Measuring and monitoring forest canopy height are essential for effective forest management and ecosystem assessment. We evaluated the accuracy of forest canopy height based on Digital Surface Models (DSMs) derived from three different optical earth observation satellite stereo images: WorldView-3 along-track stereo pair images, BlackSky Gen-2 along-track 5-shot stereo images, and Newsat cross-track multiple stereo images. BlackSky Gen-2 5-shot stereo images achieved the highest accuracy on the forest canopy height measurement. Over coniferous forest areas with minimal seasonal height variation, the accuracy metrics were as follows: for WorldView-3, the standard deviation was 1.87 meters, Root Mean Square Error (RMSE) was 2.12 meters, and Normalized Median Absolute Difference (NMAD) was 0.97 meters; for BlackSky Gen-2, the standard deviation was 1.65 meters, RMSE was 1.79 meters, and NMAD was 1.24 meters; and for Newsat, the standard deviation was 1.59 meters, RMSE was 1.84 meters, and NMAD was 1.34 meters. The topographic azimuth affected the decreasing accuracy of the DSM derived from the stereo pair images of WorldView-3 on slopes facing away from the satellite's azimuth. The results highlighted the importance of acquiring stereo images from multiple suitable viewing angles to ensure accurate forest measurements in mountainous area. These findings demonstrate the applicability of DSMs derived from satellite stereo images, particularly multi-view stereo images from small satellites, for measuring forest canopy height.

The Greenhouse Gases Observing Satellite-2 (GOSAT-2), launched in October 2018, is equipped with the Cloud and Aerosol Imager-2 (CAI-2), which is used to estimate aerosol properties. In this study, the error characteristics of the estimated products were investigated through comparison with ground-based observations. Worldwide AERONET data from all available sites were primarily used to evaluate the optical properties of aerosol for the period March 2019 to December 2024. The comparison results for each year were similar, and most error statistics showed little or no trend. Consequently, the results from 2019 were considered to be representative and are presented here.

Comparing the satellite data with the AERONET data and calculating the correlation coefficients between them, the correlation for aerosol optical depth (AOD) was generally moderate, but the correlations for other parameters (Ångström exponent (AE_ext), absorption AOD (AOD_abs), absorption AE (AE_abs), single scattering albedo (SSA), PM2.5, and BC amount) were either absent or weak. For example, in a comparison between AOD at 550 nm with AERONET_AOD (all) (i.e., over land and ocean areas), the bias (BIAS), standard deviation (SD), and root mean squared error (RMSE), were 0.090, 0.149, and 0.174, respectively, and the correlation coefficient (R) was 0.593, indicating a moderate correlation. The satellite estimates tended to be overestimated when AOD was low and underestimated when AOD was high. In the comparison with AERONET_AOD (ocean) (i.e., over ocean areas), the BIAS, SD, and RMSE were 0.138, 0.071, and 0.155, respectively, and R was 0.880, indicating a strong correlation. Both the BIAS and R were higher than the values observed over land.

The results of the comparisons varied by dataset, region, target area, and time period. For ground-based AOD values below approximately 0.2, the satellite-derived AOD estimates were insensitive to changes in AOD, suggesting that low AOD values may not be reliably estimated. The estimated AE_ext and AE_abs values were limited to a narrow range, and the SSA values tended to cluster around specific values.

In recent years, organic farming has been increasingly promoted in Japan due to a shift toward sustainable agriculture as well as the rising cost of chemical fertilizers. However, the nitrogen efficacy of organic materials shows considerable seasonal variation, complicating the adjustment of fertilization rates. This has resulted in farm management issues such as delayed crop growth and insufficient harvest volumes to meet contractual delivery requirements for leafy vegetables. To address these challenges, it is essential to record crop growth trajectories and biomass accumulation, and to develop fertilization management techniques based on historical data. In this study, we developed and validated a non-destructive monitoring method to capture temporal changes in the plant height of komatsuna (Brassica rapa var. perviridis) cultivated under greenhouse conditions. Four types of close-range remote sensing devices were used: a ropeway-type mobile imaging system, depth cameras, 2D light detection and ranging (LiDAR), and time-of-flight (ToF) sensors. Structure-from-Motion/Multi-View Stereo (SfM/MVS) analysis using the ropeway-type system achieved the highest accuracy in estimating plant height (root mean square error [RMSE]: 1.4 cm), enabling the visualization of growth heterogeneity across the field. The depth cameras also showed high accuracy (RMSE: 2.45～2.75 cm). The ToF sensors were effective for nighttime monitoring (RMSE: 3.83 cm), while the 2D LiDAR provided cross-sectional profiles of canopy height but had the lowest accuracy (RMSE: 4.96 cm). Continuous day-night monitoring confirmed the presence of a physiological response, circadian leaf angle changes influenced by light conditions, and showed that plant height continued to increase during the nighttime. These growth characteristics of komatsuna may affect the accuracy of plant height estimation.