Satellite Platforms Research Articles

Remote Sensing Tools for Monitoring Marine Phanerogams:A Review of Sentinel and Landsat Applications

Seagrasses play a pivotal role in maintaining marine ecosystems, supporting biodiversity, and preventing sediment loss during storms. Their capacity for photosynthesis and growth is linked to light availability in the continental shelf waters. Satellite platforms such as Landsat (USGS) and Sentinel (ESA) provide accessible imagery for the monitoring of these submerged plants. This study employed the PRISMA methodology to conduct a systematic review of the literature, with the objective of identifying articles focused on these seagrasses and their detection via satellite imagery. The identified methodologies included the use of vegetation and water indices, which were validated through empirical observations, as well as supervised classification algorithms, such as Random Forest, Maximum Likelihood, and Support Vector Machine. These approaches were applied to Mediterranean and other coastal regions, revealing changes in seagrass cover due to anchor damage in tourist areas and trawling scars that resemble plough marks. Such tools are vital for informing management actions, such as the implementation of restrictions on anchoring and bottom trawling, in order to protect these vulnerable ecosystems. By enabling targeted interventions, this approach facilitates the preservation of seagrass meadows, which are also critical for carbon sequestration and the sustainability of marine habitats.

RFSoC receiver calibration system for 21-cm global spectrum experiments from space: The CosmoCube case

Abstract The CosmoCube project plans to deploy a global 21-cm spectrometer with 10-100 MHz observation band in a lunar orbit. The farside part of such an orbit, i.e. the part of orbit behind the Moon, offers an ideal site for accurately measuring the 21-cm signal from the Dark Ages, Cosmic Dawn and Epoch of Reionization, as the effects of the Earth’s ionosphere, artificial radio frequency interference (RFI), and complex terrain and soil are all avoided. Given the limitations of a satellite platform, we propose a receiver calibration system design based on a Radio Frequency system-on-chip, consisting of a Vector Network Analyzer (VNA) sub-system, and a source switching sub-system. We introduce the measurement principle of the VNA, and discuss the effect of quantization error. The accuracy, stability and trajectory noise of the VNA are tested in laboratory experiments. We also present the design of the source-switching sub-system, generating mock datasets, showing that the imperfect return loss, insertion loss, and isolation of surface-mounted microwave switches have a minimal effect on the sky foreground fitting residuals, which are within ±10 mK under optimal fitting condition. When all possible measurement errors in reflection coefficients and physical temperatures are taken into account, the foreground fitting residuals for the 50-90 MHz part of the spectrum remain around ±20 mK.

Coral reef thermal microclimates mapped from the International Space Station

Abstract Satellite sea surface temperature (SST) is critical for describing marine environments. Traditional SST data, such as those provided by the Group for High Resolution Sea Surface Temperature (GHRSST) program, are valuable, but have a relatively coarse spatial resolution for mapping coral reef thermal environments. Hence, fine resolution SST from orbit would be of great utility to the coral reef research community and speed the pathway to an increased understanding of how, when, and where thermal stress afflicts individual reefs. Such data would support adaptive management, especially so for the design of marine protected areas. Flying aboard the International Space Station, the NASA ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) instrument may already fill this niche with a spatial resolution 204 times finer than GHRSST. To evaluate ECOSTRESS thermal data over reef environments, we deployed 21 temperature loggers over three years across two reef sites in the Red Sea. We compared temperature retrievals from both the coarse resolution GHRSST and the fine resolution, experimental, ECOSTRESS, to this in-situ logger dataset. While temperature data from both orbital platforms correlated strongly with the logger recordings, only ECOSTRESS, with its 70-m pixels, could construct thermal microclimate maps capturing the dynamic temperature fluctuations experienced by our studied reefs. We contend that ECOSTRESS represents a significant advancement in the capability to monitor heat stress on reefs from orbit.

Joint satellite platform and constellation sizing for instantaneous beam-hopping in 5G/6G Non-Terrestrial Networks

Existing research on resource allocation in satellite networks incorporating beam-hopping (BH) focuses predominantly on the performance analysis of diverse algorithms and techniques. However, studies evaluating the architectural and economic impacts of implemented BH-based technical solutions have not yet been addressed. Aiming to close this gap, this contribution quantifies the impact of BH and orbital parameters on the satellite platform and constellation size, considering specific traffic demand and service time indicators. The paper proposes a low complexity, instantaneous demand-based BH resource allocation technique, and presents a comprehensive analysis of LEO and VLEO scenarios using small platforms, lying on 5G/6G Non-Terrestrial Network (NTN) specifications. Given a joint set of traffic demand and time-to-serve indicators, and based on a feasible multibeam on-board antenna architecture, the paper compares the RF transmit power requirements in fixed and variable grid LEO schemes, and in VLEO with different minimum elevation angles, to assess the feasibility of these orbits. For a fixed minimum elevation and number of users, the RF transmit power and satellite platform requirements are significantly reduced when transitioning into lower altitudes with narrower satellite coverage areas. The relevant trade-off between the satellite platform and the size of the constellation required for global coverage is presented, to fulfill a set of traffic demand and time to serve indicators: approximately, 1156 3U satellites are required for VLEO constellation and 182 12U satellites for LEO. Once platform and constellation sizing trade-off is quantified, the paper estimates the economic costs for each of the deployments, showing a total cost of almost the double for the presented VLEO constellation compared to the LEO one. The article aims to provide system engineers and satellite operators with crucial information for satellite system design, dimensioning and cost assessment.

Bushfire recovery at a long-term tall eucalypt flux site through the lens of a satellite: Combining multi-scale data for structural-functional insight

Satellite earth observation (EO) data plays a vital role quantifying vegetation structural and functional metrics across spatio-temporal scales. However, the degree of coupling between satellite derived spectral signals and the rate of photosynthesis, as estimated by Gross Primary Productivity (GPP), both before and after bushfire remain understudied, yet these are a critical part of the global carbon cycle. This study evaluated a combination of passive optical and active LiDAR satellite data to quantify the disturbance and recovery of photosynthesis from a major fire event. The work was completed at the Tumbarumba long-term tall eucalypt flux site following a catastrophic bushfire in December 2019. TROPOMI solar-induced fluorescence (SIF) and Sentinel 2 derived greenness and burn severity metrics (NDVI, EVI, NIRv, and NBR) were investigated, termed ‘spectral metrics’ herewith. Detailed in-situ observations from leaf-to-canopy scales were utilised to examine variations in vegetation structural-functional parameters.We found the rate of vegetation spectral metrics recovery largely outpaced GPP recovery at the one- and two-year post-fire mark. Specifically, SIF recovered to 80–90 % compared to pre-fire levels, whereas GPP recovered only 45–50 %. This indicated that separate SIF:GPP functions were required for pre- and post-fire data to account for different recovery trajectories due to changes in canopy structure and species composition. The use of TROPOMI SIF for monitoring canopy productivity at seasonal (monthly) time-scales was advantageous over traditional greenness-based indices, as SIF tracked GPP seasonality both pre- and post-fire. Spaceborne GEDI LiDAR data effectively captured post-fire changes in forest structure, albeit at sparse spatio-temporal sampling intervals, revealing a significant reduction in overstorey vegetation density and a concurrent increase in understorey vegetation density. This contributed to reduced carbon uptake, compared to pre-fire, due to the lower light use efficiency of understorey species, which was verified with in-situ gas exchange measurements. Overall, this study highlights the importance of accounting for disturbance history and the relative abundance of overstorey and understorey vegetation for tracking GPP from satellite platforms. Our results also highlight the crucial role of longitudinal field-based data for calibration and validation of EO data, ultimately enhancing our understanding of forest recovery processes.

Utilizing the cloud-based satellite platform to explore the dynamics of coastal aquaculture ponds from 1986 to 2020 in Shandong Province, China.

Coastal pond aqua farming is critical in aquaculture and significantly contributes to the seafood supply. Meanwhile, the development of aquaculture ponds also threatens vulnerable wetland resources and coastal ecosystems. Accurate statistics regarding the distribution and variability of coastal pond aquaculture are crucial for balancing the sustainable development of coastal aquaculture and preserving the coastal environment and ecosystems. Satellite imagery offers a valuable tool for detecting spatial-temporal information related to these coastal ponds. Furthermore, integrating multiple remote sensing images to acquire comprehensive spatial information about the coastal ponds remains challenging. This study utilized a decision-tree classifier applied to Landsat data to detect the spatial distribution of coastal ponds in Shandong Province from 1986 to 2020, with data analyzed at five-year intervals, primarily based on the Google Earth Engine cloud platform. A pond map in 2020, extracted from Sentinel-2 imagery, was used as a reference map and combined with the results from Landsat data to explore the landscape changes of coastal ponds. The results indicated that Shandong Province's coastal pond area underwent significant expansion before 1990, followed by slower growth from 1990 to 2010 and eventual shrinkage after 2010. Specifically, the pond area expanded from 428.38km2 in 1986 to a peak of 2149.51km2 in 2010 before contracting to 2012.39km2 in 2020. The region near Bohai Bay emerged as the epicenter of Shandong's coastal aquaculture, encompassing 62% of the total pond area in 2020. The government policies previously promoted the expansion of coastal pond farming but shifted to curbing the uncontrolled development of aquaculture ponds.

EROSITA X-ray analysis of the PeVatron candidate Westerlund 1

It is still unclear which fraction of cosmic rays above an energy of electronvolt is accelerated by the observed Galactic PeVatron population. These sources of unknown physical origin are detected through their γ-ray emission, which also identifies them as particle accelerators. However, their γ-ray data are typically degenerate between hadronic and leptonic emission scenarios, which hinders their straightforward association with the mainly hadronic cosmic ray population. In this study, we aimed to distinguish between leptonic and hadronic particle acceleration scenarios for the PeVatron candidate HESS J1646-458, which is associated with the star cluster Westerlund 1 (Wd 1). To this end, we first studied the diffuse X-ray emission from Wd 1 to better understand if its origin is of thermal or nonthermal nature. In addition, we searched for X-ray synchrotron emission from the associated PeVatron candidate HESS J1646-458 to put new constraints on the magnetic field strength and the leptonic particle population of this source. We used data from the all-sky surveys 1 to 4 of the extended Roentgen Survey with an Imaging Telescope Array eROSITA ) on board the Spectrum-Roentgen-Gamma orbital platform to spectrally analyze the diffuse emission from Wd 1 and HESS J1646-458. For Wd 1, we fitted and compared a purely thermal model and a model with a thermal and a nonthermal component. Next, we analyzed the spectra of four annuli around Wd 1 that coincide with HESS J1646-458 to search for synchrotron radiation. We find that eROSITA data cannot distinguish between thermal and nonthermal source scenarios for the diffuse emission from Wd 1 itself. For a thermal source scenario, the observed X-ray flux can be explained in large part by unresolved pre-main sequence stars or by thermalized stellar wind shocks. In the case of the PeVatron candidate HESS J1646-458, we find no evidence of synchrotron emission. We estimated an upper confidence bound of the synchrotron flux up to arcmin around Wd 1 of 1.9e-3 second . We used this result to study the spectral energy distribution of the source. From that, we obtained an upper $1σ$ confidence bound on the magnetic field strength of HESS J1646-458 of SI gauss Our upper bound on the magnetic field strength in HESS J1646-458 is compatible with a previous estimate in the literature for a fully leptonic source scenario. Therefore, a purely leptonic emission scenario is compatible with our results. The same is the case for hadronic and hybrid scenarios, for which even less synchrotron flux is expected compared to the leptonic scenario.

Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI

Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI

Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.

Decoding the Biobased Blueprint: Key Players and Evolutionary Trends in Materials Innovation.

In the rapidly evolving biobased materials innovation landscape, our research identifies key players and explores the evolutionary perspective of biobased innovation, offering insights into promising research areas to be further developed by biobased material scientists in search of exploiting their knowledge in novel applications. Despite the crucial role of these materials in promoting sustainable production and consumption models, systematic studies on the current innovation terrain are lacking, leaving gaps in understanding key players, emerging technologies, and market trends. To address this void, we focused on examining patents related to biobased monomers and polymers, aiming to describe the innovation strategies and business dynamics of leading assignees. Embedded within the European Sustainable BIO-based nanoMAterials Community (BIOMAC) project, a Horizon 2020 initiative, our research leverages this unique framework dedicated to advancing the innovation landscape, specifically emphasizing the market readiness of biobased materials. We implemented a multi-stage strategy, prioritizing validated keyword queries to ensure the superior quality and reliability of the collected data. To understand primary contributors within these landscapes, we conducted an in-depth analysis of innovation strategies employed by leading companies. Findings from the ORBIT platform highlighted a remarkable increase in patent publications in the past decade, with China standing out as a key hub of innovation, signaling a strong focus on the development of these materials. Our research explores technological advancements in biobased materials to identify specific areas with potential for further development. By analyzing innovation trends in five key industries, we pinpoint opportunities for innovative solutions to be commercially exploited while ensuring compliance with intellectual property rights within a freedom-to-operate framework.

Plot‐level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials

Societal Impact StatementMaize plays a key role in agricultural profitability and food security on six continents. Successful efforts to breed higher‐yielding maize varieties depend on replicated yield trials in many environments. Capturing in‐season data can help improve and accelerate the development of regionally adapted hybrids, but collecting these data can be impractical in many locations. We demonstrate that satellite remote sensing could play a similar role in crop performance assessment as unmanned aerial vehicles (UAVs) but with far lower labor costs and the greatest ease of collection at remote field sites. This dataset and benchmarks have the potential to enable predictive models that could guide farmers and crop breeders in decision‐making.Summary Accurate early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices and breeders in screening thousands of varieties contributing to improving agriculture and food production systems. Effective approaches to track plant growth and predict yield require large datasets of remote sensing and ground truth data collected across multiple environments. Low‐altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at regional scales. Satellite platforms exhibit logistical and technical advantages in scalability and accessibility and could facilitate plot‐level predictions, especially with steadily improving spatial resolution. However, plot‐level, high‐resolution satellite images capturing differences in genotypes from multiple environments with ground truth measurements are not publicly available. Here, we generated, described, and evaluated over 20,000 plot‐level images of over 80 hybrid maize varieties grown across the US corn belt under various management practices collected from (near simultaneous) satellite and drone (synonym UAVs, UASs) flights integrated with ground truth yield measurement. Of the six baseline models examined, models employing data collected from satellite images often matched the performance of models employing drone images for both within and cross‐environment yield prediction.

Research on design and control method of active vibration isolation system based on piezoelectric Stewart platform.

Space payloads in orbit are vulnerable to small vibrations from satellite platforms, which can degrade their performance. Traditional methods typically involve installing a passive vibration isolation system between the platform and the payload. However, such systems are usually effective only for high-frequency, large-amplitude vibrations and perform poorly in isolating low-frequency vibrations and resonances below 10Hz. To address this limitation, this paper proposes an active vibration isolation system using a 6-degrees-of-freedom Stewart platform driven by piezoelectric actuators. First, the characteristics of the Stewart platform are analyzed and modeled, with the deformation displacement of each leg calculated through decoupling, allowing for high-precision servo control. Next, given the inherent hysteretic nonlinearity of piezoelectric ceramics, which significantly affects positioning accuracy, the hysteresis mechanism of the actuators is analyzed, and a phenomenological mathematical model based on Bouc-Wen operators is established. A Modified particle swarm optimization (MPSO) method is proposed for identifying the model's nonlinear parameters, significantly enhancing the optimization efficiency. Finally, feedforward inverse compensation and feedback linearization methods are introduced. Experimental results verify that the designed active-passive vibration isolation system greatly improves both the positioning accuracy of the piezoelectric actuators and the active vibration isolation performance of the platform.

The Detection of Small-Scale Open-Burning Agriculture Fires Through Remote Sensing

The open burning of agricultural residues is a widespread practice with significant environmental implications. This study explores the potential of satellite remote sensing to detect and analyze small-scale agricultural fires in Portugal, focusing on their spatial and temporal characteristics. Using active fire detection products from various satellite platforms, including VIIRS, MODIS, SLSTR, and SEVIRI, we conducted a detailed analysis across two local case studies and a national-scale assessment. This study evaluates both active fire detections and post-fire burned area estimations, using high-resolution satellite imagery to overcome the limitations associated with the small size and low intensity of these fires. The results indicate that while active fire detections are feasible for larger-scale burning, challenges remain for smaller fires due to resolution constraints. A systematic comparison with an agricultural burning request database further highlights the need for the enhancement of temporal and spatial precision in data to improve detection reliability. Despite these limitations, this work underscores the importance of remote sensing tools in monitoring agricultural burning practices and enhancing environmental management efforts.

Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

Strategies for detecting land-use change on the River Tea SCI ecological corridor via satellite images

Land-cover change is often accompanied by land-use change, altering ecosystem services, species habitats and contributing to climate change. The general goal of this study is to analyse different sources of information to quantify land use and land cover change (LULCC) in the River Tea SCI (Galicia, NW Spain) in 2015–2023. The study area has multiple coverages with very low variability between them and with a great deal of fragmentation in the territory, which makes it difficult to obtain high accuracy levels. Land cover was classified using object-based image analysis (OBIA) Classification and Artificial Neural Network (ANN) methodologies based on images from the Sentinel-2 and Planet Labs (RapidEye and PlanetScope) multispectral satellite platforms. In addition, simulations for 2031 were carried out using different techniques based on post-classification. The highest accuracy obtained in this study is 80 %, for the Planet Labs data and the OBIA methodology. Using the same methodology and Sentinel-2 data the best accuracy obtained is around 70 %, with the Planet Labs data and the ANN developed the accuracy is around 55 %. Thus, the data used and the methodology followed influence the accuracy levels obtained in the classifications. The choice of what data and methodology are to be used depends on the goals pursued and on the characteristics of the study area. Finally, it is concluded that the geospatial data available are very useful for detecting and quantifying changes in land cover. It is confirmed that the methodology used contributes to territorial planning and sustainable forest management, facilitating future decisions and action plans in the governance of the region.

Geographically-Informed Modeling and Analysis of Platform Attitude Jitter in GF-7 Sub-Meter Stereo Mapping Satellite

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Enhancing crop yield estimation from remote sensing data: a comparative study of the Quartile Clean Image method and vision transformer

The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2015, improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art technique, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.

Rapid retrieval of soil moisture using a novel portable L-band radiometer in the Hulunbeier Prairie, China

ABSTRACT The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management.