The VIIRS-Based RST-FLARE configuration: The Val d'Agri Oil Center Gas Flaring Investigation in between 2015-2019

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d'Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring

Improving the RST-OIL algorithm for oil spill detection under severe sun glint conditions

In recent years, the risk related to oil spill accidents has significantly increased due to a global growth in offshore extraction and oil maritime transport. To ensure sea safety, the implementation of a monitoring system able to provide real-time coverage of large areas and a timely alarm in case of accidents is of major importance. Satellite remote sensing, thanks to its inherent peculiarities, has become an essential component in such a system. Recently, the general Robust Satellite Technique (RST) approach has been successfully applied to oil spill detection (RST-OIL) using optical band satellite data. In this paper, an advanced configuration of RST-OIL is presented, and we aim to extend its applicability to a larger set of observation conditions, referring, in particular, to those in the presence of severe sun glint effects that generate some detection limits to the RST-OIL standard algorithm. To test such a configuration, the DeepWater Horizon platform accident from April 2010 was selected as a test case. We analyzed a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images that are usually significantly affected by sun glint in the Gulf of Mexico area. The accuracy of the achieved results was evaluated for comparison with a well-established satellite methodology based on microwave data, which confirms the potential of the proposed approach in identifying the oil presence on the scene with good accuracy and reliability, even in these severe conditions

A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods

Satellite data have been widely utilized for flood detection and mapping tasks, and in recent years, there has been a growing interest in using Synthetic Aperture Radar (SAR) data due to the increased availability of recent missions with enhanced temporal resolution. This capability, when combined with the inherent advantages of SAR technology over optical sensors, such as spatial resolution and independence from weather conditions, allows for timely and accurate information on flood event dynamics. In this study, we present an innovative automated approach, SAR-RST-FLOOD, for mapping flooded areas using SAR data. Based on a multi-temporal analysis of Sentinel 1 data, such an approach would allow for robust and automatic identification of flooded areas. To assess its reliability and accuracy, we analyzed five case studies in areas where floods caused significant damage. Performance metrics, such as overall (OA), user (UA), and producer (PA) accuracy, as well as the Kappa index (K), were used to evaluate the methodology by considering several reference flood maps. The results demonstrate a user accuracy exceeding 0.78 for each test map when compared to the observed flood data. Additionally, the overall accuracy values surpassed 0.96, and the kappa index values exceeded 0.78 when compared to the mapping processes from observed data or other reference datasets from the Copernicus Emergency Management System. Considering these results and the fact that the proposed approach has been implemented within the Google Earth Engine framework, its potential for global-scale applications is evident

A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution

Integration of optical and passive microwave satellite data for flooded area detection and monitoring

Flooding represents a serious threat to millions of people around the world and its hazard is rising as a result of climate changes. From this perspective, flood risk management is a key focus of many governments, whose priority is to have frequently updated and accurate information about the flood state and evolution to promptly react to the disaster and to put in place effective countermeasures devoted to limit damages and human lives losses. Remote sensing technology allows for flood monitoring at different spatial and temporal resolutions with an adequate level of accuracy. In particular, for emergency response purposes, an integrated use of satellite data, acquired by both optical and passive or active microwave instruments, has to be preferred to have more complete and frequently updated information on soil conditions and to better support decision makers. In this framework, multi-year time series of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data were processed and analyzed. In detail, the Robust Satellite Techniques (RST), a multi-sensor approach for satellite data analysis, has been implemented for studying the August 2002 Elbe river flood occurred in Germany, trying to assess the potential of such an integrated system for the determination of soil status and conditions (i.e. moisture variation, water presence) as well as for a timely detection and a near real time monitoring of critical soil conditions

How Can Seasonality Influence the Performance of Recent Microwave Satellite Soil Moisture Products?

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network

Toward the estimation of river discharge variations using MODIS data in ungauged basins

This study investigates the capability of the Moderate resolution Imaging Spectroradiometer (MODIS) to estimate river discharge, even for ungauged sites. Because of its frequent revisits (as little as every 3 h) and adequate spatial resolution (250 m), MODIS bands 1 and 2 have significant potential for mapping the extent of flooded areas and estimating river discharge even for medium-sized basins. Specifically, the different behaviour of water and land in the Near Infrared (NIR) portion of the electromagnetic spectrum is exploited by computing the ratio (C/M) of the MODIS channel 2 reflectance values between two pixels located within (M) and outside (C), but close to, the river. The values of C/M increase with the presence of water and, hence, with discharge. Moreover, in order to reduce the noise effects due to atmospheric contribution, an exponential smoothing filter is applied, thus obtaining C/M⁎. Time series of hourly mean flow velocity and discharge between 2005 and 2011 measured at four gauging stations located along the Po river (Northern Italy) are employed for testing the capability of C/M⁎ to estimate discharge/flow velocity. Specifically, the meanders and urban areas are considered the best locations for the position of the pixels M and C, respectively. Considering the optimal pixels, the agreement between C/M⁎ and discharge/flow velocity is fairly good with values in the range of 0.65–0.77. Additionally, the application to ungauged sites is tested by deriving a unique regional relationship between C/M⁎ and flow velocity valid for the whole Po river and providing only a slight deterioration of the performance. Finally, the sensitivity of the results to the selection of the C and M pixels is investigated by randomly changing their location. Also in this case, the agreement with in situ observations of velocity is fairly satisfactory (r ~ 0.6). The obtained results demonstrate the capability of MODIS to monitor discharge (and flow velocity). Therefore, its application for a larger number of sites worldwide will be the object of future studies