Leaf Area Index (LAI) is one of the most important indices for monitoring crop growth, but obtaining field measurements is laborious and time-consuming. In recent years as unmanned aerial vehicles (UAVs) have become a popular tool, it has been expected that estimation methods of LAI will be developed for UAV multispectral monitoring. However, our previous study suggested that solar radiation conditions distinctly affect observed values of canopy reflectance and vegetation indices. In this study, we examine several LAI estimation methods based on field measurement data for UAV multispectral monitoring under various solar radiation conditions. Eight paddy fields located in Sendai, Miyagi Prefecture, were selected for the field measurement of LAI and UAV multispectral monitoring. Compared with the LAI estimation based on non-linear regression using the enhanced vegetation index 2 (EVI2) as an explanatory variable, that based on multiple regression using EVI2, background reflectance and solar radiation conditions as explanatory variables improved root mean square error (RMSE) from 0.532 to 0.496 at LAI under 3.5, while it made RMSE worse from 0.929 to 1.120 at LAI over 3.5. This result suggests that the improvement of estimation accuracy by including solar radiation conditions is not consistent, probably due to the uneven data for the conditions. The LAI estimation based on support vector regression using the training data simulated by a radiative transfer model improved RMSE to 0.458 at LAI over 3.5. This implies that even data for the conditions provided by the radiative transfer model simulation properly trains the support vector regression, and improves the accuracy and robustness for LAI estimation.

The recent, frequent occurrence of heavy rainfall and earthquakes in Japan has triggered landslides. Heavy rain occurred in July 2018, causing a lot of damage, mainly in the west. In assessing the damage, data from the Synthetic Aperture Radar (SAR) was found to be effective, thanks to its ability to obtain observations with few restrictions related to weather or time. SAR Interferometry (InSAR) is a method for capturing surface deformations using two acquired SAR images. We detected the deformations of slope caused by the July 2018 heavy rain using the InSAR technique with ALOS-2/PALSAR-2 3-m resolution in Kagawa Prefecture, Japan. Along part of the Takamatsu-Ohgoshi-Sakaide Line, slope deformation of 5-6 cm was accurately detected by InSAR analysis.

Indonesia has ca. 20-27 Mha of peatlands, which function as one of the world’s carbon sinks, since a large amount of carbon is fixed within peat and swamp forests. In recent years, peat swamp forest wildfires have occurred during every El Nino dry season in Central Kalimantan, Indonesia, resulting in the emission of a large amount of carbon dioxide. In this study, we derived peatland ground surface subsidence maps, which can be used as fire hazard maps, using 5 time-series scenes of ALOS-2/PALSAR-2 SAR image data taken between April 2015 and October 2016.