Basic Studies on the Estimation of Daily Mean Air Temperature by Using Surface Temperature Derived from Satellite Data-Relationship between Daily Mean Air. Temperature and Surface Temperature Measured on the Ground.

AbstractCurrent trends show a rise in Arctic surface and air temperatures, including over the Greenland ice sheet where rising temperatures will contribute to increased sea-level rise through increased melt. We aim to establish the uncertainties in using satellite-derived surface temperature for measuring Arctic surface temperature, as satellite data are increasingly being used to assess temperature trends. To accomplish this, satellite-derived surface temperature, or land-surface temperature (LST), must be validated and limitations of the satellite data must be assessed quantitatively. During the 2008/09 boreal winter at Summit, Greenland, we employed data from standard US National Oceanic and Atmospheric Administration (NOAA) air-temperature instruments, button-sized temperature sensors called thermochrons and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument to (1) assess the accuracy and utility of thermochrons in an ice-sheet environment and (2) compare MODIS-derived LSTs with thermochron-derived surface and air temperatures. The thermochron-derived air temperatures were very accurate, within 0.1 ± 0.3°C of the NOAA-derived air temperature, but thermochron-derived surface temperatures were ∼3°C higher than MODIS-derived LSTs. Though surface temperature is largely determined by air temperature, these variables can differ significantly. Furthermore, we show that the winter-time mean air temperature, adjusted to surface temperature, was ∼11°C higher than the winter-time mean MODIS-derived LST. This marked difference occurs largely because satellite-derived LSTs cannot be measured through cloud cover, so caution must be exercised in using time series of satellite LST data to study seasonal temperature trends.

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Basic Studies on the Estimation of Climate Values (Normals for Decade Air Temperature) at Unit Cell by Using Satellite Data

Journal of Agricultural Meteorology ◽

10.2480/agrmet.59.131 ◽

2003 ◽

Vol 59 (2) ◽

pp. 131-140 ◽

Cited By ~ 1

Author(s):

Xiufeng WANG ◽

Tomomi TAKEDA

Keyword(s):

Air Temperature ◽

Satellite Data ◽

Unit Cell ◽

Basic Studies

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Is summer sea surface temperature over the Arctic Ocean connected to winter air temperature over North America?

Climate Research ◽

10.3354/cr01412 ◽

2016 ◽

Vol 70 (1) ◽

pp. 19-27

Author(s):

M Ogi ◽

S Rysgaard ◽

DG Barber ◽

T Nakamura ◽

B Taguchi

Keyword(s):

North America ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Arctic Ocean ◽

Air Temperature ◽

Sea Surface ◽

The Arctic ◽

The Arctic Ocean ◽

Winter Air Temperature

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Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0002 ◽

2018 ◽

Vol 18 (1) ◽

pp. 14-22 ◽

Cited By ~ 1

Author(s):

Georgiana Grigoraș ◽

Bogdan Urițescu

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Hot Spots ◽

Land Surface ◽

Hot Spot ◽

Monitoring Network ◽

Residential Areas ◽

Hotspot Analysis ◽

The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

Remote Sensing ◽

10.3390/rs13061177 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1177

Author(s):

Peijuan Wang ◽

Yuping Ma ◽

Junxian Tang ◽

Dingrong Wu ◽

Hui Chen ◽

...

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Frost Damage ◽

Estimation Model ◽

Spring Frost ◽

Tea Plants ◽

Elevation Model ◽

Minimum Air Temperature

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

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Influence of North Atlantic sea surface temperature anomalies on springtime surface air temperature variation over Eurasia in CMIP5 models

Climate Dynamics ◽

10.1007/s00382-021-05826-5 ◽

2021 ◽

Author(s):

Shangfeng Chen ◽

Renguang Wu ◽

Wen Chen

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Temperature Variation ◽

Air Temperature ◽

North Atlantic ◽

Surface Air Temperature ◽

Sea Surface ◽

Cmip5 Models ◽

Temperature Anomalies ◽

Sea Surface Temperature Anomalies

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Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs11172016 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2016

Author(s):

Lijuan Wang ◽

Ni Guo ◽

Wei Wang ◽

Hongchao Zuo

Keyword(s):

Surface Temperature ◽

Diurnal Variation ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Regional Scale ◽

Pso Algorithm ◽

Variation Characteristics ◽

Split Window Algorithm ◽

Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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