Search Results (155)

Methane emissions from rice paddies account for over 11% of global atmospheric CH₄, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving greenhouse gas estimation accuracy. This study investigates the backscattering mechanisms of L-band SAR for inundation/non-inundation classification in paddy fields using full-polarimetric ALOS-2 PALSAR-2 data. Field surveys and satellite observations were conducted in Ryugasaki (Ibaraki) and Sekikawa (Niigata), Japan, collecting 1360 ground samples during the 2024 growing season. Freeman–Durden decomposition was applied, and relationships with plant height and water level were analyzed. The results indicate that plant height strongly influences backscatter, with backscattering contributions from the surface decreasing beyond 70 cm, reducing classification accuracy. Random forest models can classify inundated and non-inundated fields with up to 88% accuracy when plant height is below 70 cm. However, when using this method, it is necessary to know the plant height. Volume scattering proved robust to incidence angle and observation direction, suggesting its potential for phenological monitoring. These findings highlight the effectiveness of L-band SAR for water management monitoring and the need for integrating crop height estimation and regional adaptation to enhance classification performance. Full article

Precipitable water vapor (PWV) is an important component of atmospheric spatial parameters and plays a vital role in meteorological studies. In this study, PWV retrieval by real-time precise point positioning (PPP) technique is validated by using global navigation satellite system (GNSS) observations and four real-time products from different analysis centers, which are Centre National d’Etudes Spatiales (CNES), Internation GNSS Service (IGS), Japan Aerospace Exploration Agency (JAXA), and Wuhan University (WHU). To comparatively analyze the performance of each scenario, the single-system (GPS/Galileo/BDS3), and multi-system (GPS + Galileo + BDS) PPP techniques are applied for zenith tropospheric delay (ZTD) and PWV retrieval. Then, the ZTD and PWV are evaluated by comparison with the IGS final ZTD product, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data, and radiosondes observations provided by the University of Wyoming. Experimental results demonstrate that the root mean squares error (RMS) of ZTD differences from multi-system solutions are below 11 mm with respect to the four-product series and the RMS of PWV differences are below 3.5 mm. As for single-system solution, the IGS real-time products lead to the worst accuracy compared with the other products. Besides the scenario of BDS3 observations with IGS real-time products, the RMS of ZTD differences from the GPS-only and Galileo-only solutions are all less than 15 mm compared to the four-product series, as well as the RMS of PWV differences is under 5 mm, which meets the accuracy requirement for GNSS atmosphere sounding. Full article

Bangladesh is highly susceptible to land use land cover (LULC) changes due to its geographical location and dense population. These changes have significant effects on food security, urban development, and natural resource management. Policy planning and resource management largely depend on accurate and detailed LULC maps. However, Bangladesh does not have its own national scale detailed high-resolution LULC maps. This study aims to develop high-resolution land use land cover (HRLULC) maps for Bangladesh for the years 2020 and 2023 using a deep learning method based on convolutional neural network (CNN), and to analyze LULC changes between these years. We used an advanced LULC classification algorithm, namely SACLASS2, that was developed by JAXA to work on multi-temporal satellite data from different sensors. Our HRLULC maps with 14 categories achieved an overall accuracy of 94.55 ± 0.41% with Kappa coefficient 0.93 for 2020 and 94.32 ± 0.42% with Kappa coefficient 0.93 for 2023, which is higher than the commonly accepted standard of around 87 overall accuracy for 14 category LULC map. Between 2020 and 2023, the most notable LULC increase were observed in single cropland (17 ± 4%), aquaculture (20 ± 5%), and brickfield (56 ± 25%). Conversely, decrease occurred for salt pans (47 ± 16%), bare land (24 ± 3%), and built-up (13 ± 3%). These findings offer valuable insights into the spatio-temporal patterns of LULC in Bangladesh, which can support policymakers in making informed decisions and developing effective conservation strategies aimed at promoting sustainable land management and urban planning. Full article

Land use change strongly influences ecosystem carbon services. This study evaluates long-term variations in carbon storage resulting from land use transitions in the Southeast region of Vietnam during 1990–2020. The analysis uses ALOS (JAXA) land use data in combination with QGIS-based spatial analysis to estimate carbon stocks. Land use trajectories were classified according to their dominant driving processes (urbanization, restoration, succession, reclamation, and reverse succession) to assess how each process affects carbon storage. The results indicate that total carbon stock increased from 475 million tons in 1990 to 502 million tons in 2010, before declining to 462 million tons in 2020. Carbon loss was mainly caused by urban expansion and ecological degradation, while ecological succession and forest restoration only partially compensated for these losses. This study develops a spatial framework for analyzing land use trajectories based on natural and anthropogenic dynamics, reflecting the region’s current administrative boundaries to improve management relevance. These findings underscore the need for sustainable land management, controlled urbanization, and ecosystem restoration to maintain carbon sequestration capacity and support Vietnam’s net-zero emission goals. Full article

Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band synthetic aperture radar (SAR) satellite data for BES monitoring. We found that the data have mainly been analyzed using image classification and regression methods, with classification methods attempting to understand how the extent, spatial distribution, and/or changes in different types of land use/land cover affect BES, and regression methods attempting to generate spatially explicit maps of important BES-related indicators like species richness or vegetation above-ground biomass. Random forest classification and regression algorithms, in particular, were used frequently and found to be promising in many recent studies. Deep learning algorithms, while also promising, have seen relatively little usage thus far. PALSAR-1/-2 annual mosaic data was by far the most frequently used dataset. Although free, this data is limited by its low temporal resolution. To help overcome this and other limitations of the existing L-band SAR datasets, 64% of studies combined them with other types of remote sensing data (most commonly, optical multispectral data). Study sites were mainly subnational in scale and located in countries with high species richness. Future research opportunities include investigating the benefits of new free, high temporal resolution L-band SAR datasets (e.g., PALSAR-2 ScanSAR data) and the potential of combining L-band SAR with new sources of SAR data (e.g., P-band SAR data from the “Biomass” satellite) and further exploring the potential of deep learning techniques. Full article

This paper presents a power-centric systems-engineering approach for PlanarSats and for atto-, and femto-class spacecraft where surface-limited power dominates design. We review agency practices (The National Aeronautics and Space Administration (NASA), European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA)) and the American Institute of Aeronautics and Astronautics (AIAA) framework, then extend them with refined low-power subcategories and a log-linear method for selecting phase- and class-appropriate power contingencies. The method is applied to historical and conceptual PlanarSats to show how contingencies translate into required array area, allowable incidence angles, and duty cycle, linking power sizing to geometry and operations. We define the operational power envelope as the range of satellite orientations and conditions under which generated power meets or exceeds mission requirements. Consistent with agency guidance, sizing is performed to the maximum expected value (MEV) (CBE plus contingency); when bounding or stress analyses are needed, we report the maximum possible value (MPV) (Maximum Possible Value) by applying justified system-level margins to the MEV. Results indicate that disciplined, phase-aware contingency selection materially reduces power-related risk and supports reliable, scalable PlanarSat missions under severe physical constraints. Full article

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia. Full article

Climate change is accelerating, and the monitoring of plant phenology is becoming increasingly important. In response to this need, many vegetation indices (VIs) and analytical methods have been developed. However, many VIs are vulnerable to uncertainties caused by snowmelt, making them potentially unsuitable for monitoring spring phenology in forested regions where leaf flush (start of season, SOS) begins simultaneously with snowmelt. Although several VIs for snowy regions have been proposed, most of them were designed for tundra vegetation, such as grasslands. Currently, no VI or analytical method specifically suited for snowy forested regions has been firmly established. Similarly, there is still no well-established method for continuously monitoring autumn coloration. In this study, we propose the use of hue, one of the components of the HSV model, for remote sensing of plant phenology. Hue quantifies differences in object color and is expected to facilitate clearer distinction of snow influence. It may also enable accurate detection of canopy color transitions, such as autumn coloration. We evaluate the applicability of hue to remote sensing using in situ spectroradiometer observations collected from five sites of the Phenological Eyes Network (PEN), which represent a range of ecosystems—including forests, grasslands, and paddy fields—as well as the relative spectral response (RSR) of the Second-generation Global Imager (SGLI) onboard the GCOM-C satellite operated by JAXA (Japan Aerospace Exploration Agency). The results suggest that hue is more robust to snow-related uncertainties than traditional VIs (NDVI, EVI, CCI, NDGI) and may also be effective for quantifying autumn coloration. Hue is calculated solely from blue, green, and red reflectance, without relying on near-infrared (NIR) or shortwave infrared (SWIR) channels. Since blue, green and red channels are available on almost all optical satellite sensors, hue may offer broader applicability than many traditional VIs. Full article

In the analysis of isiXhosa literary texts, the role of stylistic sentences in enhancing the meanings and reinforcement of themes and their impact in foregrounding the textual features has been largely ignored and under researched. This study is intended to explore the efficacy of stylistic sentences in the isiXhosa creative work Ukhozi Olumaphiko. In Ukhozi Olumaphiko, the author artfully employs periodic, cumulative, and balanced stylistic sentences for the realization of different purposes in the story. In this study, content analysis has been used as a qualitative and quantitative research technique, as it allowed for a detailed examination of the novel Ukhozi Olumaphiko. Stylistic sentences were identified, interpreted, and coded, using integer coding for classification. Employing literary stylistics as a theoretical approach, the stylistic sentences were analysed according to their literary impact and effect. The findings indicate that the author utilises periodic sentences predominantly in the beginning stages of the story, a spread of cumulative, balanced, and periodic sentences in the middle stages, and periodic and cumulative sentences more in the end stages of the novel. The stylistics mentioned enhance the themes, textual meanings, and narrative features of Ukhozi Olumaphiko text and are useful in weaving suspense in a way that captures the reader’s attention and evokes emotions. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

The elevated precision of data regarding the Earth’s surface, facilitated by the enhanced interoperability among various GNSSs (Global Navigation Satellite Systems), enables the classification of land use and land cover (LULC) via satellites equipped with optical sensors, such as Sentinel-2 of the Copernicus program, which is crucial for land use management and environmental planning. Likewise, data from SAR satellites, such Copernicus’ Sentinel-1 and Jaxa’s ALOS PALSAR, provide diverse environmental investigations, allowing different types of spatial information to be analysed thanks to the particular features of analysis based on radar. Nonetheless, in optical satellites, the relatively low resolution of Sentinel-2 satellites may impede the precision of supervised AI classifiers, crucial for ongoing land use monitoring, especially during the training phase, which can be expensive due to the requirement for advanced technology and extensive training datasets. This project aims to develop an AI classifier utilising high-resolution training data and the resilient architecture of ResNet, in conjunction with the Remote Sensing Image Classification Benchmark (RSI-CB128). ResNet, noted for its deep residual learning capabilities, significantly enhances the classifier’s proficiency in identifying intricate patterns and features from high-resolution images. A test dataset derived from Sentinel-2 raster images is utilised to evaluate the effectiveness of the neural network (NN). Our goals are to thoroughly assess and confirm the efficacy of an AI classifier utilised on high-resolution Sentinel-2 photos. The findings indicate substantial enhancements compared to current classification methods, such as U-Net, Vision Transformer (ViT), and OBIA, underscoring ResNet’s transformative capacity to elevate the precision of land use classification. Full article

Head and neck cancer (HNC) remains a major global health issue. It is closely linked to smoking, alcohol use, and HPV infection. Nutritional and metabolic factors significantly influence surgical outcomes in these patients, especially when undergoing extensive resections and microsurgical free flap reconstruction. This comprehensive review aims to evaluate how perioperative nutritional status—particularly body mass index (BMI), serum albumin and prealbumin levels, and enteral vs. oral feeding strategies—affects complication rates, wound healing, surgical duration, and overall recovery. Poor nutritional status is associated with increased complication rates, prolonged surgery, impaired wound healing, and higher perioperative mortality. Both high and low BMI negatively impact surgical outcomes. Obesity is linked to protracted surgery and increased blood loss, while underweight patients have higher perioperative mortality. Optimizing perioperative nutrition is important for improving surgical outcomes in HNC patients. A multidisciplinary approach is necessary to tailor nutritional support and enhance recovery. Further research should focus on long-term weight management strategies and identifying biomarkers predictive of surgical success. Full article

Spaceborne radar is a powerful tool for probing planetary subsurface structures. Earlier radar studies of the Moon have primarily examined large-scale horizontal structures. However, recent discoveries of vertical holes suggesting the existence of lava tubes and theoretically predicted subsurface gas voids formed by volatiles in magma have highlighted the importance of small-scale subsurface structures. We developed a method using SELENE Lunar Radar Sounder (LRS) data to detect small-scale subsurface echoes (hundreds of meters). Surface scattering simulations incorporating incoherent scattering from sub-resolution roughness were performed using a high-resolution digital elevation model generated by a generative adversarial network. Detection thresholds for subsurface echo candidates (SECs) were determined from the histograms of difference intensities between LRS and simulation B-scans. Results show that some SECs exist in the extension area of the analyzed graben. SECs were also detected continuously across multiple LRS ground tracks in areas unrelated to grabens. Using the radar equation analysis, the echo intensities of SECs could be explained for subsurface structures with 50–600 m widths and dielectric constants of 1–4. This suggests the existence of either subsurface voids or materials with a high porosity of more than 35%. Among the SECs detected continuously across multiple LRS ground tracks, those that are more or less aligned in the downward elevation direction are likely indicative of lava tubes. On the other hand, the SECs distributed along the extension of the graben are aligned parallel to the contour lines. These SECs likely suggest gas voids at the tip of the intrusive magma that formed the graben. Full article