The Synergistic Effect between Precipitation and Temperature for the NDVI in Northern China from 2000 to 2018

Abstract

Based on monthly precipitation (P), temperature (T) data, and remote sensing images collected from March 2000 to February 2019, this article was constructed to reveal the synergistic effect between P and T for the NDVI in northern China qualitatively and quantitatively by using a one-variable linear regression, the coefficient of variation, multivariate correlation coefficients, and a geodetector. The results show that the NDVI in the study area decreased from 2000 to 2012, increased from 2013 to 2018, decreased in the west, and increased in the east of Northern China. Overall, the NDVI, P, and the average maximum temperature (Tmax) had the strongest multivariate correlations (approximately 43.4% of the total study area passed the 95% confidence level significance test), followed by the average temperature (Tave) and average minimum temperature (Tmin). The explanatory power of the synergistic effect between P and Tmax for the NDVI was the strongest, with the value of explanatory power varying from 0.41 to 0.81, followed by Tave and Tmin. Spatially, the explanatory power of the synergistic effect between P and T for the NDVI was strengthened overall in the study area from northwest to southeast. The annual change rate of the explanatory power showed that the overall explanatory power between P and T for the NDVI in the study area was weakened in the central area and strengthened in the east and the west. Specifically, the synergistic effect between P and T on the NDVI was weakened in both Shaanxi and Ningxia Huizu Zizhiqu, while the opposite occurred in Xinjiang Uygul Zizhiqu, Qinghai, and another five provinces in the eastern part of the study area.

Spatiotemporal Characteristics of Hourly-Scale Extreme Precipitation in the Sichuan Basin and Its Impact on Normalized Difference Vegetation Index Values

“…In this regard, some scholars point out that precipitation and temperature are closely related to NDVI, and their synergistic impact is a significant contributor to NDVI [85,86], with such multiple correlations possibly varying across different regions. Besides natural factors, anthropogenic factors also significantly affect vegetation growth and distribution [87]. For instance, the construction of well-established agricultural water infrastructure in the Sichuan Basin has resulted in good vegetation conditions [88]; around the year 2000, the NDVI in the Amazon rainforest sharply declined, which was directly related to deforestation and land reclamation [89].…”

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

“…This is in agreement with our results, and explains why, when our data were grouped by elevation and aspect, better dendrochronological parameters could be identified. It is also known that NDVI variation is influenced by a synergistic effect of temperature and precipitation [41]. Although, in general, the relationship between the NDVI and precipitation and temperature is positive, this result is not always consistent, especially for temperature [42].…”

Remote Sensing Image Target Detection Method Based on Refined Feature Extraction

“…Remote sensing image object detection plays a crucial role in various domains [1], including large-scale scene detection [2], natural disaster monitoring [3], and resource surveying. It is of great significance for human life and societal development [4,5].…”