Monitoring spatial and temporal dynamics of soil moisture is essential for managing hydrological extremes such as droughts and inland excess water. This study assesses the accuracy of NASA's Soil Moisture Active Passive (SMAP) Level 3 soil moisture product using long-term in situ measurements (2016-2024) from the national drought monitoring network in Hungary. Two distinct 36 km satellite footprint cells were extensively analyzed, representing different land use and soil type conditions. Daily averaged in situ soil moisture data measured in the upper 10 cm layer were compared to SMAP observations for both AM and PM overpasses. Statistical metrics used to evaluate performance include the Pearson correlation coefficient (R), bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE). Results indicate that PM values generally exhibit stronger agreement with in situ measurements. The SMAP product met the <0.04 m(3)/m(3) ubRMSE validation requirement threshold over several years and locations, particularly in areas with homogeneous soil conditions. However, variability in soil types and topography in more heterogeneous regions highlights the need for additional ground-based measurements and higher-resolution satellite data to enhance local-scale validation.

Climate change is impacting our everyday life and is altering the magnitude and frequency of weather-related extreme events such as precipitation, floods, and drought. For this reason, there is a great demand to study these phenomena in a changing environment on a large global spatial extent.In the past, we developed a protocol for river gauge measurements using satellite passive microwave radiometer (PMR) data and applied over multiple river watersheds [2,1]. Exploiting the high sensitivity of microwave emission to water presence we use low frequency L-band (1–2 GHz) passive microwave radiometry (PMR) to monitor rivers and reservoirs and to compare over different microwave frequencies and polarization configurations. We successfully applied the methodology to ESA Soil Moisture and Sea Salinity (SMOS) sensor data reaching high correlation to in-situ discharge measurements over various river basins in different climate. In this paper, we compare the capabilities of PMR and optical EO to observe river flow over the Amazon and Niger-Volta watershed. We tested the Moderate Resolution Imaging Spectroradiometer (MODIS), a low resolution (250 m – 5 km) NASA satellite data to derive hydrological time series. To understand the performance of optical orbital river gauge from MODIS comparable to the PMR gauge measurements we analyzed 8-day composite of Terra MOD09A1 in Google Earth Engine environment. The product is surface spectral reflectance of Bands 1 through 7 corrected for atmospheric conditions and averaged over an 8-day. Results over tropical regions showed a significant obstacle of cloud cover for optical data (r2: 0.52, std: 0.33), where PMR has the potential to measure river streamflow (r2: 0.82, std: 0.12). Yet over regions with less clouds both optical and PMR can be good alternative to in-situ streamflow ground measurements.

The era of Artificial Intelligence ‘AI’ with all the benefits brought along, has raised new and additional challenges to the ongoing efforts of assessing, defining, formulating, and implementing the quality aspects of geospatial remote sensing data. Developed practices using artificial intelligence leveraged techniques such as image interpretation, classification, thematic mapping, and even image quality enhancement, necessitating by that the reassessment and redevelopment of some of the related emerging quality aspects. Moreover, technology also made the generation of false images and false data possible, this matter constrained and increased precaution and doubtfulness, altogether making some practices based on that data almost halt to further notice. This paper presents the collaborative research work to assess and clarify the quality aspects that arose with the advent and implementation of AI and associated technologies; the concerns and issues that can accompany the generation of false satellite and aerial images including the generated geospatial data out of which, how the new emerged quality aspects fit into the currently existing methods through the lifecycle of remote sensing data production and usage, and consequently how the quality dimensions are affected and should be further developed and improved to tackle the changes and innovations. Also, lame a bit on investigating how to accommodate the new challenges in standards, and practical procedures and raise the awareness to users, the level of dependency on improved and enhanced satellite images when it comes to data collection interpretation and classification, and finally define the research gaps, future expected challenges and thus enclose suggestions and recommendations in that respect.

The Mekong Basin’s rapidly growing population and changing water infrastructure (e.g., dams and canals) requires major improvements in observations of river discharge and reservoir storage changes. Floods and droughts can affect food supplies, requiring frequent and long-term observations for evaluation. We use satellite passive microwave radiometry (PMR) to monitor rivers and reservoirs, and compare performance at different frequencies and polarization combinations. PMR from TRMM, AMSR-E, AMSR2, GPM, SMOS, and SMAP sensitively monitors water surface area change at selected satellite gauging reaches (SGRs). These reaches can be measured globally at daily or near-daily intervals from 1998 to present. Rating curves that translate PMR signal to stage and discharge units can be obtained from nearby gauging stations (even if now discontinued) or from hydrologic modeling. We demonstrate the PMR capability to measure river stage/discharge/runoff and lake/reservoir water level as verified with in-situ gauging data for selected locations in the Lower Mekong Basin.

River flow is a fundamental observable in hydrology, but there is no consistent global ground measurement network. Various types of orbital remote sensing are, therefore, well positioned to meet an important observational need, including for hydrological modeling and for understanding trends through time. In previous studies, we showed that passive microwave radiometry (PMR) can measure streamflow over selected locations around the globe with a high correlation to colocated in situ discharge observations. This article demonstrates the potential of low-frequency, L-band NASA Soil Moisture Active and Passive (SMAP) satellite observations for streamflow measurement: an unanticipated but exceptionally valuable use of this sensor. By using the fully polarimetric capability of SMAP with full Stokes parameters, we optimize the polarization combinations of the observations to retrieve accurate river hydrographs from space. Flow measurements over 150 satellite gauging reaches (SGR) are retrieved over different continents, and 14 SGRs provide comparisons to available in situ river gauging data. Results from linear correlation calculations provide coefficients of determination $r^{2}$ of approximately 0.75 for SMAP-based discharge measurements when compared to in situ streamflow observations. SMAP river observations thereby improve river gauging results compared to ESA’s Soil Moisture Ocean Salinity (SMOS) satellite L-band PMR as the analysis indicates typically lower $r^{2}$ values of approximately 0.68 for SMOS.

During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given.

Climate change has a crucial impact on the global energy and water cycle. The hydrological cycle can be studied both from ground and satellite measurements on a global scale. Yet a comprehensive overview is challenging to establish given the spatial and temporal limitations related to various Earth Observation satellite sensors or maintenance of in-situ gauges. Optical remote sensing of visible light can not overcome the substantial obstacle from cloud cover that vastly limits its capability in daily global monitoring. Active satellite sensors like SAR or altimetry are not capable to provide global coverage on a daily basis, therefore, they can be geographically limited. Passive microwave radiometry (PMR) can acquire both daily and global scales that enables the temporally frequent and spatially extensive observations of continental river gauge. Previous studies demonstrated the use of PMR measurements for global daily river gauge benefiting from its high sensitivity of microwave radiation to water presence. This study aims at comparing the methodology of PMR to optical river gauge measurements based on the assumption that at selected locations along the river channel, increase in streamflow is related to increase in the floodplain water surface inundation. Comparison showed a significant obstacle of cloud cover over tropical regions, where PMR has the potential to measure river streamflow. Yet over regions with less clouds both optical and PMR can be good alternative to in-situ streamflow ground measurements.

&lt;p&gt;River stage (surface water level H), discharge (volumetric water flow rate Q), and seasonal ice cover (freeze-up timing F, and break-up timing B) are crucial observables for hydrology and water cycle science.&amp;#160; In-situ river gauging measurements of H, Q, F, and B are laborious and costly to install and maintain at a limited number of locations.&amp;#160; It will be a breakthrough to use satellite data for global river measurements on a nearly-daily basis with multi-decadal data records.&amp;#160; Passive microwave radiometer (PMR) data have been collected from space globally since the 1980s.&amp;#160; Nevertheless, the typical satellite PMR resolution is coarse (10s km), which is much larger than general river widths.&amp;#160; The key question is how PMR can possibly measure the river parameters.&amp;#160;&lt;/p&gt; &lt;p&gt;The answer is physically founded on the first principle of Maxwell equations to derive vector wave equations for all polarization combinations in heterogeneous multi-layered geophysical media.&amp;#160; The wave equations are solved with dyadic Green&amp;#8217;s functions subject to boundary conditions. The renormalization method is applied to determine the effective permittivity in each layer while all multiple wave-boundary interactions are included. To circumvent the limitation of the isothermal condition in the Kirchhoff approach, the fluctuation-dissipation theorem is used to calculate the brightness temperature&lt;sub&gt; &lt;/sub&gt;Tb(h) for the horizontal polarization (the first modified Stoke parameter), Tb(v) for the vertical polarization (the second parameter), the polarization cross-correlation amplitude U (the third parameter), and the phase V (the fourth parameter).&lt;/p&gt; &lt;p&gt;Based on this physical foundation, a protocol to derive the river observables (H, Q, F, and B) is developed due to the high sensitivity of microwave emissivity of water versus ice and soil conveyed in the brightness temperatures. This overcomes and renders the high spatial resolution requirement unnecessary for river remote sensing by wide-swath PMR for global river observations on a daily or near-daily basis. The PMR method relies on the total areal change of river water within the footprint rather than depending on the river width per se.&amp;#160; As such, PMR can measure a narrow river when its meandering makes a sufficient total surface area in the PMR footprint.&amp;#160; The PMR method is also robust against short-term river channel migration and in-stream sand bars that can be changed by river sedimentation and dynamic processes.&lt;/p&gt; &lt;p&gt;To demonstrate the PMR capability for river monitoring, examples of satellite results for river measurements are compared and validated with in-situ river gauging time-series data records for various rivers from the tropics to cold land regions using PMR data at Ka-band such as AMSR-E, AMSR2, TRMM, and GPM and at L-band such as SMOS and SMAP.&amp;#160; The capability to measure global rivers allows PMR satellite missions to address hydrology and water cycle science as a key contribution, including the future Copernicus Imaging Microwave Radiometer (CIMR) to be launched in the 2025+ time frame, further extending the existing long-term data records for river measurements. Moreover, a significant advance of water cycle science is expected with the synergy of PMR together with SWOT successfully launched by NASA in December 2023.&lt;/p&gt;

The global water cycle is accelerating in a changing climate. A key element of the hydrology cycle is surface streamflow, which lacks a global river gauging network with open data shared among international stakeholders. As an alternative, rivers have been monitored from space with multiple orbital sensors that continue providing river measurements worldwide for the past several decades and into the future. With its all-weather and day-and-night capabilities, passive microwave satellite sensors provide a unique data source for near-daily global streamflow monitoring.

Abstract The timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.

The present era of climate change and expanding population requires major improvements in sustained observation of global river discharge. Floods and droughts are affecting food supplies, and suspected long‐term trends require appropriate data for evaluation. Orbital remote sensing can address this observational need. Here we use satellite Ka‐ (36.5 GHz) and L‐band (1–2 GHz) passive microwave radiometry (PMR) to monitor river discharge changes and determine what size rivers can be measured and the frequencies and polarization configurations that yield the most robust results. Selected satellite gauging reaches (SGRs) can be measured at near‐daily intervals from 1998 to present (Ka‐band) and 2010 to present (L‐band). The SGRs are 10–36 km in length; the dynamic proportion of water surface area within each varies with river discharge. Due to contrasting dielectric properties, water and land emit different intensities of microwave radiation; thus emission from a mixed water/land pixel decreases as the proportion of water within the pixel increases. Depending on the river and floodplain morphology, water flow area can be a robust indicator of discharge and the microwave sensors can retrieve daily discharge to ±20%. Instead of spatial resolution, it is the sensor measurement precision, geolocation accuracy, and channel and floodplain morphology that most strongly affect accuracy. Calibration of flow area signals to discharge can be performed using nearby ground stations (even if now discontinued) or by comparison to hydrologic modeling.

River floods and daily runoff have long been measured on the ground at in situ gauging stations. However, today's global hydrologic models require improvements to the quantity and quality of such observations, in order to calibrate flow routing calculations and to monitor areas where any ground information is sparse. Satellite remote sensing of the Earth's water cycle has recently been extended to measurements of daily discharge and runoff, and thereby to flood events. We demonstrate that passive microwave radiometry can monitor river flow changes with considerable accuracy at an appropriate temporal sampling interval for characterizing floods (daily) regardless of cloud cover, over multiple decades and continuing into the future. Ka-band data from the AMSR-2, AMSR-E, TMI and GMI passive microwave radiometers are now being used to provide important river flow status information, with period of records commencing in 1998. Also, the L-band sensors now returning data from the NASA SMAP and ESA SMOS satellites can provide even more sensitive and accurate information over forested floodplains. Moreover, the timing of ice cover establishment and break up can also be tracked along cold region rivers: the annual spring flood can be immediately detected and compared to all previous years. These public observational data can be used to address important science and flood risk issues, such as the effects of climate change on flood frequencies and magnitudes.

The importance of data quality assessment has significantly increased with the boom of information technology and the growing demand for remote sensing (RS) data. The Remote Sensing Data Quality Working Group of the International Society for Photogrammetry and Remote Sensing aimed to conduct an investigation on the principles of data quality. Literature review revealed that most publications introduce data quality models for application specific processing chains and quality schemes are built case by case with particular domain indicators only. Yet no general concept independent from applications has been developed so far. This paper focuses on the formulation of a RS quality concept adopted from information technology domain describing a triangular RS data quality scheme that relates data sources, quality dimensions and lifecycle phases. Following the introduction it provides examples of international standards and fundamentals of theoretic quality modelling. After a short overview on platforms/sensors, definitions of different quality dimensions are presented with their metrics organised in clusters (like resolution or accuracy). The main achievement of the paper relates lifecycle phases to different quality dimensions of high relevance. The objective is not only to address experts of RS but to raise awareness of uncertainty for the general RS user community.

Microclimatic analysis of an urban scenario has always been an interesting but complicated challenge. The available remote sensing equipments ensure multi- or hyperspectral imagery being ready to extract excellent land cover information, but the obtained data have lower spatial resolution limiting the efficiency of such analyses. In order to increase the geometric resolution in microclimatic studies, an exercise was executed with an Unmanned Aerial System. The calibration of the imaging camera on a dedicated test field was followed by the data capture flight over the campus of the Budapest University of Technology and Economics. The evaluation of the acquired images has resulted a point cloud containing millions of points. The high density point cloud was able to be transformed into 3D mesh representation and could be fed into a geographic information system for further analysis steps. Based on the color and height information of all individual points, the obtained geometric base was easily to be converted into land cover model representing man-made and natural objects, like buildings or trees. The segmentation of the model is a suitable input for climatic analyses and simulation software packages, where extreme high geometric resolution is required.

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.

Geographic data and remote sensing have become sophisticated tools for obtaining knowledge on natural hazards of meteorological origin. In many cases the impact of disasters can not be prevented, however, efficient mitigation strategy and rapid response can reduce losses and damages in emergency situations. In addition, climate change is expected to increase the magnitude and frequency of natural hazards like extreme precipitation, floods, hurricanes, droughts. This paper aims at demonstrating the potential of satellite image analysis and Geographic Information Systems (GIS) for assisting disaster management before and during catastrophic events. Furthermore, it describes application of remote sensing to support climate change impact assessment on hydrological cycle in sensitive arctic regions. Divers applications in Hungary and around the world will illustrate the capabilities of the technology. Operational and scientific advantages of the practice will justify the use of geographical data in managing natural hazards with origin in meteorology. Not only for analyzing the hazard with an element at risk method but also for estimating the vulnerability factor accounting for physical and socio-economic resilience of the affected area.