Assessing 100 biophysical indices performances in the Mediterranean basin using multi-satellite data

Abstract

Due to its impact on our ecosystem (i.e. vegetation, soil, water, etc.), global change has become more significant over time. Therefore, several biophysical indices (e.g. NDVI, NDWI, NDBI, etc.) have been developed to quantify, assess, and monitor the ecosystem reaction to these changes. Numerous and various satellite imagery can be integrated into multiple analysis approaches to carry out this assessment. One of the efficient methods in this field is Time Series Analysis (TSA), which allows the decomposition of data into three components (i.e. seasonality, irregularity, and trend). In this paper, we considered 100 biophysical indices, which we classified into six categories (i.e. vegetation, water, soil, burn, building, and others). These indices were computed using images of different satellite sensors ranging from coarse to high resolution [i.e. AVHRR, MODIS, Landsat-8, Sentinel-2, etc.] considering two study cases. The first case involved monitoring the Mediterranean basin from 2010 to 2014. The second focused on four specific climate zones -most dominant within the Mediterranean basin- (i.e. San Severo, Tangier, Tobruk, and Murcia), which were conducted from 2015 to 2022. High-performance computing of biophysical indices in both study cases resulted in more than 30,000 images used in generating a significant number of time series. To examine these results and quantify the relationship between satellites across different parameters, two analysis methods (i.e. trend analysis and correlation analysis) were employed. Trend analysis results showed that in the first case, 87% of indices trends are harmonized between AVHRR and SPOT-VGT. Moreover, in the second case, the percentages of biophysical indices that showed harmonized trends among all satellites are 55% for San Severo and Tobruk, 49% for Tangier, and 34% for Murcia. Finally, this study shows that satellite-derived biophysical indices and their trends may be significantly impacted by spatial scale, climate, satellite resolution, time series length, and outliers.