Remote sensing-derived land surface...
כתובת: https://www.sciencedirect.com/science/article/pii/S1574954122004198
Spatiotemporal changes in land surface temperature (LST) over South Asia were estimated using MODIS (moderate resolution imaging spectroradiometer) data from 2000 to 2021. We calculated the monthly and annual LST trends and magnitudes by applying the Mann–Kendall test and Sen's slope estimator at both ecoregion and pixel level. More ecoregions experienced daytime cooling than warming. Central and west South Asia showed the highest daytime cooling in December compared to the nighttime warming in the central and northwest in July and September. Nineteen ecoregions demonstrated monthly daytime cooling trends at the 99% confidence level (CL), with the highest record observed in ecoregion ‘Indus Valley desert’ in March with the magnitudes of −0.26 °C/yr. While the monthly and annual nighttime warming magnitude was the maximum in ‘Gissaro-Alai open woodlands’ in December (0.19 °C/yr at 95% CL), and ‘Indus River Delta-Arabian Sea mangroves’ at annual scale (0.06 °C/yr at 99% CL). To understand the influence of large-scale atmospheric oscillations on the trends, we also correlated the estimated LST trends with the selected oscillation indices. Sea surface temperature (SST) Niño 3.4 showed the most significant influence on the trends, where it was positively correlated with 38 ecoregions during nighttime over the year. A better understanding of temperature trends and impacts on South Asia would guide sustainable development and ensures the excessive demands on food, water, and energy supplies coping with the growing population.
מידע נוסף
| שדה | ערך |
|---|---|
| עדכון הנתונים האחרון | 15 באוגוסט 2024 |
| עדכון אחרון של נתוני העל | 20 באוגוסט 2024 |
| מועד היצירה | 15 באוגוסט 2024 |
| תצורה | HTML |
| רישיון | Other (Public Domain) |
| Datastore active | False |
| Has views | True |
| Id | d4e33710-af02-4158-af88-eb1b2cdeece9 |
| Package id | 5c0b5598-8ceb-41fd-9533-7580c4ef65c9 |
| Position | 0 |
| State | active |