Assessing the relationship between vegetation greenness and surface temperature through Granger causality and Impulse-Response coefficients: a case study in Mexico

• Published in: International journal of remote sensing 2020-05, Vol.41 (10), p.3761-3783
• Main Authors: Reygadas, Yunuen, Jensen, Jennifer L.R., Moisen, Gretchen G., Currit, Nate, Chow, Edwin T.
• Format: Article
• Language: English
• Subjects: Anomalies; Causality; Coefficients; Correlation analysis; Deciduous forests; Economic models; Environmental studies; Evaluation; Forests; Imaging techniques; Land surface temperature; Leaf area index; Normalized difference vegetative index; Regression analysis; Seasons; Spectroradiometers; Spring; Spring (season); Summer; Surface temperature; Time response; Vegetation; Vegetation index
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2019.1711241

The existence of a consistent inverse relationship between vegetation greenness and surface temperature has been the premise of many environmental studies. However, some authors have found that the nature of this relationship varies depending on the spatial and temporal scales of analysis as well as vegetation type. Therefore, this research aims to contribute to the understanding of this matter by evaluating the annual and intra-seasonal relationship between Leaf Area Index (LAI) and Land Surface Temperature (LST) in Central Mexico using monthly anomalies of Moderate Resolution Imaging Spectroradiometer (MODIS) data collected from 2002 to 2017. LAI is proposed as an alternative index to overcome the shortcomings of other widely used indicators of vegetation greenness (e.g. Normalized Difference Vegetation Index and Enhanced Vegetation Index). The presence/absence of any relationship is investigated through the notion of Granger causality, while the sign and strength of the relationship is estimated by means of an Impulse-Response (IR) function. Unlike traditional regression and correlation analysis, the Granger causality approach enables the examination of lagged effects of one variable over the other based on past values of both variables. IR coefficients, which have been rarely used in the related literature, help to model the over-time response of a variable with the change of another variable. The overall results indicate that, at any temporal scale, Granger causality from LST to LAI occurs more consistently than causality in the opposite direction. At the annual scale, the nature of the relationship is primarily inverse in both directions and usually weaker from LAI to LST. At the seasonal scale, the occurrence of LST to LAI causality is higher in spring (it occurs in about 40% of the evaluated pixels) and lower in winter (10%) among all forest types. The effect of LST on LAI is predominantly inverse (median coefficient 1 month after impulse −0.043) and particularly strong in deciduous broadleaf forest during summer. On the other hand, the effect of LAI on LST is mainly direct in autumn and inverse in the remaining seasons, except for evergreen needleleaf forest where the effect is inverse only in summer. The highest presence (23%) and strength (−0.039) of LAI to LST causality occur in spring over deciduous broadleaf forest. Based on these results, caution has to be exercised when assuming a consistent strong inverse relationship between vegetation gr