A tool for analysis of the influence of the Earth surface soil layer temperature on the inhomogeneity of grain crops development by the Earth remote sensing data

The work is devoted to the analysis of the influence of the earth surface temperature on the inhomogeneity of the agricultural crops development. The aim of the work is to expand the object-relational model for describing the inhomogeneous spatial structure of a spatial object by including surface temperature as one of the key features that allow determining the cause of vegetation heterogeneity, along with relief features, differences in the soil chemical composition and other significant characteristics. Experimental studies are carried out at sites located in Sukhobuzimsky district of Krasnoyarsk Territory, for which agricultural crops (grains) and the their sowing dates are known a priori, which allows stating any facts of the vegetation development deviation from the normative trajectory with reference to the sequence and timing norms of phenological phase changing. Landsat-8 OLI (Operational Land Imager) TIRS (Thermal Infrared Sensor) data are used as initial data for temperature measurements. Objects of research are presented in the form of a polygon map in SHP format. The temperature values are calculated using the algorithm for estimating the earth temperature developed by Weng Q., Lu D. and Schubring J. The surface reflectance values are the NDVI vegetation index values also obtained from the Landsat-8 OLI data that underwent atmospheric correction by the DOS method. The research results are implemented in the form of a software module and integrated into the Earth remote monitoring (ERM) system of SFU Space and Information Technologies Institute (SITI). The results are used within the concept of object-oriented monitoring of spatial objects developed by the team of authors, and represent index images of the surface temperature of objects, as well as vector schematic maps.

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Estimation of Land Surface Temperature Using LANDSAT 8 Satellite Data of Panchkula District, Haryana

Journal of Geography Environment and Earth Science International ◽

10.9734/jgeesi/2020/v24i1030263 ◽

2020 ◽

pp. 47-66

Author(s):

Ravi Kumar ◽

Anup Kumar

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Global Scale ◽

Sensor Data ◽

Landsat 8 ◽

Heat Island ◽

The Earth ◽

Satellite Sensor Data

Land surface temperature (LST) represents hotness of the surface of the Earth at a particular location. Land surface temperature is useful for meteorological, climatological changes, heat island, agriculture, hydrological processes at local, regional and global scale. Presently many satellite sensor data are available for calculation of land surface temperature like Landsat 8 and MODIS. In the present study land surface temperature in Panchkula district of Haryana have been calculated using Landsat 8 satellite data of 5th May 2019 and 28th October 2019. Already available equations were used for computation of LST in the study area. LST in the study area varies from 18°C to 56°C. High LST is observed in cultivation land, urban area while low LST is observed in hilly forest area in the study area. In the study validation of LST could not be done because of not available of temperature data of studied dates, however, the result gives idea of land surface temperature on a particular day and location.

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ESTIMATION OF THE EARTH SURFACE TEMPERATURE BY HIMAWARI-8/AHI

Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) ◽

10.2208/jscejcei.72.ii_1 ◽

2016 ◽

Vol 72 (2) ◽

pp. II_1-II_6 ◽

Cited By ~ 1

Author(s):

Yuuki UCHIDA ◽

Tomohito ASAKA ◽

Takashi NONAKA ◽

Keishi IWASHITA ◽

Toshiro SUGIMURA

Keyword(s):

Surface Temperature ◽

Earth Surface ◽

The Earth

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Satellite-Driven Land Surface Temperature (LST) Using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) Data and Its Association with Built-Up and Green Cover Over Urban Delhi, India

Remote Sensing in Earth Systems Sciences ◽

10.1007/s41976-018-0004-2 ◽

2018 ◽

Vol 1 (3-4) ◽

pp. 63-78 ◽

Cited By ~ 6

Author(s):

Babita Kumari ◽

Mohammad Tayyab ◽

Shahfahad ◽

Salman ◽

Javed Mallick ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Green Cover

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Atmospheric correction of multispectral satellite imagery

Ukrainian journal of remote sensing ◽

10.36023/ujrs.2020.25.170 ◽

2020 ◽

pp. 4-11

Author(s):

Leonid Katkovsky

Keyword(s):

Land Surface ◽

Satellite Image ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Sensor Data ◽

Physical Parameters ◽

Landsat 8 ◽

Optical Parameters ◽

Outgoing Radiation ◽

Correction Technique

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

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PEMANFAATAN CITRA SATELIT LANDSAT 8 UNTUK MEMETAKAN SEBARAN TITIK PANASSEBAGAI INDIKASI PENINGKATAN SUHU PERMUKAAN TANAH DI KABUPATEN KUTAI TIMUR

ZIRAA'AH MAJALAH ILMIAH PERTANIAN ◽

10.31602/zmip.v46i3.5250 ◽

2021 ◽

Vol 46 (3) ◽

pp. 383

Author(s):

Donny Dhonanto ◽

Nurul Puspita Palupi ◽

Ghaisani Salsabila

Keyword(s):

Land Use ◽

Information System ◽

Data Analysis ◽

Geographic Information System ◽

Surface Temperature ◽

Land Surface ◽

Maximum Temperature ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Area Distribution

Transformation of land-use cause forest area decrease that will affect microclimate (weather tends heat), thus hotspot may possible to scattered in that area and raise the transformation of surface temperature. The objective of this research is to determine the indication of surface temperature in the East Kutai District. The advantage of this research is to give information about hotspot area distribution based on land use and relate between hotspots with surface temperature increase so it is supposed to be one of the consider to transform land use in East Kutai District. This research was held from April until May 2019 at the Laboratory of Carthography and Geographic Information System, Faculty of Agriculture, Mulawarman University. This research using calculation of Land Surface Temperature (LST) value to determine the transformation of surface temperature in East Kutai District by data analysis from Landsat-8 OLI/TIRS satellite. Hotspot area distribution adapted to map of land-use so we found the source of the hotspot. The result of the research shows there are about 6 hotspots in land-use of plantation in 2017 and the increase of the surface temperature is not static cause by depending of rainfall in East Kutai District. Increasing of surface temperature in East Kutai District in October 2013 become 22.35 oC (for minimum temperature), whereas in May 2017 become 37.24 oC (for maximum temperature).

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Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i5.1059 ◽

2021 ◽

Vol 12 (5) ◽

pp. 607-617

Author(s):

A. Rajani, Dr. S.Varadarajan

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Andhra Pradesh ◽

Temperature Data ◽

Sensor Data ◽

Spectral Radiance ◽

Landsat 8 ◽

Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

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Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

European Journal of Remote Sensing ◽

10.1080/22797254.2018.1474494 ◽

2018 ◽

Vol 51 (1) ◽

pp. 667-678 ◽

Cited By ~ 44

Author(s):

Subhanil Guha ◽

Himanshu Govil ◽

Anindita Dey ◽

Neetu Gill

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Analytical Study ◽

Landsat 8 ◽

Landsat 8 Oli

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Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques

Geocarto International ◽

10.1080/10106049.2019.1687591 ◽

2019 ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

Rajkumar Dhakar ◽

Vinay Kumar Sehgal ◽

Debasish Chakraborty ◽

Rabi Narayan Sahoo ◽

Joydeep Mukherjee

Keyword(s):

Atmospheric Correction ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Field Scale ◽

Sentinel 2A ◽

Inversion Techniques ◽

And Inversion

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Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

Remote Sensing ◽

10.3390/rs12122065 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2065 ◽

Cited By ~ 1

Author(s):

Feng Xu ◽

Zhaofu Li ◽

Shuyu Zhang ◽

Naitao Huang ◽

Zongyao Quan ◽

...

Keyword(s):

Winter Wheat ◽

Google Earth ◽

Shandong Province ◽

Sensor Data ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Multi Temporal ◽

Wheat Development ◽

Development Phases ◽

Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

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