Abstract
National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.
Highlights
Accepted: 24 February 2021Land surface temperature is a key parameter for the monitoring, modeling, and forecasting of water and carbon cycles in our context of climate change
We present here the methodology used to carry out our objectives, which are split in three parts: building of the validation time series, presentation of the orbital drift effect correction methods, and statistical methods used for method assessment
When we look at these statistics by International Geosphere Biosphere Programme (IGBP) land cover (Figure 7), we see that the orbital drift effect is highly land cover dependent, with higher effects for bias and standard deviation for given classes corresponding to shrublands, savannas, grasslands, and deserts, as observed previously in the literature [7]
Summary
Accepted: 24 February 2021Land surface temperature is a key parameter for the monitoring, modeling, and forecasting of water and carbon cycles in our context of climate change. Before 2000, the Advanced Very High Resolution Radiometer (AVHRR), onboard the successive National Oceanic and Atmospheric Administration (NOAA) platforms, is the only instrument (see Table 1) to allow for the estimation of this parameter [3], platform characteristics prevent their use because of temporal incoherence [4] It is—at the moment—impossible to further relate observed worldwide air temperature increases to observed sea and land surface temperatures, as already carried out recently from the MODIS archive [5,6]. None of the NOAA platforms was designed to remain on its nominal orbit, so the platforms’ orbit started to drift immediately after their positioning [4] This resulted in a progressively later acquisition of data, introducing a spurious decreasing trend in afternoon land surface temperature (LST) time series throughout the lifetime of each NOAA satellite. It has little influence for surfaces with high heat capacity, especially water, for arid
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