Jongjin Baik

Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea

Estimating the evapotranspiration (ET) is a requirement for water resource management and agricultural productions to understand the interaction between the land surface and the atmosphere. Most remote-sensing-based ET is estimated from polar orbiting satellites having low frequencies of observation. However, observing the continuous spatio-temporal variation of ET from a geostationary satellite to determine water management usage is essential. In this study, we utilized the revised remote-sensing-based Penman–Monteith (revised RS-PM) model to estimate ET in three different timescales (instantaneous, daily, and monthly). The data from a polar orbiting satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS), and a geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), were collected from April to December 2011 to force the revised RS-PM model. The estimated ET from COMS and MODIS was compared with measured ET obtained from two different flux tower sites having different land surface characteristics in Korea, i.e. Sulma (SMC) with mixed forest and Cheongmi (CFC) with rice paddy as dominant vegetation. Compared with flux tower measurements, the estimated ET on instantaneous and daily timescales from both satellites was highly overestimated at SMC when compared with the flux tower ET (Bias of 41.19–145.10 W m−2 and RMSE of 69.61–188.78 W m−2), while estimated ET results were slightly better at the CFC site (Bias of –27.28–13.24 W m−2 and RMSE of 45.19–71.82 W m−2, respectively). These errors in results were primarily caused due to the overestimated leaf area index that was obtained from satellite products. Nevertheless, the satellite-based ET indicated reasonable agreement with flux tower ET. Monthly average ET from both satellites showed nearly similar patterns during the entire study periods, except for the summer season. The difference between COMS and MODIS estimations during the summer season was mainly propagated due to the difference in the number of acquired satellite images. This study showed that the higher frequency of COMS than MODIS observations makes it more ideal to continuously monitor ET as a geostationary satellite with high spatio-temporal coverage of a geostationary satellite.

Multi-satellite-based water budget components in South Korea

Interpreting and predicting variations of the water cycle are a significant concern given the emerging threat of climate change. Generically, hydrological components of the water cycle are routinely observed with ground-based measurements, yet it is difficult to measure their spatiotemporal variability. Remote sensing approach is recognized as one of the most promising tools to obtain continuous data over large areas, thereby offering the unique possibility to assess the complicated and non-local features of hydrological phenomena. To estimate water budget components using remote sensing, this research considers precipitation (P), evapotranspiration (ET), and the change in water storage (∆S) calculated from satellites (i.e., Communication, Ocean and Meteorological Satellite; COMS, and Gravity Recovery and Climate Experiment; GRACE) and the Global Land Data Assimilation System (GLDAS) model-based datasets in South Korea from April to December 2011. The P estimates from the COMS rainfall intensity (COMS RI), COMS CM (which employs conditional merging [CM] to improve the accuracy of COMS RI), and GLDAS were compared with the measured P values from the two flux towers on a monthly scale. These results showed that COMS CM and GLDAS are in reasonable agreement, and additionally, their correlation, bias, and root-mean-square errors are favorable compared to the original COMS RI. The ET estimation of GLDAS and COMS applied from the revised RS-PM method were compared which indicated reasonable agreement with the two flux tower measurements. The derived runoff from COMS RI, COMS CM, and GLDAS was evaluated with that of the flux towers. The statistical results indicated that COMS CM and GLDAS were slightly better than that of COMS RI. The spatial distribution of P from COMS CM and GLDAS indicated similar pattern with that of ground-based measurement with the exception of COMS RI. ET from COMS and GLDAS showed slightly analogous pattern. The spatial distribution of runoff from both COMS and GLDAS showed evidence of a seasonality, which mainly resulted from the seasonally varying effects of ET and P. This research shows that it is possible to conduct the analysis of COMS products for efficient water resource planning, monitoring, and water budget modeling.

An Assessment and Analysis of the Gap-Filling Techniques for Revising Missing Data of Flux Tower based Evapotranspiration - FAO-PM, MDV, and Kalman Filter -

This study was conducted to evaluate the performance of gap-filling techniques in improving the accuracy of evapotranspiration data provided at the flux tower based on the eddy covariance method in Seolma(SMC) and Cheongmi(CMC). The quality control was applied for the raw data observed at the flux tower using the KoFlux program to provide Level 1 data. Through the statistical validation with the raw data, Level 1 data which was applied for correcting and removing the bad data indicated the good results. After that, we conducted the three gap-filling techniques including the Food and Agriculture Organization Penman-Monteith(FAO-PM), Mean Diurnal Variation(MDV), and Kalman filter to Level 1 data and compare the validation results based on several statistical analysis such as, bias, Root Mean square error, correlation coefficient(R), and Index of Agreement(IOA). The good results of IOA(average value of 0.69, 0.62, and 0.90 at SMC and average value of 0.91, 0.86, and 0.94 at CMC) and R(average value of 0.54, 0.47, and 0.84 at SMC and average value of 0.89, 0.77, and 0.93 at CMC) indicated the goodness of fit between estimated and Level 1 data. Overall, this good statistical results demonstrated the potential application of three gap-filling techniques in replacing the missing data. Specially, our results also revealed that the FAO-PM method which consider the meteorological factors along with the Kalman filter method which consider the climatic conditions and diurnal variation patterns simultaneously outperformed the MDV method.

Estimation of Water Quality using Landsat 8 Images for Geum-river, Korea

In this study, the water quality parameters of Geum-river were estimated using Landsat 8 satellite image data which had launched in March 2013. The goal of this research is to predict HAB and to monitor spatial pattern of total nitrogen (TN) and total phosphorus (TP) because both TN and TP are the dominant factors of the growth of harmful algal blooms (HABs). To investigate the relationship between satellite band reflectance and in situ measurement value, Pearson` correlation coefficient analysis was used. The band2, 3, 4 and 5 reflectance values among 11 bands of Landsat 8 were used which was highly associated with detecting TN and TP. The 20 in situ data set with satellite`s overpass time were identified. TN showed positive relation with band 2 (0.48), band3 (0.62), band4 (0.57) at a significance level of p