Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea

Abstract

Previous studies have observed seasonal cooling effects in paddy rice as compared to temperate forest through enhanced evapotranspiration (ET) in Northeast Asia, while rare studies have revealed biophysical factors responsible for spatial variations of ET and its cooling effects. In this study, we adopted a data fusion method that integrated MODIS 8-day surface reflectance products, gridded daily climate data of ground surface, and a remote sensing pixel-based Penman-Monteith ET model (i.e., the RS–PM model) to quantify ET patterns of paddy rice in South Korea from 2011 to 2014. Results indicated that the regional variations of the rice-growing season ET (RGS-ET, the sum of daily ET from the season onset of rapid canopy expansion (SoS) to the end of the rice-growing season (EGS)) were primarily influenced by phenological factors (i.e., the length of growing period-LGP), followed by growing season mean climatic factors (i.e., vapor pressure deficit-VPD, and air temperature). For regional variations of the paddy field ET (PF-ET, the sum of daily ET from the field flooding and transplanting date detected by satellite observations (FFTDsat) to SoS, and to EGS), the extents were substantially reduced, only accounting for 54% of the RGS-ET variations. The FFTDsat and SoS were considered critical for the reduced PF-ET variations. In comparison to the temperate forest, changes in monthly ground surface air temperature (Ts) in paddy fields showed the V-shaped seasonal pattern with significant cooling effects found in late spring and early summer, primarily due to a large decline in daytime Ts that exceeded the nighttime warming. Bringing FFTDsat towards late spring and early summer was identified as vital field management practices, causing significant declines in daytime Ts due to enhanced ET. Results highlighted climate-warming mitigation by paddy fields due to early flooding practices.

Change of Rice Paddy and Its Impact on Human Well-Being from the Perspective of Land Surface Temperature in the Northeastern Sanjiang Plain of China

“…From 2000 to 2020, the intense paddy field expansion area formed the cold island phenomenon or the cooling land surface temperature phenomenon in the paddy field region in the study area. In the hot summer in inland areas, such as the Northeast Sanjiang Plain, the ground temperature effect brought by the large-scale expansion of paddy fields can effectively reduce the temperature in paddy fields, which was conducive to improving the comfort of farmers in paddy field planting regions and making them feel cooler in hot summer, to avoid physical discomfort caused by high temperatures, such as heat stroke and human dehydration, considering that land surface temperature may be a factor affecting human well-being [ 53 , 54 ]. This comfort was beneficial not only to farmers but also to the tourists who enjoyed the natural scenery and agricultural engineering investigation, etc.…”

Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

“…This is due to the fact that rare satellite observations can be considered as "sudden/spike points" and are either removed or flattened [28]. Previous studies have proposed the use of the DL function to fit MODIS NDVI observations from a single season, which allows for less homogeneity in the temporal distribution while preserving important cloud-free observations [23,24]. In this study, we applied the DL model to fit Landsat EVI observations for one season using Equation (1), resulting in the generation of daily EVI data (dEVI DL ) (as shown in Figure 1).…”

“…The ranges of x 2 and x 4 are limited to [8.8, 40.9] and [8.8, 40.9], respectively. The DL model, which incorporates constrained values for x 2 and x 4 , has shown the ability to accurately predict VI time series data for temperate crops [24]. It should be noted that the range of x 2 and x 4 may differ between temperate and tropical crops.…”

“…The VI temporal interpolation function is specifically designed for Landsat VI data. Two commonly used global temporal interpolation functions, namely Savitzky-Golay (SG) and Double-Logistic (DL), have been successfully applied to single-year continuous MODIS VI time series [21][22][23][24]. They have also been used for aggregating multi-year cloud-free Landsat Enhanced Vegetation Index (EVI) data [25].…”

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

“…Due to these limitations, several remote sensing (RS)-based models and algorithms have been developed to quantify the ET a in rice, complementing the agrometeorological data observed on the ground and providing more detailed spatial information. Most of them are based on the surface energy balance, e.g., the Simplified Surface Energy Balance Index (S-SEBI) [18], Surface Energy Balance Algorithm for Land (SEBAL) [19], and Mapping Evapotranspiration at High Spatial Resolution with Internalized Calibration (METRIC) [20]; other models couple biophysical parameters and energy balance, e.g., the Breathing Earth System Simulator (BESS) [21], while others combine carbon and vapor fluxes through the response of the canopy conductance to the photosynthesis rate (PML-V2) [22], or integrate earth observations (i.e., the MODIS surface reflectance, albedo, and daily ground surface climate datasets) and numerical algorithms to calculate the ET a [23].…”