Landsat Thermal Infrared Data Research Articles

Integrating Gravimetry Data With Thermal Infra‐Red Data From Satellites to Improve Efficiency of Operational Irrigation Advisory in South Asia

AbstractThe rapid decline of groundwater resources in South Asia due to excessive irrigation during dry season requires awareness of optimal on‐field water requirements. Such information is currently provided on farmer cellphones through an operational Irrigation Advisory System (IAS). To minimize the cost of sending such irrigation advisory texts to farmers while maximizing impact of IAS on groundwater sustainability, we integrated Gravity Recovery and Climate Experiment (GRACE) data with Landsat Thermal Infrared (TIR) Imagery to target regions in greater need of the IAS service. We demonstrated the concept of an improved IAS over eight irrigation districts of the Ganges and Indus basins. The Surface Energy Balance Algorithm for Land (SEBAL) was used to monitor on‐field water consumption (evapotranspiration‐ET) over cropped areas using Landsat TIR data at plot‐scale spatial resolution. Comparison of SEBAL ET with crop water demand from Penman‐Monteith (FAO56) technique quantified the extent of overirrigation at the plot scale and provided a tangible pathway to microtarget the IAS service only to farmers with the largest groundwater use footprint, thereby improving the impact of the IAS service further. Our results suggested that an operational IAS that integrates GRACE and Landsat TIR data on average can save about 85% (80 million m3) of groundwater per dry season for irrigation districts of Northern India and 87% (or 150 million m3) per year for irrigation districts of Eastern Pakistan.

Summer season sea surface temperature changes in the Aegean Sea based on 30years (1989-2019) of Landsat thermal infrared data.

In this research, sea surface temperature (SST) variability for the summer season in the Aegean Sea was analysed over a period of 30years by using the Landsat thermal infrared bands. A total of 88 Landsat 5, 7, and 8 thermal infrared satellite images from June, July, and August were used for each year from 1989 to 2019. To estimate SST from the thermal infrared band data, thermal infrared sensor at-sensor spectral radiance, and thermal infrared sensor top-of-atmosphere brightness temperatures were used. SST pixel values were extracted from thermal images for the 3-month summer season of each year. In order to validate the findings, regression analysis was performed between the Mediterranean Sea Ultra High Resolution SST L4 data and Landsat data for the 2008-2019 period. Regression constant R-squared values were found to be 0.9672 for June, 0.9550 for July, 0.9634 for August, and 0.9634 for all summer seasons. It was calculated that the minimum value of the average SST was 18.44 ± 2.87°C in 1992, and the maximum value of the average SST was 23.45 ± 0.70°C in 2018. According to the Landsat data, over the past 30years, the annual average SST changes were estimated to be 0.11°C, and the total changes of average SST were estimated to be 3.19 ± 1.26°C. As a result of the analysis and the validation, we can see that there is a rising trend in sea surface temperature in the Aegean Sea. However, it is difficult to determine whether this upward trend is related to global climate change.

The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China

To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process.

Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

Spatial–Temporal Variation in Sea Surface Temperature from Landsat Time Series Data Using Annual Temperature Cycle

Zhang, K.; Jiang, T., and Huang, J., 2019. Spatial–temporal variation in sea surface temperature from Landsat time series data using annual temperature cycle. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 58-65. Coconut Creek (Florida), ISSN 0749-0208.Sea surface temperature (SST) plays an important role in aquatic ecosystems and the biogeochemical cycle. Multi-temporal remote-sensing observations may be desirable alternatives to traditional in situ sensors for SST measurement. However, the frequently used low-to-moderate-resolution remote sensors usually cannot identify subtle SST variations in coastal areas due to the pixel radiance contamination caused by shoreline influences. For alleviating this problem, the SST of Jiaozhou Bay (JZB) between 1986 and 2017 was estimated by means of Landsat thermal infrared data and the single-channel retrieval algorithm. The retrieved results were validated by field-measured water temperature and bootstrap method. Then, the estimated SST was divided into five-year intervals and calculated the SST climatology for each period. With use of the annual temperature cycle fitting model, the time series data not only demonstrated the spatial–temporal variation of the water temperature of JZB but also effectively compensated for the lack of remote sensing data due to adverse weather in summer. The estimation results showed that the SST of JZB increased gently during the past 30 years, especially in coastal areas. The increase of SST near artificial facilities was evident, indicating the influence of urbanization and industrialization in coastal area. A correlation analysis of SST and meteorological factors revealed that air temperature influenced SST variation, especially in the central bay, whereas wind speed and precipitation had nearly no influence.

Assessment of Urban Heat Island Patterns in Bangkok Metropolitan Area Using Time-Series of LANDSAT Thermal Infrared Data

Assessment of Urban Heat Island Patterns in Bangkok Metropolitan Area Using Time-Series of LANDSAT Thermal Infrared Data

Summer surface temperature distribution analysis of Mykolayiv city based on the Landsat series thermal infrared data

The surface temperature distribution analysis of the condition and characteristics within one of the industrial centers of the south of Ukraine, Mykolaiv, from 1985 to 2018 was carried out on the basis of Landsat thermal infrared data processing. Temporal development of surface temperature distribution was investigated both for the entire territory of the city and for individual profiles crossing the diverse landscape and functional city areas. Significant surface temperature distribution divergences during temporal variations in different, but steady landscapes and functional conditions have been stated. Abnormally high temperatures are found within industrial zones, reaching 40–43оC. The water temperatures of the Southern Bug, Ingula and Lake Liski are the lowest and are determined by the amount of water mass. The highest temperature oscillation (up to 15oC) during the research period, was recorded on the artificially aggradated sands within floodplain of the Southern Bug, after the residential building “Namyv” was built and within the “Zhovtneve” reservoir, after its draining. The main types of residential development of the city — a medium, low-rise buildings and private cottages surrounded by trees and lawns, forming a stable temperature background. Limited distribution of impermeable surfaces, significant planting, intensive watering resulted in the formation of a negative thermal anomaly within the city, a peculiar cool oasis against a hot background of steppe agro-landscapes.

The effect of thermal radiation from surrounding terrain on glacier surface temperatures retrieved from remote sensing data: A case study from Qiyi Glacier, China

Large-scale spatiotemporal variations in land surface temperature (LST) can be derived from satellite observations, particularly for remote high-elevated mountain glaciers where in situ observations are scarce. Our previous LST retrieval study on Qiyi Glacier of China employed a generalized single-channel algorithm and Landsat thermal infrared data, but a clear LST summertime overestimation was observed, particularly on the glacier tongue. Here we extend our previous study by calculating the thermal radiance on Qiyi Glacier that is emitted from the surrounding terrain, and incorporating this thermal radiation term into two generalized single-channel LST algorithms to analyze its influence on the errors of glacier surface temperatures retrieved from remote sensing data. In addition, we also discuss other possible causes of this overestimation in LST retrieval. The key findings of our study are as follows. (1) The surrounding terrain provides nonnegligible summertime thermal radiation to the glacier in the Landsat7 band 6 spectrum range (10.4–12.5 μm) that is 2.6 times larger than the atmospheric down-welling thermal radiance on the glacier tongue. The retrieved LSTs yield an overestimation of up to 0.28 K when the effect of thermal radiation from the surrounding slopes is not considered. (2) The average errors due to the surrounding topographic thermal radiation and the cubic convolution resampling method (CC method) employed in the Landsat7 thermal infrared data processing are only 0.07 K and − 0.01 K, respectively. These are much smaller than the average LST errors (1.79 K), indicating that the neither the surrounding topographic thermal radiation nor the CC method are primary factors in the retrieved LST overestimation. (3) The sole dependence of the generalized single-channel algorithm on the atmospheric water vapor content may therefore be the most likely cause of this overestimation, as high-altitude regions generally possess low atmospheric water contents. Radiative transfer models (e.g., MODTRAN) may provide a solution to this problem by simulating the influence of other atmospheric constituents on LST retrieval under conditions of low water vapor, which can then indicate any additional input variables that should be incorporated into the LST algorithm.

Downscaling land surface temperature data by fusing Suomi NPP-VIIRS and landsat-8 TIR data

ABSTRACTLand surface temperature (LST) is a key parameter of great interest in many remote sensing applications. However, no single satellite system can produce thermal infrared (TIR) images at both high spatial and temporal resolution to retrieve LST. Various algorithms have been developed to enhance the spatial or temporal resolution of TIR data in the past decades. Among them, the Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) model is one of the most widely used algorithms for fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To our knowledge, Visible Infrared Imaging Radiometer Suite (VIIRS) TIR data have not yet been used in thermal downscaling with Landsat-8 TIR data. This study aims to generate daily LST images at Landsat-8 resolution (100 m) by fusing VIIRS and Landsat-8 TIR data for the first time with the SADFAT algorithm. The results indicate that the prediction accuracy for the study area ranged from 1.1 K to 1.4 K, which suggests that VIIRS data can be used as a good alternative for MODIS data for generating daily LST images by fusing Landsat TIR data.

Retrieving water surface temperature from archive LANDSAT thermal infrared data: Application of the mono-channel atmospheric correction algorithm over two freshwater reservoirs

Water surface temperature is a key element in characterizing the thermodynamics of waterbodies, and for irregularly-shaped inland reservoirs, LANDSAT thermal infrared images are the best alternative yet for the retrieval of this parameter. However, images must be corrected mainly for atmospheric effects in order to be fully exploitable. The objective of this study is to validate the mono-channel correction algorithm for single-band thermal infrared LANDSAT data as put forward by Jiménez-Muñoz et al. (2009). Two freshwater reservoirs in continental France were selected as study sites, and best use was made of all accessible image and field data. Results obtained are satisfactory and in accordance with the literature: r2 values are above 0.90 and root-mean-square error values are comprised between 1 and 2°C. Moreover, paired Wilcoxon signed rank tests showed a highly significant difference between field and uncorrected image data, a very highly significant difference between uncorrected and corrected image data, and no significant difference between field and corrected image data. The mono-channel algorithm is hence recommended for correcting archive LANDSAT single-band thermal infrared data for inland waterbody monitoring and study.

Landfill monitoring using remote sensing: a case study of Glina, Romania

Landfill monitoring is one of the most important components of waste management. This article presents a case study on landfill monitoring using remote sensing technology. The study area was the Glina landfill, one of the largest municipal waste disposal sites in Romania. The methodology consisted of monitoring the differences of temperature computed for several distinct waste disposal zones with respect to a ground reference area, all of them located within the landfill site. The remote sensing data used were Landsat satellite multi-temporal data. The differences of temperature were computed using Landsat thermal infrared data. The study confirmed the use of multi-temporal Landsat imagery as a complementary data source.

Research on Thermal Discharge Pollution of Xiangshan Power Plant Based on Remote Sensing

Thermal power technology has brought great convenience for human electricity energy demand, but thermal discharge from thermal power plants has caused great harm to the coastal environment. Therefore, it’s important to strengthen the monitoring of thermal pollution from power plants for guarantee the normal operation of coastal environment and ecological system. Thermal infrared remote sensing technology provides a new measure for monitoring the thermal discharge. In this paper, we use mono-window algorithm and Landsat thermal infrared data to retrieved the sea surface temperature around Xiangshan Power Plants, and achieved the result of 1~5.4°C temperature rise in 106.52km2 in Xiangshan Harbor, revealed the spatial distribution regularities of thermal discharge and discussed the influence of thermal discharge on costal environment.

Urban heat island lesson

Among the most modified environments on the Earth's surfaces are the places where humans have congregated and built cities. Over 60% of the world's population now resides in urban centres. Because of their densely settled conditions, urban areas are generally warmer than their surrounding rural environments, resulting in what are referred to as urban heat islands (UHIs). This paper describes an instructional lesson designed to show students how to measure and analyse temperature variations within an urban environment using Landsat thermal infrared data.

Urban Air Pollution Patterns, Land Use, and Thermal Landscape: An Examination of the Linkage Using GIS

This article investigates the relationship of local air pollution pattern with urban land use and with urban thermal landscape using a GIS approach. Ambient air quality measurements for sulfur dioxide, nitrogen oxide, carbon monoxide, total suspended particles, and dust level were obtained for Guangzhou City in South China between 1981 and 2000. Landsat TM images and aerial photo derived maps were used to examine city's land use and land cover at different times and changes. Landsat thermal infrared data were employed to compute land surface temperatures and to assess urban thermal patterns. Relationships among the spatial patterns of air pollution, land use, and thermal landscape were sought through GIS and correlation analyses. Results show that the spatial patterns of air pollutants probed were positively correlated with urban built-up density, and with satellite derived land surface temperature values, particularly with measurements taken during the summer. It is suggested that further studies investigate the mechanisms of this linkage, and that remote sensing of air pollution delves into how the energy interacts with the atmosphere and the environment and how sensors see pollutants. Thermal infrared imagery could play a unique role in monitoring and modeling atmospheric pollution.

Observing air quality over the city of Nantes by means of Landsat thermal infrared data

This article investigates the potentials of satellite-made observations for the mapping of air quality parameters. Ground measurements of black particulates, sulphur dioxide and other pollutants have been measured in the city of Nantes and are compared with coincident Landsat TM6 data. It is found that the amount in black particulates is highly correlated to the apparent temperature observed by satellite. Some of the possible mechanisms for this correlation are investigated. The influence of the pixel size on this correlation is discussed in view of previous studies. Preliminary explanations are given. The mapping of the spatial distribution of the black particulates is possible from remotely sensed data. It is not very accurate and possible improvements are discussed. Finally, it is shown that pollution level should be taken into account in any use of satellite data for urban area classification purposes.