Analysis of the long‐term high‐resolution infrared radiation sounder land surface temperature against ground measurements during 1980–2009 in the Poyang Lake basin, China

Abstract

Land surface temperature (LST) plays an essential role in the energy exchanges between land and atmosphere. It reflects the changes of available terrestrial energy in the context of global warming, and thus can serve as an indicator of regional climate change. The recently developed HIRS (High‐resolution infrared radiation sounder onboard NOAA satellites) LST product is among the longest LST records. We have analysed the HIRS LST against ground measurements from 66 stations in the Poyang Lake basin, China, considering the temporal variations at daily and interannual time scales during 1980–2009. The error metrics of correlation coefficient, Bias and unbiased Root Mean Square Deviation (ubRMSD) are used, together with the long‐term linear trends. The two data sets have considerable agreement with respect to daily variations. Their correlation coefficients are higher than 0.95 for all stations; and those for daily anomalies range from 0.73 to 0.85 across the basin. The Bias and the ubRMSD have indicated that the HIRS data have generally underestimated the LST across the basin, mainly because of its large underestimation of the occurrences of temperature higher than 35 °C. Nevertheless, the HIRS LST has largely overestimated the occurrences of summer days with temperature ranging 20–30 °C. The Bias and the ubRMSD appear to be smaller in the cropped plains and much larger in the afforested mountain areas. As for the interannual variations, the HIRS LST shows highest correlations with ground measurement in spring; they are lowest in summer, possibly due to the interference of monsoonal precipitation. Both the HIRS LST and ground measurements show significant increasing trends with similar patterns in spring and winter. However, the long‐term trends are significantly overestimated across the basin in summer, but underestimated in autumn in the HIRS LST data.

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

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