Land use and land cover change (LUCC) has been increasingly recognized as having important effects on climate systems. Paddy fields, one kind of artificial wetland, have seen a significant increase in the Sanjiang Plain, China since 2000 and have become the most typical LUCC at the regional scale. Against this background, in this paper, we discuss the effects of this artificial wetland increase on surface temperature, in addition to its driving mechanisms. Firstly, the spatiotemporal variations of land surface temperature (LST) and its two driving variables (albedo and latent heat flux (LE)) in the Sanjiang Plain are analyzed and assessed based on remote sensing observation information from 2001 to 2015. Our results from both spatial distribution difference and time series analysis show that paddy field expansion led to day-time cooling and night-time warming over the study area. However, the LST changes show different characteristics and magnitudes in the spring (May to June) compared to the other months of the growing season (July to September). The daytime cooling trend is found to be -0.3842 K/year and the warming trend at night 0.1988 K/year during the period 2001 to 2015, resulting in an overall cooling effect in May and June. In July-September, the LST changes have the same sign but a smaller magnitude, with a -0.0686 K/year temperature trend seen for the day-time and a 0.0569 K/year increase for the night-time. As a consequence, a pronounced decrease in the diurnal temperature range is detected in the growing season, especially in spring. Furthermore, albedo and LE are demonstrated to be very sensitive to land use changes, especially in the earlier periods of the growing season. Correlation analysis between LST and albedo and LE also indicates the dominant role played by evapotranspiration in paddy fields in regulating local temperature.
CITATION STYLE
Yu, L., & Liu, T. (2019). The impact of artificial wetland expansion on local temperature in the growing season-the case study of the Sanjiang Plain, China. Remote Sensing, 11(24). https://doi.org/10.3390/rs11242915
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