Characteristics of changes in the moisture content of the tundra cover in the area of the Bovanenkovo field using Landsat satellite data

2021 ◽  
pp. 30-48
Author(s):  
S.G. Kornienko
Keyword(s):  
Moisture Content ◽  
Surface Temperature ◽  
Oil And Gas ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Surface Moisture ◽  
Ndvi Index ◽  
The Difference ◽  
Landsat Satellite

By the case of the area of a long-term technogenic load of the Bovanenkovo oil and gas condensate field, the possibility of monitoring the moisture content of the tundra cover near technical objects according to the Landsat 5 and Landsat 8 satellites is shown. The work used multispectral images from 1990, 1994, 2013 and 2020. The analysis was carried out on images characterizing the Earth’s surface temperature, surface moisture (NDWI index) and chlorophyll content in the canopy (NDVI index). Characterization and mapping of changes in the moisture content of the cover were carried out according to the difference between the images of 1990 and 2020. Variations in the NDVI index allow us to identify the reasons for these changes. The technogenic impact is shown to lead to an increase in the surface temperature and a decrease in the NDWI and NDVI values, which indicates the predominance of drainage processes and a decrease in the volume of living phytomass near technical objects. Such transformations are less dangerous for objects in comparison with waterlogging of the cover, however, they contribute to an increase in the emission of carbon-containing gases, since an increase in temperature and a decrease in surface moisture, as a rule, lead to degradation of the permafrost and an increase in the depth of the thawed layer.

scholarly journals ESTIMATION OF SOIL MOISTURE AND EARTH’S SURFACE TEMPERATURE USING LANDSAT-8 SATELLITE DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 327-330
Author(s):  
M. Entezari ◽  
A. Esmaeily ◽  
S. Niazmardi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Landsat 8 ◽  
Surface Soil Moisture ◽  
Surface Moisture ◽  
Moisture Estimation

Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.

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

2020 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Jingjing Yang ◽  
Si-Bo Duan ◽  
Xiaoyu Zhang ◽  
Penghai Wu ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Profiles ◽  
Atmospheric Profile

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.

scholarly journals THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY

2017 ◽  
Vol 12 (1) ◽  
pp. 13 ◽  
Author(s):  
Kuncoro Teguh Setiawan ◽  
Yennie Marini ◽  
Johannes Manalu ◽  
Syarif Budhiman
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Image Data ◽  
Landsat 8 ◽  
Calculation Error ◽  
Sensing Technology ◽  
The Difference ◽  
Landsat Satellite

Remote sensing technology can be used to obtain information bathymetry. Bathymetric information plays an important role for fisheries, hydrographic and navigation safety. Bathymetric information derived from remote sensing data is highly dependent on the quality of satellite data use and processing. One of the processing to be done is the atmospheric correction process. The data used in this study is Landsat 8 image obtained on June 19, 2013. The purpose of this study was to determine the effect of different atmospheric correction on bathymetric information extraction from Landsat satellite image data 8. The atmospheric correction methods applied were the minimum radiant, Dark Pixels and ATCOR. Bathymetry extraction result of Landsat 8 uses a third method of atmospheric correction is difficult to distinguish which one is best. The calculation of the difference extraction results was determined from regression models and correlation coefficient value calculation error is generated.

scholarly journals Moisture content of cereals at harvesting time by comparing microclimate values and standard weather data

1993 ◽  
Vol 41 (3) ◽  
pp. 167-178
Author(s):  
A.J. Atzema
Keyword(s):  
Moisture Content ◽  
Surface Temperature ◽  
Air Temperature ◽  
Weather Conditions ◽  
Dew Point ◽  
Weather Data ◽  
Daily Cycle ◽  
The Difference ◽  
High Equilibrium ◽  
Screen House

The moisture content of wheat and barley together with the weather elements were measured at 3 different experimental sites in the Netherlands in 1990-91. The difference in the dew point temperature in the screen[house] and in the field was small. However, the differences between air temperature in the screen and those at different heights in wheat and in barley stands were considerable. In daytime the surface temperature of barley was higher than that of wheat under the same weather conditions as a result of a higher absorbtion coefficient. Both for wheat and barley, the maximum difference between the calculated moisture content was 0.5%, using the air temperature at 1.5 m height from the nearest standard weather station and the surface temperature of the spikes. Barley had a greater daily cycle in the moisture content of the grains than wheat as a result of a high equilibrium moisture content during the night and a low one in daytime.

scholarly journals Water deficit detection in sugarcane using canopy temperature from satellite images

2020 ◽  
pp. 400-407
Author(s):  
Rodrigo Moura Pereira ◽  
Derblai Casaroli ◽  
Lucas Melo Vellame ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Water Deficit ◽  
Relative Error ◽  
Land Surface ◽  
Canopy Temperature ◽  
Landsat 8 ◽  
Temperature Estimation ◽  
Calibration Uncertainty ◽  
Limited Application ◽  
The Difference

Water deficit (WD) is the main yield gap for sugarcane in Midwest Brazil. Thus, WD detection is essential to quantify yield losses, but field detection requires measurement of soil water content over large areas. In this study, we tested leaf temperature (TL) and land surface temperature (TS) to detect WD in a commercial sugarcane area. The area is located in the central region of Goiás State, Brazil. According to Köppen classification, the climate of the region is Aw (humid tropical, with rainy summer and dry winter). The soil is a Ferralsol (clayey texture). TL was measured by a portable infrared thermometer, and TS was obtained using a spectral image from Landsat 8. Both TL and TS measurements occurred between 28 Jan and 24 Aug 2014 (298-506 DAP). The water balance identified periods of water deficit (WD) and surplus (WS). The difference between TL Ta was greater than zero (7.11 °C) in WD periods and lower than zero (-2.18 °C) in WS periods. The difference between TS-Ta, in turn, ranged from -0.66 °C to 4.06 °C, but not following the tendency of WD or WS, which is associated with a relative error between TL and TS near 20% for some date. The TS Ta difference detected soil WD or WS when the relative error was low (362 and 410 DAP) and under higher WD (506 DAP) and WS (394 DAP). This way, TL was able to detect WD and WS along sugarcane growth, while TS showed limited application, requiring improvement based on surface properties to reduce the error in relation to TL. Furthermore, bands 10 and 11 are recommended for surface temperature estimation. Calibration uncertainty increases when the band 11 is used alone, being this band more affected by the absorption of radiation by the atmospheric water vapor, which implies larger errors related to the atmospheric profile in the acquisition of surface temperature.

scholarly journals SOIL MOISTURE ANALYSIS OF LALITPUR DISTRICT UTTAR PRADESH INDIA USING LANDSAT AND SENTINEL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 351-356
Author(s):  
S. K. Yadav ◽  
P. Singh ◽  
S. P. S. Jadaun ◽  
N. Kumar ◽  
R. K. Upadhyay
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Uttar Pradesh ◽  
Sensor Data ◽  
Landsat 8 ◽  
Available Water Content ◽  
Study Results

<p><strong>Abstract.</strong> Soil moisture is the available water content within the voids of soil particles. Remote sensing and GIS technique provide an advance &amp; better information to extract the soil moisture of Lalitpur district Uttar Pradesh. The Landsat-8 OLI+TIRS (Optical Level Imager +Thermal Infrared Sensor) data (2013&amp;ndash;18) and Sentinel 2A &amp; 2B data (2015&amp;ndash;18) was used to retrieve soil moisture content for the period 2013&amp;ndash;2018. The optical and thermal bands were used to retrieve Land surface temperature (LST), NDVI and NDWI of Lalitpur district for the different years. Using land surface temperature encompasses with NDVI and NDWI the moisture content of the soil was estimated for the study area.Using Advance Microwave Scanning Radiometer-2, (2013&amp;ndash;18) (AMSR-2) data and measurement of soil moisture through in-situ (Field) soil samples collections for soil moisture estimation was used to check the accuracy of the output resulted from Landsat and Sentinel data. This study results that; the output obtained from Landsat-8 in comparison to Sentinel data provide an accurate and better information of soil moisture at a high resolution.</p>

Examination of moisture content variation within an operational wet deck

TAPPI Journal ◽  
2013 ◽  
Vol 12 (1) ◽  
pp. 45-50 ◽  
Author(s):  
LAURENCE SCHIMLECK ◽  
KIM LOVE-MYERS ◽  
JOE SANDERS ◽  
HEATH RAYBON ◽  
RICHARD DANIELS ◽  
...  
Keyword(s):  
Moisture Content ◽  
Southeastern United States ◽  
Forest Products ◽  
Time Domain Reflectometry ◽  
Water Application ◽  
Wood Moisture ◽  
International Paper ◽  
Content Variation ◽  
Moisture Variation

Many forest products companies in the southeastern United States store large volumes of roundwood under wet storage. Log quality depends on maintaining a high and constant wood moisture content; however, limited knowledge exists regarding moisture variation within individual logs, and within wet decks as a whole, making it impossible to recommend appropriate water application strategies. To better understand moisture variation within a wet deck, time domain reflectometry (TDR) was used to monitor the moisture variation of 30 southern pine logs over an 11-week period for a wet deck at the International Paper McBean woodyard. Three 125 mm long TDR probes were inserted into each log (before the deck was built) at 3, 4.5, and 7.5 m from the butt. The position of each log within the stack was also recorded. Mixed-effects analysis of variance (ANOVA) was used to examine moisture variation over the study period. Moisture content varied within the log, while position within the stack was generally not significant. The performance of the TDR probes was consistent throughout the study, indicating that they would be suitable for long term (e.g., 12 months) monitoring.

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