Solar photovoltaic (PV) technology has been widely used as a major source of renewable energy. Vietnam is especially active in installing solar energy systems. The total installed solar PV capacity in Vietnam has significantly increased since 2019, but the spatial evolution of solar panels is yet to be discussed. Therefore, this study aims to reveal the shift that occurred in solar photovoltaic planning sites in Vietnam from 2019 to 2022. Solar PV maps were produced from Sentinel-2 imagery via a deep learning segmentation model. Land cover maps, terrain slope, solar power potential, population density, and power grid datasets were compared to the locations of the detected PV sites each year to reveal a shift in the solar farm planning sites. The result show that the deep learning model achieved satisfactory performance. The observed shift in the PV installation sites suggests that for the first two years, large solar farms were built on suitable land near the electricity grid, while smaller PVs were constructed at locations less suitable for solar energy production in 2021 and 2022. These findings suggest that the shift in solar PV planning in Vietnam was caused by the availability of suitable land with an appropriate energy transfer capacity and the participation of smaller-scale PV operators.

Photovoltaic (PV) technology is critical to achieving a sustainable society. These PV systems are distributed over large areas, making it important to study their characteristics for applications such as solar power output modeling. The combination of satellite imagery, especially multispectral imagery, with machine learning models has proven to be an effective tool for locating PVs. However, PVs are typically treated as a single category in previous studies, despite the existence of different types with different important parameters such as energy conversion efficiency and lifetime. The objective of this research was to investigate the detailed spectral data of four PV types currently on the market to enable their differentiation in hyperspectral satellite data. Spectral samples were collected using a handheld spectrometer and the Hyperspectral Imager SUIte (HISUI) satellite hyperspectral sensor. There were notable differences in the reflectance characteristics of the four PV types. The presence of vegetation around the PVs can affect the satellite-encoded spectral signature. Four spectral indices were defined based on the spectral characteristics of each PV type, and the discriminability of the different PV types based on the proposed indices was evaluated using a statistical metric. The potential to discriminate between different solar PV types was demonstrated through the combined use of satellite hyperspectral data and spectral indices.

The recent development of solar photovoltaics (PV) has generated considerable interest in energy management, appropriate environmental impact assessment, and the seamless integration of PV technology into society. A critical first step in exploring these research opportunities is the creation of a comprehensive PV database that describes the locations and extents of existing PV installations. Automated solar PV detection in satellite remote sensing, based on a machine learning approach, is particularly suitable for studying the characteristics of national-scale solar PV distribution and its impact on the environment. In our study, we first proposed an XGBoost-based solar PV detection with post-processing procedures supported by a dedicated solar PV spectral index. This approach was applied to Sentinel-2 images acquired in 2022 to create a national solar PV database in Japan. The resulting solar PV map showed a high degree of accuracy, with an overall accuracy of 0.984. Our dataset revealed the presence of solar PVs covering a total area of 571 km² in Japan. The comparison of PV extents with the land cover map showed that the megawatt-scale solar PV facilities were predominantly located in forested areas, suggesting potential changes to existing forest ecosystems and the local environment at these facility locations. Conversely, smaller megawatt-scale PV systems showed a similar preference for both farmland and forest. PV expansion also contributed to forest fragmentation at forest edge areas. To further investigate these findings, we did the clustering analyses to identify high-concentration PV areas and analyzed the distribution of solar PVs alongside socio-economic and environmental factors using an explainable AI approach based on Shapley values. Through the study, we showed how the established PV dataset can be used to uncover spatial patterns and driving factors of PV deployment. Our results indicate that site selection is influenced by a multitude of variables—such as local environmental conditions, power demand, and installation costs—highlighting the need for well-informed strategies when deploying solar PV. Overall, this study demonstrates the efficacy of integrating machine learning models, spectral indices, and post-processing techniques with satellite remote sensing data to accurately map and analyze solar PV installations. Regular updates of these maps from freely available satellite datasets provide valuable insights for policymakers and stakeholders, enabling data-driven decisions regarding the placement, monitoring, and management of PV systems, and supporting a timely transition to a renewable-powered society.

Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved $R^2=0.85$ and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework.