to understand the climates for their wellbeing. The use of multispectral satellite remote sensing to investigate the climatic conditions through radiation measurement is applied across the two major South African cities. The thermal remote sensing technique applied for this study is the direct determination of land surface temperatures (LST) using multispectral thermal imagery (ETM+). In addition, meteorological data which included air temperature and relative humidity for the same satellite image dates were used. The LST values obtained showed Johannesburg has many micro heat islands scattered across the metro than in Cape Town. These areas of heat islands corresponded to areas of human settlement and more so the unplanned as opposed to the planned ones. The estimated LST values and observed air temperature values with an R2 of 0.9. It could be concluded that expansion of urban areas in South Africa has led to increased thermal radiation of land surface in densely populated areas.

Sign in / Sign up

Export Citation Format

Share Document