Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease.

There are certain growth stages and spectral regions that are optimal for obtaining a high accuracy in rice yield prediction by remote sensing. However, there is insufficient knowledge to establish a yield prediction model widely applicable for growth environments with different meteorological factors. In this study, high temporal resolution remote sensing using unmanned aerial vehicle-based hyperspectral imaging was performed to improve the yield prediction accuracy of paddy rice cultivated in different environments. The normalized difference spectral index, an index derived from canopy reflectance at any two spectral bands, was used for a simple linear regression analysis to estimate the optimum stage and spectral region for yield prediction. Although the highest prediction accuracy was obtained from the red-edge and near-infrared regions at the booting stage, the generalization performance for different growth environments was slightly higher at the heading stage than at the booting stage. The coefficient of determination and the root mean squared percentage error for the heading stage were R² = 0.858 and RMSPE = 7.52%, and they were R² = 0.853 and RMSPE = 9.22% for the booting stage, respectively. In addition, a correction by solar radiation was ineffective at improving the prediction accuracy. The results demonstrate the possibility of establishing regression models with a high prediction accuracy from a single remote sensing measurement at the heading stage without using meteorological data correction.

Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the composition of true color images, and a fourth in the near-infrared region, which employs liquid crystal tunable filter (LCTF) technology for wavelength scanning. Band-to-band image registration methods have also been developed for the HPT and implemented in the image processing procedure. The processed images were compared with other satellite images, and proven to be useful in various remote sensing applications. Thus, LCTF technology can be considered an innovative tool that is suitable for future multi/hyperspectral remote sensing by nano/microsatellites.

Radiometric calibration utilizing the Moon as a reference source is termed as lunar calibration. It is a useful method for evaluating the performance of optical sensors onboard satellites orbiting the Earth. Lunar calibration provides sufficient radiometric calibration opportunities without requiring any special equipment, and is suitable for nano/microsatellites. This study applies lunar calibration to a multispectral sensor, Ocean Observation Camera (OOC), on board a microsatellite named Rapid International Scientific Experiment Satellite. Simulating the brightness of the Moon based on the RObotic Lunar Observatory and SELENE/Spectrum Profiler models, sensitivity degradation was proven to be negligible in any of the four spectral bands of the OOC with the sensor temperature correction. A bluing trend in the OOC’s sensor sensitivity was revealed, indicating a shorter observation wavelength shows larger irradiance. Comparing the top-of-atmosphere reflectance of Railroad Valley Playa with the Radiometric Calibration Network dataset revealed that the derived calibration parameter from the lunar calibration was valid for correcting the bluing trend in the visible range. Although the lunar and vicarious calibration parameters for the infrared band were unexpectedly inconsistent, lunar calibration could potentially contribute toward estimating the contaminated background radiance in the Earth observation images.

Approximately half of the world’s apple production occurs in East Asia, where apple Valsa canker (AVC) is a prominent disease. This disease affects the bark of the tree, ultimately killing it and resulting in significant economic loss. Visual identification of the diseased area of the bark, particularly in the early stages, is extremely difficult. In this study, we conducted hyperspectral imaging of the trunks and branches of AVC-infected apple trees and revealed that the diseased area can be identified in the near-infrared reflectance, even when it is difficult to distinguish visually. A discriminant analysis using the Mahalanobis distance was performed on the normalized difference spectral index (NDSI) obtained from the measured spectral reflectance. A diagnostic model for discriminating between the healthy and diseased areas was created using the threshold value of NDSI. An accuracy assessment of the diagnostic model presented the overall accuracy as >0.94 for the combinations of spectral bands at 660–690 nm and 720–760 nm. This simple diagnostic model can be applied to other tree bark canker diseases.