Integration Of Satellite Data Research Articles

Comparison of three machine learning algorithms for retrieving soil moisture information from Sentinel-1A SAR data in northwest Shandong plain, China

Soil moisture (SM) plays a critical role in the growth and management of grain in semi-humid regions. However, little is known about how to integrate satellite data with machine learning to accurately retrieve SM information in these areas. This study compares the capability of three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), to extract SM information over the Northwest Shandong Plain using multi-phase dual-polarized Sentinel-1A satellite data. The backscattering coefficients were obtained through standard intensity and phase processing to calculate the SAR indices, and several characteristic parameters were extracted as impact factors using the Cloude-Pottier decomposition. The importance of these factors was analyzed, while the performance of each machine learning algorithm was comprehensively evaluated using the K-fold cross-validation method. The best-performing model was utilized to retrieve the spatio-temporal changes in SM in the study area. The findings indicate the following: (1) The first eigenvalue has the greatest impact on retrieval accuracy, followed by entropy, where the intensity component of Shannon's entropy is more important than its polarization component; (2) The addition of more impact factors does not bring a continuous improvement in model performance, but the optimal factor combinations differ for different machine learning retrieval models; (3) The RF model trained using the IM12 combination demonstrates better performance than SVM and ANN in retrieving SM information, with a coefficient of determination (R2) of 0.55 and a root mean square error of 6.12 vol% on the validation set. The level of SM in the Yellow River National Wetland Park is higher than that of the surrounding areas, with substantial seasonal changes. Precipitation, temperature, and vegetation significantly influence the regional variations in SM at the macroscopic level.

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

AbstractIrrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel‐1 backscatter observations into the Noah‐MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model‐only 14‐day irrigation estimates by about 20% in terms of root‐mean‐squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real‐world cases.

Estimation of hourly one square kilometer fine particulate matter concentration over Thailand using aerosol optical depth

In the recent years, concentration of fine particulate matter that are 2.5 microns or less in diameter (PM2.5) in Thailand has consistently exceeded the national ambient air quality standard. Currently, the measurement of PM2.5 concentration relies on air quality monitoring stations operated by the Pollution Control Department of Thailand (PCD). However, these stations are insufficient, particularly in rural areas, where agricultural open burning are major sources of pollution after harvesting period. This study aims to enhance the monitoring of PM2.5 concentration by leveraging cost-effective technologies. We propose the integration of satellite data, specifically Aerosol Optical Depth (AOD) from Multi-Angle Atmospheric Correction (MAIAC) product and Himawari-8 satellites, with the Weather Research and Forecasting Model (WRF) data, to provide supplementary data to the ground-based monitoring. Hourly 5 × 5 km2 AOD data from Himawari-8 were downscaled to a high-resolution of 1 × 1 km2, leveraging the AOD distribution pattern of the concurrent MAIAC product using eXtreme Gradient Boosting (XGBoost) model. Notably, during Thailand’s rainy season (May to August), the study observed a relative reduction in the training model’s R-square value. This phenomenon is attributed to temporal discrepancies between Himawari-8 and the MAIAC products during this period. The predictive models of PM2.5 concentrations with the identification of pertinent variables through Pearson’s correlation analysis and recursive feature elimination, driven by the robust XGBoost model. Subsequently, the downscaled AOD, wind speed, temperature, and pressure were identified as predictors for the estimation of hourly PM2.5 concentration. This comprehensive approach enabled the projection of PM2.5 levels across Thailand, encompassing over 600,000 grids at 1 × 1 km2 resolution. The developed models, thus, offer a valuable tool for robust and high-resolution PM2.5 concentration estimation, presenting significant implications for air quality monitoring and management in Thailand.

Tracking tsunami propagation and Island’s collapse after the Hunga Tonga Hunga Ha’apai 2022 volcanic eruption from multi-space observations

The quantity and accuracy of satellite-geodetic measurements have increased over time, revolutionizing the monitoring of tectonic processes. Global Navigation Satellite System (GNSS) and satellite radar signals provide observations beyond ground deformation, including how earthquake and tsunami processes affect variations in the ionosphere. Here, we study the Hunga Tonga Hunga Ha’apai (HTHH) volcanic eruption 2022 and its associated tsunami propagation with the analysis GNSS derived Total Electron Content (TEC), Synthetic Aperture Radar (SAR) Sentinel-1 data, complemented with tide gauge observations. We utilize GNSS sites data within a ~ 5000 km radius from the volcanic eruption for estimating the ionospheric perturbation as Vertical TEC. We give evidence on the detection of acoustic gravity, internal gravity, and atmospheric Lamb waves signatures in the TEC perturbation. In particular, the internal gravity waves that concentrated in the southwest of Tonga, directly correlates with the observed tsunami propagation direction as accounted by the tide gauge measurements. However, the acoustic gravity wave signature in the TEC is dominant in the north direction suggesting a surface deformation, which could be verified using Sentinel-1A SAR amplitude data. The analysis presented herein shows that within 5 h of the volcanic eruption, the central part of the HTHH island landscape disappeared with the biggest explosion. The unprecedented detail resolved by integrating satellite data yields previously unknown details of the deformation of the 2022 HTHH volcano eruption.

Fire Affects Tree Growth, Water Use Efficiency and Carbon Sequestration Ecosystem Service of Pinus nigra Arnold: A Combined Satellite and Ground-Based Study in Central Italy

The Mediterranean basin is an area particularly exposed to fire risk due to its climate and fire-prone vegetation. In recent decades, the frequency and intensity of wildfires increased, leading to negative effects on forests, such as a decrease in tree growth or an increase in tree mortality, producing a relevant loss of carbon sequestration ecosystem service. This study of the impacts of fires on forests is fundamental for planning adequate forest management strategies aimed at recovering and restoring the affected areas. In this framework, our research delves into the effects of a forest fire that, in 2017, affected a forest of black pine (Pinus nigra Arnold) in Central Italy. Combining satellite and terrestrial analyses, this study evaluated the impact of the fire on tree growth, water use efficiency and carbon sequestration capacity. Our findings highlight the importance of using remote sensing for the accurate identification of fire-affected areas and precise planning of ground-based activities. However, the integration of satellite data with forest surveys and sampling has proven crucial for a detailed understanding of fire’s effects on trees. Dendrochronology and stable isotopes have revealed the post-fire growth decline and altered water usage of defoliated trees. Furthermore, the quantification of CO2 sequestration highlighted a significant reduction in carbon uptake by damaged trees, with severe implications for this ecosystem service.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Enhanced Soil Moisture Retrieval through Integrating Satellite Data with Pedotransfer Functions in a Complex Landscape of Ethiopia

Remotely sensed soil moisture products potentially provide a valuable resource for monitoring agricultural drought and assessing food security. The agriculture dominated countries of Eastern Africa experience high inter-annual variability of rainfall, but the monitoring and assessment of the predominantly rainfed agriculture systems is hindered by an absence of ground-based observations. This study evaluates the accuracy of three soil moisture products: ASCAT SWI 12.5 km, SMAP soil moisture data 9 km (SPL3SMP_E), and enhanced surface soil moisture map derived through integrating ASCAT SWI and Pedotransfer Functions (PTFs) (ASCAT_PTF_SM), in Ethiopia, through comparison with in situ-observed soil moisture datasets. Additionally, a new water retention PTF, developed for Ethiopian soils, is integrated with a high-resolution soil property dataset to enhance the spatial resolution of the soil moisture product. The results show that the new integrated dataset performs better in terms of unbiased root mean square error (ubRMSE = 0.0398 m3/m3) and bias (0.0222 m3/m3) in comparison with ASCAT SWI 12.5 km (ubRMSE = 0.0.0771 m3/m3, bias = 0.1065 m3/m3). SMAP is found to have limitations during the wet season, overestimating soil moisture. The finer spatial resolution of the data allows for a better depiction of heterogeneity of soil moisture across the landscape and can be used to identify water-related issues and improve hydrological models for agricultural water management.

A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles

ABSTRACT The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ET c and ET a on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (K s) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET 0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ET a showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.

Estimating irrigation water requirement in rice by integration of satellite data and agrometeorological indices in Palakkad, Kerala

The sustainability of irrigated agriculture is jeopardized by catastrophic climate change, with projected forecasts indicating that by 2025, one out of every four people on the planet will be experiencing extreme water scarcity. In this context, an attempt was made for scheduling irrigation at a regional scale combining satellite data and agrometeorological indices over major rice growing tracts of Palakkad district in Kerala. Normalized Difference Vegetation Index (NDVI) product of MODIS (MOD13Q1) with a temporal resolution of 16 days and a spatial resolution of 250 m was utilized to establish a relationship with crop coefficient (Kc) of rice during the mundakan rice season of 2020-21 and 2021-22 in 30 ground truth locations. The results revealed that NDVI values have strong relationship with Kc values with an R2 value of 0.81. Crop coefficient (Kc) maps developed using satellite derived NDVI provided Kc values at a regional scale during different stages of crop growth and it helped to estimate crop evapotranspiration with greater accuracy. Based on this crop water demands maps depicting the spatial and temporal distribution of irrigation requirement were generated for the whole study area. These maps can be used as a tool for the estimation of the crop water requirement of a rice field if the geographical coordinates of the location are known. The total crop water requirements estimated during mundakan season 2020-21 and 2021-22 in Palakkad district were in the range of 700-975 mm and 560-897 mm respectively. Integration of remote sensing & agrometeorological techniques has scope for regional-scale crop water requirement estimation in a cost-effective and time-bound manner.

German farmers' perceived usefulness of satellite-based index insurance: insights from a transtheoretical model

PurposeIndex insurance is promising to mitigate drought-related income losses in agriculture. To reduce the basis risk of index insurance, the integration of satellite data is of growing interest in research. The objective of this study is to obtain preliminary evidence regarding farmers' perceived usefulness (PU) of satellite-based index insurance.Design/methodology/approachBy modifying the transtheoretical model of change to a transtheoretical model of PU, German farmers' gradual PU of satellite-based index insurance was investigated.FindingsThe results show that the average farmer perceives satellite-based index insurance as useful. It can be particularly seen that a higher level of education in an agricultural context as well as higher trust in index insurance products increases farmers' gradual PU. Moreover, higher relative weather-related income losses increase farmers' gradual PU.Research limitations/implicationsIt is recommended to apply latent variables when conducting future investigations regarding farmers' PU.Originality/valueTo the best of the authors' knowledge, this is the first study to explore farmers' PU of upcoming satellite-based index insurance by modifying and applying the transtheoretical model in a new way.

Chlorophyll dynamics from Sentinel-3 using an optimized algorithm for enhanced ecological monitoring in complex urban estuarine waters

Urban estuaries are dynamic environments that hold high ecological and economic value. Yet, their optical complexity hinders accurate satellite retrievals of important biogeochemical variables, such as chlorophyll-a (Chl-a) biomass. Approaches based on a limited number of satellite spectral bands often fail to capture seasonal transitions and sharp spatial gradients in estuarine Chl-a concentrations, inhibiting integration of satellite data into water quality monitoring and conservation programs. We propose a novel approach that utilizes the wide range of spectral information captured by the Ocean and Land Color Instrument (OLCI) to retrieve estuarine Chl-a. To validate our approach, we used measurements in Long Island Sound (LIS), a highly urbanized estuary increasingly susceptible to anthropogenic stressors and climate change. Hyperspectral remote sensing reflectance (Rrs) and Chl-a data representing the spatiotemporal diversity of LIS were used to assess the ideal atmospheric correction approach for OLCI and develop a multi-spectral multiple linear regression (MS-MLR) Chl-a algorithm. POLYMER derived Rrs proved to be the preferred atmospheric correction approach. Evaluation of MS-MLR performance in retrieving Chl-a with in situ Rrs showed good agreement with field measurements. Application to OLCI-retrieved Rrs showed significant improvement (20%-30%) in common error metrics relative to other algorithms assessed. The MS-MLR approach successfully captured seasonal cycles and spatial gradients in Chl-a concentration. Application of this method to urban estuaries and coasts enables accurate, high resolution Chl-a observations at the ecosystem scale and across a range of conditions, as needed for conservation and ecosystem management efforts.

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane‐dominated African savannah dry forest

AbstractThis study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L‐band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north‐western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite‐derived metrics, a two‐pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands.

Drylands contribute disproportionately to observed global productivity increases

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982–2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle.

Structural Health Monitoring of transport infrastructures: preliminary experimental calibration of the numerical model of “Ponte della Musica – Armando Trovajoli”

Considering the high vulnerability of existing structures and infrastructures, mainly due to deterioration of building materials, to the exposure to cyclic and seismic loads and environmental factors, one of the main challenges of recent years in the field of structural health monitoring is the development of systems capable of integrating satellite data with on-site sensor measurements in order to obtain more reliable information on structural safety conditions for the structures at different scales: from the local to the territorial one. However, this enormous potential is counteracted by some limitations of the common satellite techniques used to obtain displacement along the LOS (line of Sight) that can lead to the loss of information in the case of particular flexible infrastructures, as confirmed from recent studies. The bridge "Ponte della Musica Armando-Trovajoli" in Rome, made by a steel arch structures with a underlying prestressed concrete deck, considered as case study in this work, represents an example in which the satellite images do not allow to obtain useful information on the displacement, at least for the central and more deformable portion of the deck, and therefore also on the possible pathological movements. In the attempt to interpret of this phenomenon, this paper focuses on a dynamic identification campaign carried out on the bridge using ambient vibration measurements, to suitably calibrate a SAP2000 3D structural numerical model of the bridge to be considered for successive numerical evaluation of thermal deformations useful to better understand the link between physiological deformations and limits of satellite techniques.

Regional thermal environment changes: Integration of satellite data and land use/land cover

Land surface temperature (LST) is subject to location and environmental influences, which makes quantification difficult in terms of timeliness. Based on 10-d geostationary satellite LST TCI products, we quantitatively evaluated the thermal environment differentiation of various ground objects in North, South, and Northwest China from 2017 to 2021. We found that the thermal condition index (TCI) in Northwest China decreased, whereas it increased in North and South China. In contrast, Moran's I index increased in Northwest and South China, with strong spatial agglomeration. The TCI for artificial surfaces decreased from North (0.633) to Northwest (0.554) and South China (0.384). The bare land TCI was always the lowest among the land use/land cover (LULC) types in each region. Our results reflect the LULC thermal environment of China against the background of new urbanization and provide theoretical support for scientific planning to improve the ecological environment.

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.

Dissolved oxygen estimation in aquaculture sites using remote sensing and machine learning

Dissolved oxygen (DO) is one of the most critical parameters for aquaculture, as it is vital for all living organisms. The survival, growth and food intake of fish is directly affected by changes in DO concentration. Therefore, the systematic and continuous monitoring of DO is of crucial importance for proper production management. DO does not change the optical properties of water, so it is impossible to estimate its concentration directly from the reflection values of satellite sensors. However, several studies have suggested that it can be estimated indirectly, based on its correlation with other parameters such as chlorophyl-a (chl-a) and sea surface temperature (SST). The present study aims to integrate satellite data, along with in-situ observations to bring forth innovative approaches on how DO can be estimated and monitored on large scale near aquaculture facilities. In this context we exploited daily CMEMS data (chl-a and SST) along with in-situ data (DO) to train a support vector regression (SVR) model. Our in-situ dataset included daily DO measures from Agrilia fish farm in Lesvos for the year 2021. The accuracy of our model was tested using the value of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Our preliminary results indicate that our model performs well locally with promising scalability, which paves the way for the development of real-time monitoring systems for aquaculture.

Mapping urban–rural differences in the worldwide achievement of sustainable development goals: land-energy-air nexus

Land use efficiency (LUE), energy efficiency (EE), and air quality are key indicators when assessing urban-related Sustainable Development Goals, yet recent trends and trade-offs in and around urban areas worldwide remain largely unknown. We use an Earth Observation approach to map the land-energy-air sustainability nexus and highlight distinct urban–rural gradients worldwide (2000–2015). In the Global South, urban areas perform relatively better in land-energy-air sustainability trends than rural areas, which are the least sustainable in our global comparative analysis. Comparatively, urban areas in the Global North tend to be less sustainable than surrounding rural regions. Trade-offs among land-energy-air change directions are mostly related to EE versus air quality in urban areas, while spatial and temporal trade-offs between LUE and EE are more pronounced in suburban and rural areas. Integrating satellite data is crucial for tracking the progress of the land-energy-air nexus and can guide context-specific strategies to account for urban–rural differences in achieving sustainability and creating more livable environments.

Volcanic crisis management supported by near real-time lava flow hazard assessment at Piton de la Fournaise, La Réunion

Since 1979, Piton de la Fournaise (La Réunion) has erupted on average two times per year, with 95 % of these eruptions occurring within an uninhabited caldera. However, lava flows have occasionally impacted populated regions on the island, as in 1977 and 1986. Since 2014, an integrated satellite data–driven multinational response to effusive crises has been developed to rapidly assess lava inundation area and flow runout distance. In 2018, this protocol was implemented as a standalone software to provide a lava flow hazard map showing the probability of flow coverage and runouts as a function of discharge rate. Since 2019, the produced short-term hazard map is shared with local civil protection in the first few hours following the start of an eruption to aid in mitigation actions. Multiple exchanges between scientists, the observatory, and civil protection has improved the delivered hazard map, ensuring a common understanding, a product which is of use and usable, and helping to build effective mitigation strategies at Piton de la Fournaise. In this work we illustrate this effective near real-time protocol with case studies and document how the produced short-term hazard map has been tailored to meet the needs of civil protection.

Identification and Quantification of Actual Evapotranspiration Using Integrated Satellite Data for Sustainable Water Management in Dry Areas

Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region. The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.