Terrestrial ecosystems exchange carbon fluxes (CFs) with the atmosphere. The three main CFs are gross primary production (GPP), terrestrial ecosystem respiration (TER), and net ecosystem exchange (NEE). GPP stands for the total amount of carbon that is fixed by plants through photosynthesis, whereas TER refers to carbon that is released into the atmosphere by respiration, and NEE is the difference between GPP and TER. As CFs are related to CO₂ assimilation and biomass production, they can be used to evaluate the state of ecosystems. This work aims to quantify long-term changes of CFs using EO data of terrestrial ecosystems at the regional scale in mainland Spain. Eight days' worth of CF images (and uncertainties), at a 1 km scale for the 2002–2023 period, were obtained from a daily global monitoring product elaborated on by the authors using machine learning tools. A data-driven approach based on a multi-output Gaussian process regression algorithm, blending MODIS products and in situ eddy covariance data, allowed us to jointly estimate GPP, TER, and NEE, preserving their physical relationship. A novel methodology based on nonlinear Bκ embeddings (NLEs) that can analyze time series at various temporal scales was applied. CF time series are nonlinearly embedded into an encompassing mathematical structure that depends on a continuous parameter k, which gives the NLE method a great flexibility and enormous potential to analyze the series at various temporal scales (just changing the k value). Its potential to map subtle, long-term changes has been demonstrated using NDVI data. NLE is able to quantify vegetation changes with very low uncertainty and high statistical significance by means of the slope Q of the trend (or inter-annual component of the time series). A map of statistically significant areas showing vegetation greening (positive Q) and browning (negative Q) is produced, showing the state of ecosystems in this highly vulnerable area.

The Polarimetric Radio Occultation (PRO) technique tracks GPS signals captured by Low-Earth-Orbit (LEO) satellites as they rise or set behind the Earth's limb. This method extends the capabilities of the traditional Radio Occultation (RO) approach by not only measuring the vertical profiles of thermodynamic variables but also incorporating polarimetric information. Unlike standard RO, PRO uses two orthogonal linear polarizations, horizontal (H) and vertical (V), offering relevant insights into atmospheric conditions.

Since its deployment aboard the PAZ satellite in 2018, the GNSS-PRO concept has been successfully demonstrated. In 2023, it was further implemented aboard three of Spire Global’s commercial CubeSats. The polarimetric capability of PRO allows it to retrieve the vertical profiles of differential phase shift (ΔΦ), the difference in phase delay between H and V polarizations. Heavy precipitation events, dominated by oblate spheroid-like hydrometeors, induce a positive differential phase shift as PRO signals traverse these conditions, enabling unique information into the microphysical properties of these precipitation events.

The primary hypothesis that PRO onboard PAZ is sensitive to oblate raindrops was validated; also, its response to frozen hydrometeors was unexpectedly demonstrated. The technique's performance was corroborated through comparisons with two-dimensional data like IMERG-GPM products and three-dimensional data from NEXRAD weather radars.

Ongoing analyses focus on evaluating the sensitivity of the Polarimetric Radio Occultation (PRO) technique to various microphysical parameterizations derived from the Weather Research and Forecasting (WRF) model and particle habits simulated using the Atmospheric Radiative Transfer Simulator (ARTS). This research is particularly centered on Atmospheric Rivers, aiming to examine how variations in microphysical parameterizations influence the PRO technique's ability to detect and characterize hydrometeors. These findings will contribute to a more comprehensive understanding of the phenomena associated with extreme weather systems, ultimately advancing the application of PRO in atmospheric science.

Land surface temperature (LST) is a key indicator of thermal dynamics and environmental change, with critical applications in evapotranspiration (ET) estimation, urban heat monitoring, and drought assessment. The Sustainable Development Science Satellite 1 (SDGSAT-1), with its three thermal infrared bands, offers significant potential for high-resolution LST retrieval, yet lacks established algorithms and calibration parameters. This study presents a comprehensive calibration and validation approach for LST retrieval from SDGSAT-1 Thermal Infrared Spectrometer (TIS) data using both the split-window (SW) and three-channel (TC) methods, applicable under varied atmospheric conditions, day and night. In this process, the atmospheric profile data from the Thermodynamic Initial Guess Retrieval (TIGR) dataset and observation data from the University of Wyoming were used to build LST retrieval models.
Validation was performed with in situ LST measurements from sites in China and the SURFRAD network in North America. The models achieved high accuracy, with root-mean-squared errors (RMSEs) of 2.507 K (daytime) and 2.272 K (nighttime) for the SW method, and 2.847 K (daytime) and 1.923 K (nighttime) for the TC method. Models using University of Wyoming data outperformed those with TIGR2000 profiles, underscoring the value of accurate atmospheric profiles. The proposed models are robust across varying atmospheric water vapor content and surface conditions. In this study, we provide an innovative LST retrieval solution for SDGSAT-1 TIS data, enabling high-precision temperature prediction and shedding light on climate change patterns and trends. Overall, we propose practical tools to achieve the UN Sustainable Development Goals, bolstering our understanding of climate change while advancing sustainability globally.

Microwave oil film thickness (OFT) inversion has always been a difficult problem. Based on the empirical relationships between the oil film’s normalized radar cross section (NRCS) obtained from a field experiment and the inversion of the oil film thickness retrieved from a synchronous optical image, several rules have been identified. Inversion formulas suitable for different oil film thickness ranges under certain conditions are established. Following experimental data verification, our results show that the correlation coefficient is greater than 0.99, and the root mean square error is less than 15 µm.

We aim to apply the research results to the oil spill event observed by spaceborne SAR.

The damping ratio (DR) of a spaceborne synthetic aperture radar (SAR) image is used as a bridge to establish the correlation between the optical oil film thickness and the SAR-inverted oil film thickness. By synchronizing SAR and optical oil spill inversion data, the oil film thickness obtained from SAR data is closer to the measured data. It is found that the oil film thickness of the SAR image can explain the spatial distribution of the oil film. This study provides a new idea for spaceborne SAR oil film thickness inversion.

Abstract: With increasing threats to forest resources, there is a growing demand for accurate, timely, and quantitative information on their status, trends, and sustainability. Satellite remote sensing provides an effective means of consistently monitoring large forest areas. Vegetation Indices (VIs) are commonly used to assess forest conditions, but their effectiveness remains a key issue. This study aimed to assess and map forest degradation status and trends in the Lagawa locality, West Kordofan State, Sudan, using the Soil-Adjusted Atmospheric Resistant Vegetation Index (SARVI), quantify the relationship between SARVI and the Normalized Difference Vegetation Index (NDVI), and compare the efficiency of both indices in detecting and monitoring changes in forest conditions. The study utilized four free-cloud images (TM 1988, TM 1998, TM 2008, and OLI 2018) that were processed using GEE to derive the indices. The study found significant forest degradation over time, with 63% of the area categorized as moderately to severely degraded. A strong, positive relationship between SARVI and NDVI (R² = 0.9085, P <0.001) was identified, indicating both are effective in detecting forest changes. Both indices proved effective, cost-efficient, and applicable for monitoring forest changes across Sudan's drylands. The study recommends applying similar methods in other arid regions.

Dinder National Park (DNP) is one of Sudan’s most significant protected areas, playing a critical role in biodiversity conservation and ecosystem services. However, like many protected areas, DNP faces growing challenges from climate change, human activities, and land use pressures, necessitating detailed and continuous monitoring to ensure its sustainability. This study explores land use and land cover (LULC) changes in Dinder National Park over the period from 2014 to 2024. Utilizing Sentinel-1 and Sentinel-2 imagery processed in Google Earth Engine (GEE), the study assesses vegetation health using indices such as NDVI, EVI, and RVI. A Random Forest classifier was employed to delineate key LULC classes, including trees, cropland, water bodies, grasslands, flooded vegetation, shrubland, built areas, and bare land. The analysis revealed a significant increase in tree cover by 18.3%, while cropland and shrubland decreased by 8.85% and 4.2%, respectively. These shifts, influenced by both natural and anthropogenic factors, reflect critical changes in the park's ecosystem. The findings offer valuable insights for sustainable land management and emphasize the necessity of continuous monitoring in this ecologically sensitive region.

The territory of the Kingdom of Saudi Arabia is relatively safe and does not contain large landslide-prone regions. However, the west coast of the Arabian Peninsula is susceptible to landslide activity. Therefore, monitoring of these regions is urgently needed. The best way to observe the areas susceptible to landslide activity is the application of remote sensing technologies, particularly InSAR. The presented paper analyzes the monitoring results in Jizan province using InSAR observations. The primary goal was to develop a landslide-forecasting model using various data types. The InSAR monitoring data cover observation epochs from 2020 to 2023. Since the monitoring region lacks reliable reflecting surfaces, the displacements were obtained using the SBAS processing algorithm in the open-source Python package Miami INsar Time-series software. Apart from displacements, meteorological parameters and the landslide susceptibility index were used as independent variables for forecasting model creation. The best way to gain the advantages of different data fusion techniques is by employing a machine learning approach. The group method for data handling (GMDH) algorithm was used for the forecasting model simulation. Different GMDH processing strategies were tested, and the optimal one was selected. The Combinatorial GMDH and Generalized Iterative Algorithm GMDH provided the best prediction outcomes. The analysis only showed an acceptable correlation between displacements and precipitations. The correlation between temperature and displacements was positive but statistically insignificant. GMDH algorithms demonstrated high efficiency and flexibility in analyzing such complex data. The simulation results show that points placed in medium- and high-landslide-activity areas have an average settlement velocity of around -7 ± 2 mm/year and an average uplift velocity of around +9 ± 1 mm/year for some regions.

Droughts are increasing in frequency, magnitude and impact. Agroecosystems are anthropogenic ecosystems with high water demand, providing essential ecosystem services. Although water use efficiency has increased in agriculture in the last few decades, drought management should be based on long-term strategies for proactive water management, rather than crisis management. The AgrHyS network of sites in French Brittany collects high-resolution data on soil moisture across agronomic stations and catchments. Remote sensing has proven to be an excellent tool in upscaling point measurements up to catchment scale by using images captured by UAVs or satellites. Mapping soil moisture and plant water stress is crucial to perform water stress risk assessments. Our research seeks to upscale in situ point measurements of soil moisture and plant water stress not only in agricultural areas but also in natural ecosystems to assess the usability of UAV and satellite images in tracking and monitoring soil moisture. Our objectives are as follows: i) to demonstrate the applicability of UAVs in mapping soil moisture (SM) and plant water stress (PWS) by upscaling in situ measurements and to ii) evaluate the use of in situ measurements from a highly instrumented eLTER site in validating these essential water stress indicators.

In recent decades, grasslands have become increasingly important due to their great potential to contribute to the Sustainable Development Goals (SDGs) as key ecosystems for biodiversity conservation, agronomic production, erosion control, and regulation of the carbon cycle, among other factors. In this sense, European authorities are particularly interested in preserving these ecosystems. The new Common Agricultural Policy, which proposes agronomic practices to improve and maintain grasslands, is an example of this interest. For these reasons, the study and monitoring of grasslands is essential to improve our knowledge of the dynamics and functioning of these ecosystems, allowing us to develop more sustainable management practices. In this sense, remote sensing and time series analysis allow us to study the behaviour of these ecosystems in space and time. Therefore, in this work, based on Corine Land Cover 2018 (CLC18) cartography, and through the synergic use of remote sensing products such as MODIS MOD09Q1 and time series analysis, we try to characterise different grassland dynamics from the point of view of their annual vegetation cycles, trends, and structural changes. Finally, a cartography is developed showing the distribution of grasslands according to these characteristics.

For several decades, aircraft have been extensively used in both civil and military applications. Currently, we are witnessing a proliferation of Unmanned Aerial Vehicles (UAVs) with various shapes in both civil and military domains for different purposes. For example, drones can be used in the production of cinema movies as well as for precise offensive strikes on the battlefield. However, these UAVs are usually smaller than modern fighter aircraft and have a very low Radar Cross Section (RCS), which prevents radars from reliably detecting them. This implies significant security issues, as inexpensive drones can be used for area surveillance or offensive tactics. Such a threat has led governments, like those in Europe in 2019, to enact new laws to curb the increasing utilization of drones.

Nevertheless, it could be possible to enhance radar capability to detect and identify drones using the micro-Doppler effect, as many UAVs use propellers to move. Therefore, our problem lies in how to improve radars' capability to detect and identify drones in various environments (sea, urban area, forests, etc.) using the micro-Doppler signatures of targets. In this perspective, we focus on the detailed modeling and simulation of the micro-Doppler effect produced by drone-type targets and the characterization of the simulated radar signal based on time–frequency representations.

A few simulations have been carried out using physical optics methods and with the software FEKO in various simplified configurations. Preliminary results show that characteristic patterns appear in time–frequency representations depending on the parameters of the drone system (tilt, relative speed, blade rotation speed, blade dimensions, etc.) that could help in the detection and identification of drone-type targets.