Non-stationarity in MODIS fAPAR time-series and its impact on operational drought detection

Abstract

A time-series of fAPAR (fraction of Absorbed Photosynthetically Active Radiation) data collected by the MODIS (MODerate Resolution Imaging Spectroradiometer) Terra satellite between 2001 and 2016 has been analysed at global scale in order to identify possible areas interested by a linear temporal trend. The main scope of this study is to quantify how the possible trend may affect estimates of drought events based on 10-day fAPAR standardized anomalies (z values). The trend analysis, performed according to the Theil-Sen approach, highlighted that about 10% of the globe shows a statistically significant trend (following the Mann-Kendall test at p = 0.05), mostly being positive (increasing fAPAR). The impact of such a trend on standardized anomalies has been formally expressed as a linear function of two quantities: 1) the z value itself, and 2) the year under investigation. This approach allows summarising the variations in z into just two parameters. In the case of negative anomalies (of greater interest for drought analyses) and the most recent years on the time-series (of greater interest for operational monitoring systems), the use of a stationary (no trend) reference tends to underestimate both severity and extension of the areas interested by drought compared to the non-stationary (or trend stationary) reference. The areas mostly interested by significant differences in the outputs of stationary and non-stationary analyses are the Indian subcontinent, eastern China and the Mediterranean countries. As an example of the impacts of the choice of one of the two frameworks on drought monitoring, the results for the recent summer drought in Italy in 2017 have been reported. This example demonstrates how the nonstationary approach tends to be conservative compared to the stationary one, with the former detecting larger affected areas and higher average severity compared to the latter.

Harmonization of GEOV2 fAPAR time series through MODIS data for global drought monitoring

“…The analogous map for MODIS (lower panel) highlights a higher concentration of values close to zero, with some similar patterns in correspondence of the areas with more extreme values (e.g., eastern China and central Latin America). Cammalleri and Vogt (2018) show that about 10% of the globe has significant fAPAR trend in MODIS data, mostly constituted by increasing values, a result that is spatial consistent with the areas with large differences in Fig. 4 (lower panel).…”

“…It is worth mentioning how in a consistent dataset a symmetric distribution centered on zero is expected, similarly to what is observed for the MODIS dataset (dotted lines). Overall, these plots confirm a limited impact of the trends observed in Cammalleri and Vogt (2018) on the baseline statistics.…”

“…Overall, both MODIS and GEOV2 ’ seem to perform relatively close to the expected normal distribution and better than the original GEOV2. The higher frequency of positive anomalies compared to negative ones (about 20% vs. 13%) can be partially explained by the slight linear trend in MODIS data observed by Cammalleri and Vogt (2018) over some areas of the globe, which is also partially visible in the baseline differences reported in Fig. 4 (lower panel).…”

Can We Use Satellite-Based FAPAR to Detect Drought?

“…The newly released QA4ECV FAPAR generally shows similar temporal and spatial patterns to MODIS and CGLS FAPAR. Since MODIS and CGLS FAPAR have been both validated and applied in previous studies, particularly for drought analyses such as [33,34,35], the agreement of QA4ECV FAPAR with these datasets suggests its potential for drought monitoring. The MODIS and CGLS FAPAR datasets have the advantage of higher spatial resolution, however they are limited to short temporal coverage; only since around the 2000s.…”

“…In addition to these indices, the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) can directly reflect the greenness and health conditions of vegetation [31,32]. A number of studies have used FAPAR to monitor and assess drought impacts [33,34,35]. In particular, FAPAR has been selected and operationally used as the Combined Drought Indicator (CDI) for monitoring drought in the European Drought Observatory (EDO) within the Copernicus Emergency Management Service [30].…”