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\"A stunning geographical exploration of our world through 50 unique maps. Modern satellite and geographical technology has enabled the world to be researched in new and incredible detail. From measuring species diversity to monitoring land shifts, our physical and sociological world is mapped like never before. Includes 50 specially commissioned maps that examine our world in a beautifully visual and fascinating way. Alastair Bonnett accompanies each map with a vivid essay that provides detailed insight into how the planet has changed and what it may look like in the future. From examining new deserts and charting airspace, to revealing emerging lands and measuring each continent's natural treasures, each map showcases an important part of our world's history, sociology and of course, geography\"-- Provided by publisher.

In this paper, the latest progress, major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed. First, the latest three FengYun (FY) meteorological satellites (FY-2H, FY-3D, and FY-4A) and their primary objectives are introduced. Second, the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated, including the latest geostationary (FY-2/4) and polar-orbit (FY-3) satellites. Third, the radiometric calibration techniques and accuracies of reflective solar bands, thermal infrared bands, and passive microwave bands for FY meteorological satellites are discussed. It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods, including the vicarious China radiance calibration site calibration, pseudo invariant calibration site calibration, deep convective clouds calibration, and lunar calibration. Fourth, recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length. The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models. Lastly, the latest progress in radiative transfer, absorption and scattering calculations for satellite remote sensing is summarized, and some important research using a new radiative transfer model are illustrated.

A large debris flow triggered by a rock avalanche in the Raunthi glaciated valley resulted in flash floods in the Rishiganga and Dhauliganga rivers on 07 February 2021 in Uttarakhand, India. Hydel projects, houses, roads and bridges in the path of debris flow were damaged resulting in many deaths. We have used high-resolution satellite data (e.g. Pleiades, WorldView, Kompsat, Cartosat, Resourcesat, Planet) to study the source of flash floods and cause of the slope failure. Our detailed geological assessment, carried out using stereoscopic Pleiades images (50 cm), revealed rock avalanche as the main source of slope failure. The slope failure has exposed a ~197-m-high head scarp near the crown and is controlled by two sets of joints and a foliation that helped in the wedge type failure. The volume of failed mass (rock and ice) estimated by cut and fill method using digital elevation models (DEMs) is ~ 29.3 million m3. The rock and ice descended from a height of ~5474 m and then crashed onto the moraine and ice bridges present in the valley at ~3732 m after travelling ~2.9 km along a steep slope. The heat generated by friction during run out and conversion of potential energy to kinetic energy due to the crashing on valley floor melted snow and ice. The ice melt water along with enhanced snow melting due to high ambient temperature on that day got intermixed with debris and created a slush, which was mobilised as a channelised flash flood. Multi-temporal high-resolution satellite data analysis showed that the debris flow was initiated at ~10:08:45 h (IST), and it travelled with a velocity of ~10.6 m/s. The rock avalanche event lasted for ~50 min, and the crash impact created a severe air blast in the valley. The rock avalanche has also resulted in debris blocking the Raunthi gad valley. Estimated Morphological Obstruction Index (MOI) and Hydro-morphological Dam Stability Index (HDSI) indicate the debris dam to be in an unstable domain.

Unvegetated sediment bars are central to river morphodynamics but are rarely used as indicators of channel dynamicity in satellite‐based studies. Linking sediment dynamics and river lateral mobility requires monitoring sustained changes in both water and sediment—the active channel (AC)—to avoid stage‐dependent noise. Yet, such monitoring remains rare. We introduce an automated, globally applicable approach that detects and quantifies activation (erosion) and deactivation (vegetation colonization) by tracking multi‐year sustained AC directional shifts from Sentinel‐2 imagery. Applied to the Po River (Italy), this approach captures trajectories across different morphologies, distinguishing lateral mobility, widening, and narrowing from changes caused by stage‐dependent hydrological forcing. Results identify the exposed sediment‐to‐water ratio as a strong predictor of AC dynamicity, with sediment‐rich reaches showing greater instability and responsiveness to hydrological variations. Our findings demonstrate that incorporating sediment areas alongside the water channel improves understanding of river dynamics, with implications for river restoration and risk mitigation.

This study carefully assesses the capability of supervised machine learning classification algorithms in identifying land cover (LC) in the context of the Jhelum River basin in Kashmir. Sentinel 2 and Landsat 8 high-resolution data from two satellite sources were used. Through preprocessing techniques, we removed any potential noise inherent to satellite imagery and assured data consistency. The study then utilized and compared the skills of the supervised algorithms random forest (RF) and support vector machine (SVM). A hybrid approach, amalgamating classifications from both methods, was also tested for potential synergistic enhancements in accuracy. Using a stratified random sampling approach for validation, the SVM algorithm emerged with a commendable accuracy rate of 82.5%. Using simulations from 2000 to 2015, the soil and water assessment tool (SWAT) model was used to further explore the hydrological effects of LC alterations. Between 2009 and 2019, there were discernible changes in the land cover, with a greater emphasis on ranges, forests, and agricultural plains. When these changes were combined with the results of the hydrologic simulation, a resultant fall in average annual runoff—from above 700 mm to below 600 mm—was seen. With runoff values possibly ranging between 547 mm and 747 mm, the statistics emphasize the direct effects of urban communities encroaching upon forest, agricultural, and barren lands. This study concludes by highlighting the crucial role that technical pipelines play in enhancing LC classifications and by providing suggestions for future water resource estimation and hydrological impact evaluations.

We describe changes in the cropland distribution for physiographic and bioregions of continental Ecuador between 2000 and 2016 using Landsat satellite data and government statistics. The cloudy conditions in Ecuador are a major constraint to satellite data analysis. We developed a two-stage cloud filtering algorithm to create cloud-free multi-temporal Landsat composites that were used in a Random Forest model to identify cropland. The overall accuracy of the model was 78% for the Coast region, 86% for the Andes, and 98% for the Amazon region. Cropland density was highest in the coastal lowlands and in the Andes between 2500 and 4400 m. During this period, cropland expansion was most pronounced in the Páramo, Chocó Tropical Rainforests, and Western Montane bioregions. There was no cropland expansion detected in the Eastern Foothill forests bioregion. The satellite data analysis further showed a small contraction of cropland (4%) in the Coast physiographic region, and cropland expansion in the Andes region (15%), especially above 3500m, and in the Amazon region (57%) between 2000 and 2016. The government data showed a similar contraction for the Coast (7%) but, in contrast with the satellite data, they showed a large agricultural contraction in the Andes (39%) and Amazon (50%). While the satellite data may be better at estimating relative change (trends), the government data may provide more accurate absolute numbers in some regions, especially the Amazon because separating pasture and tree crops from forest with satellite data is challenging. These discrepancies illustrate the need for careful evaluation and comparison of data from different sources when analyzing land use change.

Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.

Assimilation of the Advanced Geostationary Radiance Imager (AGRI) clear-sky radiance in a regional model is performed. The forecasting effectiveness of the assimilation of two water vapor (WV) channels with conventional observations for the “21·7” Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study. The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition of the AGRI exceeds 500 mm, compared to a maximum value of 532.6 mm measured by the national meteorological stations, and that the location of the maximum precipitation is consistent with the observations. The results for the short periods of intense precipitation processes are that the simulation of the location and intensity of the 3-h cumulative precipitation is also relatively accurate. The analysis increment shows that the main difference between the two sets of assimilation experiments is over the ocean due to the additional ocean observations provided by FY-4A, which compensates for the lack of ocean observations. The assimilation of satellite data adjusts the vertical and horizontal wind fields over the ocean by adjusting the atmospheric temperature and humidity, which ultimately results in a narrower and stronger WV transport path to the center of heavy precipitation in Zhengzhou in the lower troposphere. Conversely, the WV convergence and upward motion in the control experiment are more dispersed; therefore, the precipitation centers are also correspondingly more dispersed.

The unprecedented hydraulic visibility of rivers surfaces deformation with SWOT satellite offers tremendous information for improving hydrological‐hydraulic models and discharge estimations for rivers worldwide. However, estimating the uncertain or unknown parameters of hydraulic models, such as inflow discharges, bathymetry, and friction parameters, poses a high‐dimensional inverse problem, which is ill‐posed if based solely on altimetry observations. To address this, we couple the hydraulic model with a semi‐distributed hydrological model, to constrain the ill‐posed inverse problem with sufficiently accurate initial estimates of inflows at the network upstreams. A robust variational data assimilation of water surface elevation (WSE) data into a 1D Saint‐Venant river network model, enables the inference of inflow hydrographs, effective bathymetry, and spatially distributed friction at network scale. The method is demonstrated on the large, complex, and poorly gauged Maroni basin in French Guiana. The pre‐processing chain enables (a) building an effective hydraulic model geometry from drifting ICESat‐2 WSE altimetry and Sentinel‐1 width; (b) filtering noisy SWOT Level 2 WSE data before assimilation. A systematic improvement is achieved in fitting the assimilated WSE (85% cost improvement), and in validating discharge at 5 gauges within the network. For assimilation of SWOT data alone, 70% of data‐model fit is in [−0.25;0.25m] $[-0.25;\\,0.25\\,\\mathrm{m}]$ and the discharge NRMSE ranges between 0.05 and 0.18 (18%–71% improvement from prior). The high density of SWOT WSE enables the inferrence of detailed spatial variability in channel bottom elevation and friction, and inflows timeseries. The approach is transferable to other rivers networks worldwide.

In this study, a method for assimilating FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances (CER) is investigated, and the impact of this method on water vapor analysis and rainstorm forecasting is examined through observing system simulation experiments and actual case experiments. The high proportion of inverted humidity profiles in the cirrus‐effected pixels is the main reason for the negative effect of assimilation in the mid‐to‐lower troposphere. To address this, relevant constraint conditions are incorporated into the cost function. The statistical results reveal that the addition of a CER assimilation improves the analysis increment of water vapor, with pattern correlation coefficients of 0.33, 0.35, and 0.20 at 200, 300, and 400 hPa, respectively, which are greater than those of a clear‐sky radiance assimilation (0.28, 0.33, and 0.17, respectively). Moreover, the inclusion of a CER assimilation greatly improves data utilization, and has a neutral to positive effect on precipitation forecasting. Plain Language Summary Infrared all‐sky radiance assimilation is an attractive but challenging problem in satellite data assimilation. Is there an alternative approach to achieve the assimilation of infrared radiance for a certain type of cloud? It is found that cirrus clouds, which are loose ice clouds composed of ice crystals, can partially transmit infrared radiance and are large in number. Considering these advantages, a new direct assimilation method for FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances is proposed in this paper. The method addresses the uncertainty in the assimilation of cirrus‐effected radiances by adding weak constraints of inverse humidity. This research highlights the substantial increase in AGRI data usage when incorporating cirrus‐effected radiance data, as well as the neutral‐to‐positive impact on water vapor analysis and precipitation forecasting. This study also suggests that future endeavors could combine infrared channels with lower‐level microwave channels, which may have a more significant contribution to infrared radiance assimilation. Key Points A high percentage of inverse humidity exists in the atmospheric profile corresponding to cirrus cloud fields of view over land The assimilation of cirrus cloud pixels greatly improves the utilization rate Assimilating advanced geostationary radiance imager cirrus‐effected radiances has a neutral to positive effect on water vapor analysis and precipitation forecasting