scholarly journals A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017

2020 ◽  
Vol 12 (4) ◽  
pp. 2555-2577
Author(s):  
Bing Zhao ◽  
Kebiao Mao ◽  
Yulin Cai ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Meteorological Station ◽  
Western China ◽  
Drought Monitoring ◽  
Ground Observation ◽  
Station Data ◽  
Reconstruction Model

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).

scholarly journals A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017

2019 ◽  
Author(s):  
Bing Zhao ◽  
Kebiao Mao ◽  
Yulin Cai ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Meteorological Station ◽  
Drought Monitoring ◽  
Thermal Infrared Remote Sensing ◽  
Cloud Coverage ◽  
Station Data ◽  
Reconstruction Model

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperatures over large areas. However, there are many missing and low-quality values in satellite-based LST data caused by cloud coverage exceeding 60 % of the global surface every day. This article presents a unique LST dataset in China for 2003–2017, which filters and removes missing values and poor-quality LST pixel values contaminated by clouds from raw LST images and retrieves real surface temperatures under cloud coverage by a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the true LST under cloud coverage, and then the data performance is further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with the in situ observations. For the six natural subregions with different climatic conditions in China, the RMSE ranges from 1.24 °C to 1.58 °C, the MAE varies from 1.23 °C to 1.37 °C, and the R2 ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003–2017, the overall annual mean LST in China shows a weak increase. Moreover, the warming trend was remarkably unevenly distributed over China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region (slope > 0.10, R > 0.71, P  <0.05), and a strong cooling trend was also observed in some parts of the Northeast Region. Seasonally, there was significant warming in the western part in winter, which was most pronounced in December. The reconstructed dataset exhibited significant improvements and can be used for the spatiotemporal evaluation of LST and high temperature and drought monitoring studies. The data are published in the Zenodo at https://doi.org/10.5281/zenodo.3378912 (Zhao et al., 2019).

scholarly journals Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

2020 ◽  
Vol 12 (3) ◽  
pp. 488 ◽  
Author(s):  
Nusseiba NourEldeen ◽  
Kebiao Mao ◽  
Zijin Yuan ◽  
Xinyi Shen ◽  
Tongren Xu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Observation Data ◽  
The West ◽  
Thermal Infrared Remote Sensing ◽  
Ground Station ◽  
Station Data ◽  
Effects Of Temperature ◽  
Reconstruction Model

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.

scholarly journals SPATIAL AND TEMPORAL ANALYSIS ON THE EVOLUTION CHARACTERISTICS OF LAND SURFACE TEMPERATURE IN THE PEARL RIVER DELTA REGION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 713-720
Author(s):  
Q. Y. Huang ◽  
J. L. Jing
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Pearl River Delta ◽  
Pearl River ◽  
Pearl River Delta Region ◽  
Delta Region ◽  
The Pearl River Delta ◽  
The Pearl River

Abstract. With the rapid development of social economy and the continuous progress of urbanization in the Pearl River Delta region, the urban surface temperature had a significant change, which seriously affects people's normal life. The MOD11B3 LST_Day_6km product and other auxiliary data were selected as data sources, and the temperature-standard deviation method, the profile method and the coefficient method were used to analyze the spatial and temporal evolution characteristics of land surface temperature in the Pearl River Delta Region from 2000–2015. The research results showed that: (1) the maximum surface temperature from 2000 to 2015 in June, July and August showed an upward trend, while the minimum temperature showed a fluctuation downward trend, and the average temperature changed gently. (2) the surface temperature had a spatial pattern of low on all sides, and high in the middle. The high temperature zone were mainly distributed in Guangzhou, Dongguan, Foshan and Shenzhen cities and the surrounding areas. And the high temperature areas were more concentrated and showed a spreading direction of East and South. (3) The profile analysis showed that the surface temperature had a double-peak feature along the profile line, and the temperature decreased with the distance farther away from the center of the high temperature zone. (4) The correlation coefficient between land surface temperature and elevation, vegetation index were −0.551 and −0.734 respectively, both passing p < 0.01 significance test, indicating that the surface temperature had a significant negative correlation relationship with elevation and vegetation index. Therefore, strengthen urban greening is of great significance to the healthy development of the city.

scholarly journals Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1778
Author(s):  
Soo-Jin Lee ◽  
Nari Kim ◽  
Yangwon Lee
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Crop Yield ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The State ◽  
Agricultural Drought ◽  
Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

2010 ◽  
Vol 23 (3) ◽  
pp. 618-633 ◽  
Author(s):  
Arnon Karnieli ◽  
Nurit Agam ◽  
Rachel T. Pinker ◽  
Martha Anderson ◽  
Marc L. Imhoff ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Growing Season ◽  
Primary Factor ◽  
Drought Monitoring ◽  
Negative Slope ◽  
Limiting Factor ◽  
Vegetation Growth ◽  
Wide Range

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

scholarly journals Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations

2019 ◽  
Vol 11 (14) ◽  
pp. 1704 ◽  
Author(s):  
Donglian Sun ◽  
Yu Li ◽  
Xiwu Zhan ◽  
Paul Houser ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Daily Basis ◽  
Drought Monitoring ◽  
Stress Index ◽  
Coarse Spatial Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions

Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.

scholarly journals Identification of Urban Heat Islands &Its Relationship withVegetation Cover: A Case Study of Colombo & Gampaha Districts in Sri Lanka

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
G.M.T.S. Fernando
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Urban Heat

Global Warming is a major environmental problem that all kind of organisms has been affected at present. Urban Heat Island (UHI) is one of primary impacts of Global Warming. UHI is a phenomenon that the temperature of urban area is higher than surrounding rural areas or suburban areas. This increasing trend of temperature in urban areas affects many environmental entities such as air quality, water resources, habitats behaviors and climate changes. The most remarkable incident that relate with UHI is the difference of thermal properties of the surfaces. Many countries experience the consequences of Urban Heat Islands in many aspects such as economic, health, social and environmental affects. Thus to mitigate such impacts of UHI, it is very important to identify the main reasons behind this. In this paper UHIs in Colombo, Gampaha Districts and the relationship between UHI and vegetation cover were analyzed based on Landsat 8, 30m resolution data. Land Surface Temperature was derived from Landsat thermal Infrared band through several equations of United State Geological Survay (USGS) guidelines using Arc GIS 10. Conversion of Digital Number (DN) values to Top of Atmosphere (TOA) Radiance, Conversion of TOA Radiance to Satellite Brightness temperature and final calculation of Land Surface Temperature considering land surface emissivity are the steps that had been done for the analysis. Vegetation cover was derived by using vegetation index with the Red and Near Infra Red bands. The result shows that the land high surface temperature directly relates with the urbanized regions where vegetation cover is very less. High temperature difference could be identified that cause to arise the urban heat island effects in Colombo & Gampaha districts. There is a strong linearly negative correlation with correlation coefficient value of -0.742 between land surface temperature and vegetation cover. 78.8 km2 (including water) of total area had been identified as NDVI value less than 0.1. And extent of high temperature area was 74.12 km2 where temperature more than 27oC at 10.22am. The area in temperature range of 25-27 was 464.95km2 and area in NDVI value range 0.1-0.2 was 333.04 km2. 1471.1 km2 was identified as NDVI value between 0.3-0.4 and the area at low temperature was 1529 km2where temperature less than 25oC. According to this results, high temperature at non-vegetated areas and low temperature at vegetated areas could be noted very clearly. This is probably due to the ecological function of vegetation that lay down the surface temperature from high evapotranspiration. Vegetated areas are mostly sensed with surface temperature.Thus research output can be useful for policy-makers and planners of development projects such as Western province Megapolis project as well as for general public to understand the urban heat island effects and importance of vegetation cover to mitigate such impacts.

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