Chlorophyll content has been used as an indicator for assessing photosynthetic ability, health and defense against a variety of degenerative diseases. Hyperspectral remote sensing offers some non-destructive methods and has played an important role in evaluating vegetation characteristics. However, the prices of traditional field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have not yet decreased to consumer levels, which prevents much practical use. Recently, fingertip-sized spectrometers have been developed and then they could be powerful tools for evaluating vegetation characteristics. In the present study, a compact spectrometer (C12880MA-10, Hamamatsu Photonics) was used to evaluate chlorophyll content in tea leaves. Incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy and then de-trending was the best pre-processing technique in this study, achieving an RPD of 2.03 and an RMSE of 3.07 μg cm^－2. The proposed method is cost effective, practical for consumers to apply and will enable effective crop management.

Landslide map is a thematic map used for disaster management. In recent years, there have been attempts to create landslide maps using artificial intelligence-based approaches. This study aimed to clarify the effectiveness of two normalization methods, derived from the spatial non-uniformity and continuity of landslide topography, for the deep generative model of landslide moving mass. We propose a normalization method for the supervised data to correct the spatial non-uniformity of landslides. The resulting supervised data, normalized by landslide area occupancy, improved the learning efficiency of the deep generative model. We also propose a normalization method for the inferenced results using the spatial continuity of landslides. The inferenced results, post-processed by employing our normalization method, showed reasonable distribution in comparison to the ground truth.