scholarly journals Estimating the Surface Air Temperature by Remote Sensing in Northwest China Using an Improved Advection-Energy Balance for Air Temperature Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Weizhen Wang
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Air Temperature ◽  
Driving Force ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Northwest China ◽  
Absolute Deviation ◽  
Distance Weighting

To estimate the surface air temperature by remote sensing, the advection-energy balance for the surface air temperature (ADEBAT) model is developed which assumes the surface air temperature is driven by the local driving force and the advective driving force. The local driving force produces a local surface air temperature whereas the advective driving force changes it by adding an exotic air temperature. An advection factorfis defined to measure the quantity of the exotic air brought by the advection. Since thefis determined by the advection, this paper improves it to a regional scale by using the Inverse Distance Weighting (IDW) method whereas the original ADEBAT model uses a constant offfor a block of area. Results retrieved by the improved ADEBAT (IADEBAT) model are evaluated and comparison was made with the in situ measurements, with anR2(correlation coefficient) of 0.77, an RMSE (Root Mean Square Error) of 0.31 K, and a MAE (Mean Absolute Error) of 0.24 K. The evaluation shows that the IADEBAT model has higher accuracy than the original ADEBAT model. Evaluations together with at-test of the MAD (Mean Absolute Deviation) reveal that the IADEBAT model has a significant improvement.

scholarly journals A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

2015 ◽  
Vol 7 (5) ◽  
pp. 6005-6025 ◽  
Author(s):  
Renhua Zhang ◽  
Yuan Rong ◽  
Jing Tian ◽  
Hongbo Su ◽  
Zhao-Liang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vapor Pressure ◽  
Air Temperature ◽  
Regional Scale ◽  
Surface Air Temperature

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

scholarly journals Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

2018 ◽  
Vol 22 (4) ◽  
pp. 2311-2341 ◽  
Author(s):  
Nishan Bhattarai ◽  
Kaniska Mallick ◽  
Nathaniel A. Brunsell ◽  
Ge Sun ◽  
Meha Jain
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Regional Scale ◽  
Absolute Error ◽  
Error Statistics ◽  
Aridity Gradient ◽  
Balance System

Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote-sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman–Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth–Wallace (SW) framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13 core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of −4 %. These error statistics showed relatively better accuracies than a widely used but previous version of the SEB-based Surface Energy Balance System (SEBS) model, which utilized a simple NDVI-based parameterization of surface roughness (zOM), and the PM-based MOD16 ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate ET (PBIAS = −26 %). The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapor pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories, as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient, points to the potential for this simplified analytical model for near-real-time ET mapping from regional to continental scales.

scholarly journals A Spherical Temperature Sensor Array Design for Near-Surface Atmospheric Temperature Studies

2020 ◽  
Vol 37 (8) ◽  
pp. 1497-1506
Author(s):  
Jie Yang ◽  
Qingquan Liu ◽  
Feng Ding ◽  
Renhui Ding
Keyword(s):  
Air Temperature ◽  
Temperature Sensor ◽  
Sensor Array ◽  
Elevation Angle ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
Absolute Error ◽  
Temperature Error ◽  
Near Surface ◽  
Surface Precipitation

AbstractThe observation accuracy of the surface air temperature less than 0.1 K is a requirement, stated by the meteorological and climatological community. However, the accuracy of a temperature sensor inside a shield is affected by a number of factors including solar radiation, wind speed, upwelling longwave radiation, air density, sun elevation angle, sun azimuth angle, underlying surface, precipitation, moisture, structure, and coating of the radiation shield. Due to these factors, the temperature error of the temperature sensor may be much larger than 1 K under adverse conditions. To improve the observation accuracy, this paper proposed a spherical temperature sensor array. A series of analytical calculations based on a computational fluid dynamics (CFD) method is performed to verify the design principle of this sensor array. The calculation results show that the temperature error ratio can be assumed as a constant. To verify the accuracy of this sensor array, simulations and observation experiments are conducted. The simulation results show that the mean difference between the temperature provided by this sensor array and the reference air temperature is 0.072 K. The field experiment results show that a root-mean-square error (RMSE) and a mean absolute error (MAE) between the temperature provided by this sensor array and the reference air temperature are 0.173 and 0.153 K, respectively.

scholarly journals Evaluation of Greenland near surface air temperature datasets

2017 ◽  
Vol 11 (4) ◽  
pp. 1591-1605 ◽  
Author(s):  
J. E. Jack Reeves Eyre ◽  
Xubin Zeng
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Ice Sheet ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Near Surface ◽  
Satellite Retrieval ◽  
Highly Correlated ◽  
Global Reanalysis

Abstract. Near-surface air temperature (SAT) over Greenland has important effects on mass balance of the ice sheet, but it is unclear which SAT datasets are reliable in the region. Here extensive in situ SAT measurements ( ∼  1400 station-years) are used to assess monthly mean SAT from seven global reanalysis datasets, five gridded SAT analyses, one satellite retrieval and three dynamically downscaled reanalyses. Strengths and weaknesses of these products are identified, and their biases are found to vary by season and glaciological regime. MERRA2 reanalysis overall performs best with mean absolute error less than 2 °C in all months. Ice sheet-average annual mean SAT from different datasets are highly correlated in recent decades, but their 1901–2000 trends differ even in sign. Compared with the MERRA2 climatology combined with gridded SAT analysis anomalies, thirty-one earth system model historical runs from the CMIP5 archive reach  ∼  5 °C for the 1901–2000 average bias and have opposite trends for a number of sub-periods.

scholarly journals Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

2020 ◽  
Vol 12 (15) ◽  
pp. 2414
Author(s):  
Xiao Bai ◽  
Lanhui Zhang ◽  
Chansheng He ◽  
Yi Zhu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Northwest China ◽  
Heihe River ◽  
Regression Equations ◽  
Mountainous Areas ◽  
Soil Layers ◽  
Temporal And Spatial

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends

2011 ◽  
Vol 24 (13) ◽  
pp. 3179-3189 ◽  
Author(s):  
Yuyu Ren ◽  
Guoyu Ren
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Image Data ◽  
Temperature Series ◽  
Urbanization Effect ◽  
Temperature Reference ◽  
Weather Stations ◽  
Reference Climate

Abstract In the global lands, the bias of urbanization effects still exits in the surface air temperature series of many city weather stations to a certain extent. Reliable reference climate stations need to be selected for the detection and correction of the local manmade warming bias. The underlying image data of remote sensing retrieval is adopted in this study to obtain the spatial distribution of surface brightness temperature, and the surface air temperature reference stations are determined based on the locations of the weather stations in the remote sensing surface thermal fields. Among the 672 national reference climate stations and national basic weather stations of mainland China, for instance, 113 surface air temperature reference stations are selected for applying this method. Compared with the average surface air temperature series of the reference stations obtained by a more sophisticated method developed in China, this method is proven to be robust and applicable, and can be adopted for the evaluation and adjustment study on the urbanization bias of the currently used air temperature records of surface climate stations in the global lands.

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.

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