جستجوی مقالات مرتبط با کلیدواژه « thermal remote sensing » در نشریات گروه « کشاورزی »

Monitoring land surface temperature (LST) using remote sensing data and examining its temporal and spatial variation is important in various studies such as environmental research, land use change, water resources management, and drought monitoring, especially at local scales. Remote sensing data can provide a long-term and continuous spatial coverage of LST. However, LST data retrieved from thermal infrared (TIR) band imagery have a coarser spatial resolution than surface reflectance (SR) data collected from shortwave bands on the same instrument. LST products with high spatial and temporal resolutions are not yet available. Therefore, several downscaling algorithms to produce high-resolution LST maps from sensors with appropriate temporal resolution have been developed recently. In this study, thermal sharpening of land surface temperature obtained from Sentinel-3 satellite images with a spatial resolution of 1 km and temporal resolution of less than 1 day was carried out using the PyDMS model and Sentinel-2 images to produce LST maps with a spatial resolution of 20 meters for Isfahan, Iran. PyDMS is a machine learning algorithm based on decision tree regression that relates the reflectance of high-resolution bands to the LST of the corresponding low-resolution image. The results of this model have been compared against the LST measurements at a depth of 5 cm in three meteorological stations including Isfahan Airport, Isfahan, and Kaboutarbad. Moreover, LST products of MODIS and Landsat-8 have been used to assess the consistency of the sharpened LSTs. The results show that the correlation coefficient is higher than 0.74 in all 3 stations and the RMSE error is equal to 6.7, 4.0 and 15.5 °C in Isfahan, Kabutrabad and the airport, respectively. Moreover, the spatial pattern of the sharpened LST is compatible with the spatial pattern of the LST products of Landsat 8, Sentinel 3 and MODIS. The findings of this study indicate the promising application of the PyDMS algorithm for producing LST maps with a spatial resolution of 20 meters and temporal resolution of fewer than 7 days, though bias correction using in situ LST can improve results. Production of LST maps with both high spatial and temporal resolutions is extremely useful for many practical applications such as soil and crop management practices, evapotranspiration estimation, and irrigation water management.

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.

Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.