Abstract
Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.
Highlights
It is important to note that the re-visit cycle of AMSR-2 at low latitudes such as those of Kenya is normally greater than a day, so not all cloud gap-filled MODIS land surface temperature (LST) pixels were able to be bias-adjusted based on a near-simultaneous AMSR-2 observation
Our results reveal the effectiveness of using passive microwave (PM) observations for calibration of the gap-filled longwave infrared (LWIR)-derived LSTs, especially during the daytime
We have presented an effective two-step framework for improving the cloud gap-filling performance of the LWIR-based daily land surface temperature (LST)
Summary
Land surface temperature (LST) is classified as a Global Climate Observing System. Essential Climate Variable (GCOS-ECV) [1]. It is widely used in both research and commercial applications, with its key domains of relevance including agriculture [2], urban landscape management [3], disaster risk analysis [4], and investigations on heat flux and hydrological features across the globe [5,6]. Satellite LST data derived from brightness temperatures (BT) recorded in the longwave infrared (LWIR) spectral region. The resulting spatiotemporal ‘cloud gaps’ pose a challenge for applications reliant on regular and routine LST observations, both in terms of environmental models and derived data products [10,11]. The cloud gap problem may be further exacerbated when the sensor providing BT data is mounted on a polar orbiting satellite
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