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Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.

Abstract:Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.

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