DOI: 10.57760/sciencedb.11652
title: Nighttime Land Surface Temperature Images of Danjiang River Basin
title: Nighttime Land Surface Temperature Images of Danjiang River Basin

description: 1.nighttime LST19881220(1) Sensor of original remote sensing image: Landsat 5 TM.(2) Time of original remote sensing image: 21:57 Beijing time on December 20, 1988.(3) Location: it basically covers the entire Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: basically, we adopted the mono-window algorithm[1],However, there are two improvements listed below.The estimation of land surface emissivity: in previous remote sensing inversion of daytime LST, the estimation of land surface emissivity was based on visible and near-infrared images synchronized with thermal infrared images. However, when imaging at night in the Landsat thermal infrared band, the visible light and near-infrared bands cannot be imaged simultaneously. Considering the land surface emissivity is a relatively stable surface feature property in the time domain, it will not change strongly in a short period of time and has a certain interannual periodic repeatability, so the visible light and near-infrared images of adjacent dates during the day are used to approximate the nighttime emissivity.The air temperature data at the imaging time of the sensor is very important for LST inversion. However, the weather stations record the lowest and highest air temperatures of the day, which are not the time of imaging. To solve this problem, we introduced the intraday air temperature variation equation proposed in Reference [2], so as to calculate the temperature at the imaging time of the sensor based on the lowest and highest air temperature data recorded by the weather stations.(6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius 2. nighttime LST20101202(1) Sensor of original remote sensing image: Terra ASTER(2) Time of original remote sensing image: 22:38 Beijing time on December 2, 2010.(3) Location: it covers 50.07% of the Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: it was produced online through the Earthdata system of the Land Processes Distributed Active Archive Center (LP DAAC).(6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius 3. nighttime LST20181122(1) Sensor of original remote sensing image: Terra ASTER(2) Time of original remote sensing image: 22:39 Beijing time on November 22, 2018.(3) Location: it covers 50.07% of the Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: it was produced online through the Earthdata system of the Land Processes Distributed Active Archive Center (LP DAAC). (6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius References[1] Z. Qin, A. Karnieli, and P. Berliner, A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, vol. 22, no. 18, pp. 3719-3746, Sep. 2001. [2] F. Xu, and C. Ma, Daily change and math-expression method of outside temperature in winter for greenhouse environmental analysis, Transactions of the Chinese Society of Agricultural Engineering, vol. 29, no. 12, pp. 203-209, Jun. 2013. (in Chinese with English abstract)
description: 1.nighttime LST19881220(1) Sensor of original remote sensing image: Landsat 5 TM.(2) Time of original remote sensing image: 21:57 Beijing time on December 20, 1988.(3) Location: it basically covers the entire Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: basically, we adopted the mono-window algorithm[1],However, there are two improvements listed below.The estimation of land surface emissivity: in previous remote sensing inversion of daytime LST, the estimation of land surface emissivity was based on visible and near-infrared images synchronized with thermal infrared images. However, when imaging at night in the Landsat thermal infrared band, the visible light and near-infrared bands cannot be imaged simultaneously. Considering the land surface emissivity is a relatively stable surface feature property in the time domain, it will not change strongly in a short period of time and has a certain interannual periodic repeatability, so the visible light and near-infrared images of adjacent dates during the day are used to approximate the nighttime emissivity.The air temperature data at the imaging time of the sensor is very important for LST inversion. However, the weather stations record the lowest and highest air temperatures of the day, which are not the time of imaging. To solve this problem, we introduced the intraday air temperature variation equation proposed in Reference [2], so as to calculate the temperature at the imaging time of the sensor based on the lowest and highest air temperature data recorded by the weather stations.(6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius 2. nighttime LST20101202(1) Sensor of original remote sensing image: Terra ASTER(2) Time of original remote sensing image: 22:38 Beijing time on December 2, 2010.(3) Location: it covers 50.07% of the Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: it was produced online through the Earthdata system of the Land Processes Distributed Active Archive Center (LP DAAC).(6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius 3. nighttime LST20181122(1) Sensor of original remote sensing image: Terra ASTER(2) Time of original remote sensing image: 22:39 Beijing time on November 22, 2018.(3) Location: it covers 50.07% of the Danjiang River Basin.(4) Spatial resolution: 30 * 30 m. (5) method: it was produced online through the Earthdata system of the Land Processes Distributed Active Archive Center (LP DAAC). (6) Format: ESRI GRID(7) Pixel Type: Floating point(8) Unit: Degrees Celsius References[1] Z. Qin, A. Karnieli, and P. Berliner, A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, vol. 22, no. 18, pp. 3719-3746, Sep. 2001. [2] F. Xu, and C. Ma, Daily change and math-expression method of outside temperature in winter for greenhouse environmental analysis, Transactions of the Chinese Society of Agricultural Engineering, vol. 29, no. 12, pp. 203-209, Jun. 2013. (in Chinese with English abstract)