Satellite Data Research Articles

Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran.

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

Water Quality Analysis Through Satellite Images

This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.

Thin Clouds Control the Cloud Radiative Effect Along the Sc‐Cu Transition

AbstractIn situ and spaceborne studies reveal the prevalence of thin clouds in the major Stratocumulus‐to‐Cumulus Transition (SCT) regions. Using instantaneous satellite and reanalysis data, this study investigates the properties of thin clouds in the Southeast Pacific Ocean and their impact on the cloud radiative effect (CRE). Our findings demonstrate that thin clouds are intrinsic to the SCT. The overcast stratocumulus‐dominated regime exhibits a minimal presence of thin clouds, which become notably prominent after the clouds breakup into the cumulus‐dominated regime. The regime dependence of the occurrence of thin clouds is also observed in terms of the marine cold‐air outbreak parameter and the sea surface temperature. Thin clouds at a given cloud cover significantly modulate the shortwave (SW) and longwave (LW) components of CRE. SW CRE decreases by 46 %–65 % with increasing thin cloud cover. They account for a larger variance in cloud albedo than the combined influence of the liquid water path and effective radius. Furthermore, LW CRE decreases by about 12 %–52 % with thin cloud cover. An increase in the fraction of thin clouds also leads to a larger fraction of negative SW CRE offset by positive LW CRE at a given cloud cover. This LW compensation ranges from approximately 8 % at overcast cloud cover to as much as 19 % at about 50 % cloud cover. These findings elucidate the crucial role of thin clouds, and thus cloud morphology, in modulating CRE and underscore the necessity of their accurate representation in climate models.

Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on Geospatial Computing Platform

Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.

Thermal fronts in coastal waters regulate phytoplankton blooms via acting as barriers: A case study from western Guangdong, China

Oceanic fronts play an important role in regulating the distributions of phytoplankton and are subject to climate change. However, the relationship between climate change, oceanic fronts, and coastal phytoplankton blooms requires clarification. Using long-term satellite data, the seasonal, interannual, and decadal variations of the thermal fronts were examined, along with their effects on the distribution of phytoplankton in western Guangdong. Thermal fronts between the cold western Guangdong coastal current waters and the warm South China Sea (SCS) waters were found along the coast of western Guangdong during winter due to northeast winter monsoon. At decadal timescales, an increase (decrease) in frontal activities occurred during the negative (positive) Pacific Decadal Oscillation (PDO) phase. This pattern suggests that the positive (negative) PDO activity decreases (increases) sea level anomaly (SLA) and sea surface temperature in the SCS, thereby weakens (enhances) frontal activities along the coast. Accordingly, extensions and positive anomalies (> 1.5 mg/m3) in chlorophyll-a (Chl a) were observed during the positive PDO phase, whereas Chl a was concentrated in the nearshore areas during the negative PDO phase. These processes are primarily related to the barrier effects of enhanced thermal fronts and positive SLA on offshore materials transport during the negative PDO period. This study highlights the need to incorporate PDO activity into phytoplankton production in coastal oceans.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

Curbing methane emissions is among the most effective actions that can be taken to slow down global warming. However, monitoring emissions remains challenging, as detection methods have a limited quantification completeness due to trade-offs that have to be made between coverage, resolution, and detection accuracy. Here we show that deep learning can overcome the trade-off in terms of spectral resolution that comes with multi-spectral satellite data, resulting in a methane detection tool with global coverage and high temporal and spatial resolution. We compare our detections with airborne methane measurement campaigns, which suggests that our method can detect methane point sources in Sentinel-2 data down to plumes of 0.01 km2, corresponding to 200 to 300 kg CH4 h−1 sources. Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

The Observed Impact of the Lower Stratospheric Thermodynamic Environment on Overshooting Top Characteristics During the RELAMPAGO‐CACTI Field Campaign

AbstractOvershooting tops (OTs) are manifestations of deep convective updrafts that extend above the tropopause into the stratosphere. They can induce dynamic perturbations and result in irreversible transport of aerosols, water vapor and other mass from the troposphere into the stratosphere, thereby impacting the chemical composition and radiative processes of the stratosphere. These and other effects of OTs depend on their characteristics such as depth and area, which are understood to connect to mid‐tropospheric updraft speed and width, respectively. Less understood is how static stability in the lower stratosphere (LS) potentially modulates these OT–updraft connections, thus motivating the current study. Here, LS static stability and observed OT characteristics are quantified and compared using a combination of reanalysis data, observed rawinsonde data and geostationary satellite data. A weak to moderate relationship between OT depth and LS lapse rate and Brunt‐Väisälä frequency (N2) (R = 0.38, −0.37, respectively) is found, implying that OT depth is reduced with an increasingly stable LS. In contrast, a weak relationship (R = −0.03, 0.03, respectively) is found between OT area and LS static stability, implying that OT area is controlled primarily by mid to upper tropospheric updraft area. OT duration has a weak relationship to LS lapse rate and N2 (R = 0.02, −0.02, respectively). These relationships may be useful in interpreting mid‐ and low‐level storm dynamics from satellite‐observed characteristics of OTs in near real‐time.

Rapid seagrass meadow expansion in an Indian Ocean bright spot

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000–2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km2 year−1, r2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass ‘bright spot’ highly worthy of increased attention across scientific, commercial, and conservation policy contexts.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Study on the Typical Environmental Factors in the Middle Part of Zhoushan Fishery Based on HY-1C/D and Other Multi-Source Data

This study utilizes satellite data, including HY-1C/D, along with reanalysis data, to unveil the typical environmental characteristics of the sea surface in the middle of Zhoushan fishery. The article addresses three main issues. The first one is the development of an ocean primary productivity (OPP) inversion algorithm model. The second one is the study of chlorophyll-α (Chl-α) concentration and OPP distribution characteristics in Zhoushan fishery using China’s domestically produced ocean satellite HY-1C/D CZI data. The last one is the revelation of the characteristics of typical environmental factors on the sea surface at Zhoushan fishery by combining HY-1C/D with multi-source data. The results show the following: (1) The middle part of Zhoushan fishery exhibits significant seasonal and regional variations in Chl-α concentration and OPP. Chl-α concentration ranges mainly between 0.2 and 2.9 µg/L, with higher concentrations in spring and summer and lower Chl-α concentrations in autumn and winter. Spatially, Chl-α concentration gradually decreases from west to east. The OPP in the study area ranges from 100 mg·m−2d−1 to 1000 mg·m−2d−1, with high OPP values distributed on the western side, ranging from 400 mg·m−2d−1 to 1000 mg·m−2d−1, and gradually decreasing seaward. The highest OPP occurs in summer and the lowest in winter. (2) The correlation analysis between Chl-α concentration and OPP revealed a strong positive relationship. Consequently, this study developed an empirical model for estimating OPP based on Chl-α concentration and validated its feasibility. The model applies to areas with Chl-α concentrations ranging from 0.2 to 4 µg/L. (3) The convergence of freshwater injection, multiple ocean currents, and seasonal upwelling in the study area brings about a rich supply of nutrients. Additionally, the region is characterized by suitable conditions, including optimal Chl-α concentrations, OPP, SST, salinity, currents, and geological water depths. The synergistic effect of these factors together contributed to the formation of Zhoushan fishery.

Global area boom for greenhouse cultivation revealed by satellite mapping.

Greenhouse cultivation has been expanding rapidly in recent years, yet little knowledge exists on its global extent and expansion. Using commercial and freely available satellite data combined with artificial intelligence techniques, we present a global assessment of greenhouse cultivation coverage and map 1.3 million hectares of greenhouse infrastructures in 2019, a much larger extent than previously estimated. Our analysis includes both large (61%) and small-scale (39%) greenhouse infrastructures. Examining the temporal development of the 65 largest clusters (>1,500 ha), we show a recent upsurge in greenhouse cultivation in the Global South since the 2000s, including a dramatic increase in China, accounting for 60% of the global coverage. We emphasize the potential of greenhouse infrastructures to enhance food security but raise awareness of the uncertain environmental and social implications that may arise from this expansion. We further highlight the gap in spatio-temporal datasets for supporting future research agendas on this critical topic.

Lake Surface Water Temperature in high altitude lakes in the Pyrenees: Combining satellite with monitoring data to assess recent trends

Lake Surface Water Temperature (LSWT) influences critical bio-geological processes in lake ecosystems, and there is growing evidence of rising LSWT over recent decades worldwide and future shifts in thermal patterns are expected to be a major consequence of global warming.At a regional scale, assessing recent trends and anticipating impacts requires data from a number of lakes, but long term in situ monitoring programs are scarce, particularly in mountain areas. In this work, we propose the combined use of satellite-derived temperature with in situ data for a five-year period (2017–2022) from 5 small (<0.5km2) high altitude (1880–2680 masl) Pyrenean lakes. The comparison of in situ and satellite-derived data in a common period (2017–2022) during the summer season showed a notably high (r = 0.94, p < 0.01) correlation coefficient, indicative of a robust relationship between the two data sources. The root mean square errors ranged from 1.8 °C to 3.9 °C, while the mean absolute errors ranged from 1.6 °C to 3.6 °C.We applied the obtained in situ-satellite eq. (2017–2022) to Landsat 5, 7 and 8/9 data since 1985 to reconstruct the summer surface temperature of the five studied lakes with in situ data and to four additional lakes with no in situ monitoring data. Reconstructed LSWT for the 1985–2022 showed an upward trend in all lakes. Moreover, paleolimnological reconstructions based on sediment cores studies demonstrate large changes in the last decades in organic carbon accumulation, sediment fluxes and bioproductivity in the Pyrenean lakes.Our research represents the first comprehensive investigation conducted on high mountain lakes in the Pyrenees that compares field monitoring data with satellite-derived temperature records. The results demonstrate the reliability of satellite-derived LSWT for surface temperatures in small lakes, and provide a tool to improve the LSWT in lakes with no monitoring surveys.

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.

Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica

Abstract. The firn temperature is a crucial parameter for understanding firn densification processes of the Antarctic Ice Sheet (AIS). Simulations with firn densification models (FDM) can be conceptualized as a function that relies on forcing data, comprising temperature and surface mass balance, together with tuning parameters determined based on measured depth-density profiles from different locations. The simulated firn temperature is obtained in the firn densification models by solving the one-dimensional heat conduction equation. Microwave satellite data on brightness temperature at different frequencies can also provide remote sensing monitoring of firn temperature variations across the AIS (i.e., the L-band up to 1500 meters). The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. One method estimated the temperature profiles by solving the one-dimensional heat conduction equation driven by reanalyses and regional climate models, which are used in the simulation of FDMs. The other one established a relationship between the multi-frequency brightness temperature data from microwave remote sensing satellites and the firn temperature.

Joule Heating rate at high-latitudes by Swarm and ground-based observations compared to MHD simulations

We compare Joule Heating rates as derived from ground-based magnetic field and all-sky camera data, from Low Earth Orbit satellite data (ESA Swarm) and from a MHD simulation (GUMICS-5) with each other in a case study of an auroral arc system. The observational estimates of Joule Heating rates provide information on regional scales and with high spatial resolution (10–100 km). Their comparison with global MHD results is conducted for a quiet time interval of a few minutes, just before a magnetic substorm. Analysis of the ground-based observations yields electric field with dominating North-South component pointing towards the arcs and having maxima values in the range 20–35 V/km. Combining these values with Pedersen conductance estimates from optical data (5–10 S) yields Joule Heating rates in the range 2.5–3.5 mW/m2. Swarm electric field measurements are consistent in their direction and intensity with the ground-based estimates. They also show that heating is increased particularly in the region where the conductance is low. The total amount of Joule heating in the area between the Swarm A and C satellite footprints while crossing the all-sky camera field of view is estimated to be 46 MW and the total amount energy dissipation during the 80 s overflight is around 3.6 GJ (1000 kWh). GUMICS-5 estimate of the peak Joule Heating in the magnetic local time sector of the arc system is smaller than that from the ground-based data with a factor of 2.9. Comparisons of GUMICS-5 results with Space Weather Modeling Framework (SWMF), shows that the latter gives on average larger heating rates being thus more consistent with our regional observations. However, both MHD-codes yield smaller Joule Heating rates around the time of the arcs and during the following substorm than the CTIP-e code. CTIP-e has a more detailed description of ionosphere-thermosphere interactions than the MHD-codes and its convection electric field is enhanced with a randomly varying additional component mimicking small scale structures. GUMICS-SWMF comparisons of global Joule Heating patterns in the Northern polar area reveal that the two simulations have significant differences in their spatial distribution of heating rates. Main cause for these deviations is the difference in the derivation of ionospheric Pedersen conductance. Our results emphasize the fact that future estimates of the global energetics in the magnetosphere–ionosphere–thermosphere system require better knowledge on ionospheric conductivities, both by new measurement concepts and by better understanding on the background physics controlling conductivity variations.

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Operational workflow to simulate biophysical variables, based on the coupled WRF/SEBAL models

Three subsystems including an atmospheric regional forecast and analysis system, satellite data retrieval and processing, and a surface energy balance model have been employed in a 24/7 operational system to measure and simulate some primary agricultural variables, including evapotranspiration, biomass production, and other associated productivity metrics such as NDVI and Albedo. The final products are generated in a daily routine, covering Iran as the domain of simulation. The WRF atmospheric model, VIIRS, and SENTINEL2 satellite data, as well as the SEBAL model (implemented in pySEBAL) are the main components of the operational system, with products in two different spatial resolutions of 375 m and 100 m. The output products can be used by farmers and stakeholders to manage agricultural water requirements and monitor biophysical parameters.

An explainable machine learning technique to forecast lightning density over North-Eastern India

Increasing lightning fatalities over India is a concerning subject. Especially, it is pretty crucial over North-Eastern part of the country where lightning is extremely frequent. Given the complex nature of the problem, machine learning can be an excellent option in such forecasting scenarios. However, such dynamic processes seek proper transparency of the model. The current work attempts to devise a model for short range prediction (one month ahead) of lightning density based on primary atmospheric parameters from satellite data with a lead time of one month over North –Eastern and Eastern part of the country. Random Forest regression seems to outperform other models explored, with a R2 of 0.86 and an MAE of 0.0071. The interpretation of the model output using SHAP index reveals that 2 m temperature at previous two months and CAPE and K-index at previous month has a positive impact on the output of the model whereas, instantaneous surface heat flux of previous month and two month prior K-index has an inhibiting effect on model's output. The use of machine learning techniques for atmospheric predictions without the shed of the black box can be of importance to the scientific community. Such studies especially over lightning prone tropical regions can be crucial in meteorological forecasting applications.