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Expanding upon the well-established Takagi–Sugeno (T–S) fuzzy model, the T–S fuzzy descriptor model emerges as a robust and flexible framework. This article introduces the development of optimal and robust-optimal controllers grounded in the principles of stability control and fuzzy descriptor systems. By transforming complicated inequalities into linear matrix inequalities (LMI), we establish the essential conditions for controller construction, as delineated in theorems. To substantiate the utility of these controllers, we employ the rotary inverted pendulum as a testbed. Through diverse simulation scenarios, these controllers, rooted in fuzzy descriptor systems, demonstrate their practicality and effectiveness in ensuring the stable control of inverted pendulum systems, even in the presence of uncertainties within the model. This study highlights the adaptability and robustness of fuzzy descriptor-based controllers, paving the way for advanced control strategies in complex and uncertain environments.

This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.

Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more relevant for all studies related to extreme event (e.g., floods, droughts, and landslides) mitigation and assessment. In this study, data acquired by the advanced microwave sounding unit (AMSU) onboard the European Meteorological Operational Satellite Program (MetOP) satellites were used for the first time to extract information on the variability of SM by implementing the original soil wetness index (SWI). Long-term monthly SWI time series collected for the Basilicata region (southern Italy) were analyzed for drought assessment during the period 2007–2021. The accuracy of the SWI product was tested through a comparison with SM products derived by the Advanced SCATterometer (ASCAT) over the 2013–2016 period, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the relevance of the long-term achievements in terms of drought analysis. The results indicate a satisfactory accuracy of the SWI, with the mean correlation coefficient values with ASCAT higher than 0.7 and a mean normalized root mean square error less than 0.155. A negative trend in SWI during the 15-year period was found using both the original and deseasonalized series (linear and Sen’s slope ~−0.00525), confirmed by SPEI (linear and Sen’s slope ~−0.00293), suggesting the occurrence of a marginal long-term dry phase in the region. Although further investigations are needed to better assess the intensity and main causes of the phenomena, this result indicates the contribution that satellite data/products can offer in supporting drought assessment.

This paper presents a study on controlling a rotary inverted pendulum (RIP) system using a hierarchical sliding mode control (HSMC) approach. The objective is to swing up and stabilize the pendulum at a desired position. The proposed HSMC controller addresses the underactuation challenge through a hierarchical structure of sliding surfaces. The particle swarm optimization (PSO) algorithm is used to optimize the controller parameters. Simulations were performed to evaluate the performance of the HSMC controller at different initial pendulum angles, demonstrating its effectiveness in achieving swing-up and stabilization. The integration of the PSO algorithm enhances the controller’s adaptability and robustness, emphasizing the benefits of combining optimization algorithms with controller parameter tuning for underactuated systems like the RIP.

Lake Chad, in the Sahelian zone of west-central Africa, provides food and water to ~50 million people and supports unique ecosystems and biodiversity. In the past decades, it became a symbol of current climate change, held up by its dramatic shrinkage in the 1980s. Despites a partial recovery in response to increased Sahelian precipitation in the 1990s, Lake Chad is still facing major threats and its contemporary variability under climate change remains highly uncertain. Here, using a new multi-satellite approach, we show that Lake Chad extent has remained stable during the last two decades, despite a slight decrease of its northern pool. Moreover, since the 2000s, groundwater, which contributes to ~70% of Lake Chad’s annual water storage change, is increasing due to water supply provided by its two main tributaries. Our results indicate that in tandem with groundwater and tropical origin of water supply, over the last two decades, Lake Chad is not shrinking and recovers seasonally its surface water extent and volume. This study provides a robust regional understanding of current hydrology and changes in the Lake Chad region, giving a basis for developing future climate adaptation strategies.

This study compares the capability of Sentinel-1, Sentinel-2, and PlanetScope (PS) satellites in monitoring the variations of surface water of Dai Lai Lake, located in North Vietnam, for the 2018–2023 period. The analysis involves the utilization of Google Earth Engine to partially process Sentinel-1 and Sentinel-2 observations, while PS observations are processed using local computers, to generate VH-polarized backscatter coefficient, Normalized Difference Water Index (NDWI), and Modified of Normalized Difference Water Index (MNDWI) maps. The method for making binary water/non-water maps primarily employs the Otsu algorithm on each single map derived from the previous step. Findings reveal that the lake's water extent remains relatively stable over the 6-year period, and is not strongly affected by the seasonal cycle. Although the spatial distribution patterns of the lake exhibit significant similarity, average water extent of the lake derived from 3-m resolution PS imagery is about 2.17 and 5.60% more than that obtained from 10-m resolution Sentinel-2 and Sentinel-1 imagery, respectively. PS observations are effective for monitoring small lakes, but it is advised to check the quality of its NIR band. Sentinel-2 observations prove great effectiveness for lake monitoring, using both NDWI and MNDWI. For Sentinel-1 observations, potential misclassifications could arise due to similarities in VH-polarized backscatter coefficients between water surfaces and other flat surfaces.

Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years.

Continental surface water extents and dynamics are key information to model Earth's hydrological and biochemical cycles. This study presents global and regional comparisons between two multisatellite surface water extent datasets, the Global Inundation Extent from Multi-Satellites (GIEMS) and the Surface Water Microwave Product Series (SWAMPS), for the 1993–2007 period, along with two widely used static inundation datasets, the Global Lakes and Wetlands Database (GLWD) and the Matthews and Fung wetland estimates. Maximum surface water extents derived from these datasets are largely different: ∼13 × 10⁶ km² from GLWD, ∼5.3 × 10⁶ km² from Matthews and Fung, ∼6.2 × 10⁶ km² from GIEMS, and ∼10.3 × 10⁶ km² from SWAMPS. SWAMPS global maximum surface extent reduces by nearly 51% (to ∼5 × 10⁶ km²) when applying a coastal filter, showing a strong contamination in this retrieval over the coastal regions. Anomalous surface waters are also detected with SWAMPS over desert areas. The seasonal amplitude of the GIEMS surface waters is much larger than the SWAMPS estimates, and GIEMS dynamics is more consistent with other hydrological variables such as the river discharge. Over the Amazon basin, GIEMS and SWAMPS show a very high time series correlation (95%), but with SWAMPS maximum extent half the size of that from GIEMS and from previous synthetic aperture radar estimates. Over the Niger basin, SWAMPS seasonal cycle is out of phase with both GIEMS and MODIS-derived water extent estimates, as well as with river discharge data.

Central Sahel is affected by a reinforcement of rainfall since the beginning of 1990s. This increase in rainfall is affected by high inter-annual variability and is characterized by extreme rain events causing floods of unprecedented magnitude. However, few studies have been carried out on these extreme events. Moreover, with current climate change expected to strengthen the hydrological cycle, we don’t know if these events could become more frequent. Here, we report the hydrological changes that currently occur in the Lake Chad basin. Based on ground observations and satellite data, we focused on the 2022 flood event, demonstrating that it was the most important event from the last 60 years, comparable to what occurred during the last wet period between the 1950s and the 1960s. We showed that under this precipitation regime and if warming is not regulated at a global scale, the return period of the 2022 major riverine flood is expected to be between 2 and 5 years. By using modelling experiments, our study also suggested that in the next decade, future flow rates of the main rivers draining the Lake Chad basin could reach the values observed in the 1950s. These results strongly suggest anticipating water management in a context of poor infrastructural development.

This study estimates monthly variation of surface water volume of Thac Mo hydroelectric reservoir (located in South Vietnam), during the 2016–2021 period. Variation of surface water volume is estimated based on variation of surface water extent, derived from Sentinel-1 observations, and variation of surface water level, derived from Jason-3 altimetry data. Except for drought years in 2019 and 2020, surface water extent of Thac Mo reservoir varies in the range 50–100 km2, while its water level varies in the range 202–217 m. Correlation between these two components is high (R = 0.948), as well as correlation between surface water maps derived from Sentinel-1 and free-cloud Sentinel-2 observations (R = 0.98), and correlation between surface water level derived from Jason-3 altimetry data and from in situ measurement (R = 0.99; RMSE = 0.86 m). We showed that water volume of Thac Mo reservoir varies between −0.3 and 0.4 km3 month−1, and it is in a very good agreement with in situ measurement (R = 0.95; RMSE = 0.0682 km3 month−1). This study highlights the advantages in using different types of satellite observations and data for monitoring variation of lakes’ water storage, which is very important for regional hydrological models. Similar research can be applied to monitor lakes in remote areas where in situ measurements are not available, or cannot be accessed freely.