Study on air temperature estimation and its influencing factors in a complex mountainous area

Abstract

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

“…In previous studies, LST and NDVI were two important parameters for Ta estimation [47,62]. In this study, NDVI significantly influenced long-term Ta modeling, with a maximum relative importance of 25% in the GLASS combination.…”

“…The potential of the artificial neural network (ANN) model for temperature estimation has also been demonstrated [39,[43][44][45][46]. Runke et al applied the ANN model to achieve high-temperature estimation accuracy in complex mountainous areas [47]. Şahin utilized the ANN model to model monthly average temperatures for 20 cities [48].…”

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

“…This limitation becomes evident in areas with significant variations in elevation and ground cover types, resulting in imprecise temperature predictions. 44 Moreover, conventional image-level regression analyses, considered spatial methods, overlook critical factors affecting temperature variation. 45 These include latitude, longitude, altitude, topography, and the type of underlying surface, with altitude and topography exerting the most pronounced impact on LST.…”

“…While spatial interpolation is relatively straightforward and offers significant practical value, 43 it was observed that IMA largely relies on image time-dependence for data imputation. This limitation becomes evident in areas with significant variations in elevation and ground cover types, resulting in imprecise temperature predictions 44 . Moreover, conventional image-level regression analyses, considered spatial methods, overlook critical factors affecting temperature variation 45 .…”

Comparative Analysis of Sentinel-3 and Terra MODIS Satellite Images for Air Temperature Observation in Denpasar Area

“…Some satellite images with high temporal resolution and equipped with thermal infrared bands are Sentinel-3 satellite images and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. In observing air temperature using remote sensing technology, the first element that can be identified is surface temperature, therefore the surface temperature obtained by remote sensing systems needs to be converted back into air temperature based on satellite imagery [4]. The results of air temperature observations on each satellite have different levels of accuracy, sothe study of the comparison of Sentinel-3 and Terra MODIS satellite images for air temperature observations needs to be studied.…”