Where Are Global Vegetation Greening and Browning Trends Significant?

José Cortés,Ranga B Myneni,Alexander Brenning,Markus Reichstein,Chi Chen,Miguel D Mahecha

doi:10.1029/2020gl091496

Abstract

AbstractGlobal greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple hypothesis testing and false positive trend detection. We reanalyze global greening and account for this issue with a novel statistical method that allows robust inference on greening regions. Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation. Our results confirm several greening regions (China, India, Europe, Sahel, North America, Brazil, and Siberia), that are also supported by independent data products. We also report evidence for an increasing seasonal amplitude in LAI north of 35°N. Considering the widespread use of spatially replicated trend tests in global change research, we recommend adopting the proposed multiple testing procedure to control false positive outcomes.

Highlights

The analysis of vegetation data based on satellite products has led to a consensus on a greening Earth
A hypothesis test is performed for each grid cell; this leads to multiple hypothesis testing and false positive trend detection
Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation

Summary

Introduction

We reanalyze global greening trends based on the Boston University Advanced Very-HighResolution Radiometer (BU AVHRR) LAI data set and validate the results with four other data products: NOAA CDR, LTDR, BU moderate resolution imaging spectroradiometer (MODIS C6), and SPOT. An increase in the seasonal amplitude of LAI would make the 0.1 and 0.9 quantiles behave differently than the mean To test this hypothesis, we aggregate our yearly data into the 0.1, 0.25, 0.5, 0.75, and 0.9 quantiles, and perform trend tests on these quantiles.

Methods

Results and Discussion

Conclusions

Data Availability Statement