Understanding the periods when irrigation starts in paddy fields is important for grasping the current situation of usage of paddy irrigation water. I developed a method to extensively analyze periods of irrigation using multiple Sentinel-2 satellite data, multispectral images available free of charge. In this method, the periods when irrigation starts for individual paddy fields was determined by the following procedures; (1) improvement of spatial resolution of short-wave infrared band data by the pan-sharpening method, (2) creation of Modified Normalized Difference Water Index (MNDWI) images, (3) creation of binary images by the Otsu' threshold method, (4) determination of whether individual paddy fields are flooded on each satellite observation day, (5) determination of whether irrigation has started in individual paddy fields on each satellite observation day, (6) determination of the period when irrigation starts in each paddy field. The periods when irrigation started in individual paddy fields located in the target area were determined by this method using the eight Sentinel-2 satellite data observed from April to June, and paddy parcel boundary data. As a result, the paddy fields where irrigation started from April 14th to April 20th were the most numerous. In addition, accuracy verification and a breakdown analysis of the results for judging whether irrigation had started was conducted for each paddy field in part of the target area. As a result, the correct determination of whether irrigation had started in each paddy field was 98% on May 15th. On June 4th, many paddy fields where rice plants were relatively large were judged as "no flooding". However, since they were judged as "flooded" at least once in April to May, they were judged to be already irrigated. Therefore, the periods when irrigation starts for individual paddy fields can be determined with high accuracy by applying this method.
Spatiotemporal analysis using satellite imagery data is effective for extracting information about saline soils, which adversely affect the yield of agricultural products. Northeast Thailand has been suffering from the influence of saline soils for many years, and this has created difficulties in the sustainable cultivation of rice, which is a main agricultural product of this country. In the present study, we developed a method for extracting the saline soil areas using salts measurement data from paddy fields as well as time series satellite images taken by Sentinel-2, and we then analyzed the influence of salinity on the rice crop cultivation. In the soil investigation, we measured two items concerned with soil salinity, namely the electric conductivity (EC 1:5) and the hydrogen ion value (pH 1:5), to create a soil EC map and a soil pH map. The classification results showed a total accuracy of 83.67% for soil EC (Kappa coefficient: 0.47) and a total accuracy of 81.63% for soil pH (Kappa coefficient: 0.12). The saline soil areas were then extracted by overlaying both maps. We also investigated the correlation of the statistical yield data and the normalized difference vegetation index (NDVI) for the rice crop cultivation period, to create a rice crop yield map. Comparison of the generated soil salinity map with the rice crop yield map revealed a lower rice crop yield in the saline soil than non-saline soil, and the tendency was accepted remarkably, especially in the areas close to rivers or ponds.