Moderate Resolution Imaging Spectroradiometer Research Articles

Divergent biophysical responses of western United States forests to wildfire driven by eco-climatic gradients

Abstract. Understanding vegetation recovery after fire is critical for predicting vegetation-mediated ecological dynamics in future climates. However, information characterizing vegetation recovery patterns after fire and their determinants over large geographical extents is limited. This study uses Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and albedo to characterize patterns of post-fire biophysical dynamics across the western United States (US) and further examines the influence of topo-climatic variables on the recovery of LAI and albedo at two different time horizons, 10 and 20 years post-fire, using a random forest model. Recovery patterns were derived for all wildfires that occurred between 1986 and 2017 across seven forest types and 21 level III ecoregions of the western US. We found differences in the characteristic trajectories of post-fire vegetation recovery across forest types and eco-climatic settings. In some forest types, LAI had recovered to only 60 %–70 % of the pre-fire levels by 25 years after the fire, while it recovered to 120 %–150 % of the pre-fire levels in other forest types, with higher absolute post-fire changes observed in forest types and ecoregions that had a higher initial pre-fire LAI. Our random forest results showed very little influence of fire severity on the recovery of both summer LAI and albedo at both post-fire time horizons. Post-fire vegetation recovery was most strongly controlled by elevation, with faster rates of recovery at lower elevations. Similarly, annual precipitation and average summer temperature had significant impacts on the post-fire recovery of vegetation. Full recovery was seldom observed when annual precipitation was less than 500 mm and average summer temperature was above the optimal range, i.e., 15–20 °C. Climate influences, particularly annual precipitation, were a major driver of post-fire summer albedo change through its impact on ecological succession. This study provides quantitative measures of primary controls that could be used to improve the modeling of ecosystem dynamics post-fire.

Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which can arise from variations in viewing geometries and inherent differences in sensor types and LST algorithms. Despite this, previous research has primarily focused on analysing the directionality of LST without thoroughly exploring systematic differences between platforms. Hence, we presented a Solar Zenith Angle-based Calibration (SZAC) method to harmonise the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline) with the MODerate-resolution Imaging Spectroradiometer (MODIS) LST. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different platforms, which is anticipated to complement typical directionality analyses. We evaluated the harmonised LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparison. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and −0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. SZAC can also reduce LST angular effects due to its capability of quantifying spatial inhomogeneity of surface heat dynamics. Furthermore, it is possible to implement SZAC using LST data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas. This could improve our understanding of the error characteristics of coincident imageries from geostationary and polar-orbiting platforms, allowing for targeted refinements and global harmonisations to further enhance applicability.

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach

Abstract. This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager.

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana.

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Increasing Fire Activity in African Tropical Forests Is Associated With Deforestation and Climate Change

AbstractFires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfires. We evaluated historical patterns of Moderate Resolution Imaging Spectroradiometer active fires in African tropical forests from 2003 to 2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year‐to‐year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015–2016 El Niño. These results contrast with the drier African woodlands and savannas, where fire has been decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon dynamics and their local implications for biodiversity and human livelihoods.

Improving MODIS Gross Primary Productivity by Bridging Big‐Leaf and Two‐Leaf Light Use Efficiency Models

AbstractGross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product.

Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates

AbstractWater budget over anthropized basins is profoundly altered by human interventions, among which irrigation prevails. This study investigates how such water use affects hydrological flux and state variables by comparing two SURFEX/ISBA (SURFace EXternalisée/Interaction Sol Biosphère Atmosphère) Land Surface Model simulations performed over a portion of the Ebro basin (Spain) that includes a heavily irrigated area. The first simulation is forced by atmospheric input only, while the second one is fed with atmospheric forcing plus remote sensing‐derived irrigation amounts added to the liquid precipitation. Four main hydrological output variables are compared under the two configurations: surface soil moisture (SM), total evaporative flux (i.e., the sum of components from bare soil and vegetation) (E), drainage, and runoff. Results indicate that SM and E are the variables mostly impacted during the main watering season. Considering mean daily rates per each month, a maximum increase of +30% and of +220% is found over the irrigation districts in July for SM and E, respectively. The reliability of the results is supported by comparisons with satellite evapotranspiration data from MODIS (MODerate‐resolution Imaging Spectroradiometer). The inclusion of irrigation reduces the RMSE between monthly anomalies of modeled E and of MODIS reference data by 41% during the main watering season. Further validation through wavelet coherence analysis is also proposed. This study sheds light on the potential of explicit satellite‐derived irrigation water use data that, when integrated into modeling platforms, allow us to track and quantify anthropogenic impacts on water resources.

Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument

Accurate three-dimensional (3D) cloud structure measurements are critical for assessing the influence of clouds on the Earth’s atmospheric system. This study extended the MODIS (Moderate-Resolution Imaging Spectroradiometer) cloud vertical profile (64 × 64 scene, about 70 km in width × 15 km in height) retrieval technique based on conditional generative adversarial networks (CGAN) to construct seamless 3D cloud fields for the MODIS granules. Firstly, the accuracy and spatial continuity of the retrievals (of 7180 samples from the validation set) were statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bidirectional ensemble binning probability fusion (CGAN-BEBPF) technique were developed, which improved the CGAN retrieval accuracy and support to construct seamless 3D clouds for the MODIS granules. The CGAN-BEBPF technique involved three steps: cloud masking, intensity scaling, and optimal value selection. It ensured adequate coverage of the low reflectivity areas while preserving the high-reflectivity cloud cores. The technique was applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system and the results were compared with ground-based radar measurements. The cloud structures of the CGAN-BEBPF results were highly consistent with the ground-based radar observations. The CGAN-EBEPF technique retrieved weak ice clouds at the top levels that were missed by ground-based radars and filled the gaps of the ground-based radars in the lower levels. The CGAN-BEBPF was automated to retrieve 3D cloud radar reflectivity along the MODIS track over the seas to the east and south of mainland China, providing valuable cloud information to support maritime and near-shore typhoons and convection prediction for the cloud-sensitive applications in the regions.

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

Characterization of dust aerosols from ALADIN and CALIOP measurements

Abstract. Atmospheric aerosols have pronounced effects on climate at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is an incomplete understanding of the vertical structure of aerosol, largely due to observational limitations. Spaceborne lidars can directly observe the vertical distribution of aerosols globally and are increasingly used in atmospheric aerosol remote sensing. As the first spaceborne high-spectral-resolution lidar (HSRL), the Atmospheric LAser Doppler INstrument (ALADIN) on board the Aeolus satellite was operational from 2018 to 2023. ALADIN data can be used to estimate aerosol extinction and co-polar backscatter coefficients separately without an assumption of the lidar ratio. This study assesses the performance of ALADIN's aerosol retrieval capabilities by comparing them with Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) measurements. A statistical analysis of retrievals from both instruments during the June 2020 Saharan dust event indicates consistency between the observed backscatter and extinction coefficients. During this extreme dust event, CALIOP-derived aerosol optical depth (AOD) exhibited large discrepancies with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua measurements. Using collocated ALADIN observations to revise the dust lidar ratio to 63.5 sr, AODs retrieved from CALIOP are increased by 46 %, improving the comparison with MODIS data. The combination of measurements from ALADIN and CALIOP can enhance the tracking of aerosols' vertical transport. This study demonstrates the potential for spaceborne HSRL to retrieve aerosol optical properties. It highlights the benefits of spaceborne HSRL in directly obtaining the lidar ratio, significantly reducing uncertainties in extinction retrievals.

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

Evaluation of the change in crop structure under the impact of flood prevention dikes in the Long Xuyen Quadrangle in the period 2005-2020

The study was conducted to monitor spatial fluctuations of dikes affecting the rice crop structure and evaluate changes in rice crop structure inside and outside the dikes in the Long Xuyen Quadrangle in the period 2005-2020. That serves as a basis for assessing the current status of flood control and the changing trend of rice production in the region in the future. The study uses LANDSAT images and applies the normalized difference water index (NDWI) and shoreline extraction to determine the current condition of dikes in the Long Xuyen Quadrangle region, while combining a series of normalized difference vegetation index (NDVI) on moderate resolution imaging spectroradiometer (MODIS) image data to determine the structure of rice crops in 2005 and 2020. The results show that the embankment length increased by 1,910.15 km compared to 2005 and the natural embankment area increased to 123,020.31 ha (accounting for 24.63% of the area’s natural area) in the period 2005-2020. The farming system in the study area impacted by flood prevention dike changes from other crops to triple-rice crop cultivation. Specifically, the total area of the other cultivation types converted into triple-rice crop cultivation is 90,904.77 ha, accounting for 51.92% of the total area of structural transformation of farming types, concentrated in districts with large dike areas such as Thoai Son, Chau Phu, and Chau Thanh districts (An Giang province). The research results contribute to supporting environmental management and orienting rice production trends in the region in the future.

Factors influencing the variation of the Sepik-Ramu River system's sediment plume off the north coast of New Guinea

The Sepik-Ramu River system (SRRs) is a principal contributor of terrestrial materials to the pelagic waters of the tropical Western Pacific, boasting the highest sediment discharge in New Guinea. However, this system's sediment outflow and dispersal patterns are not well-documented. This study seeks to address this gap by analyzing the estuarine sediment plumes, which are the key indicators of the marine dispersion of terrestrial materials. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2010 to 2020 and applying the Novoa17 algorithm, we examined the distribution and temporal evolution of the sediment plume of the SRRs estuary. The sediment plume was categorized into two distinct zones: core and diffuse, based on suspended sediment concentration (SSC) levels. Our findings reveal significant seasonal variations of these sediment plumes in the nearshore. The plume area expands during the wet season and contracts during the dry season, respectively, with core and diffuse plumes sharing similar change characteristics. The diffuse plume primarily extends northeast in the wet season and subsequently shifts northwest in the dry season, and the core plume consistently points to the northeast. The study further explores the driving factors and mechanisms behind these patterns, identifying river discharge as the primary influence on the core plume and sea surface winds and coastal currents as determinants of the diffuse plume's behavior. Interannual variations in the plume characteristics were found to be influenced by the El Niño-Southern Oscillation (ENSO), which indirectly affects plume dynamics through its impact on regional precipitation. During El Niño events, characterized by drier conditions and reduced discharge, the plume SSC and area typically decrease. During La Niña events, the plume's SSC and area typically increase, except for the 2011 super La Niña's negative anomaly, caused by the rain belt's excessive westward shift.

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worst category. Notably, there is a significant increase in summer mean LST from 32.42 °C in 2016 to 38.27 °C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92 °C to 38.06 °C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

Estimation of Nighttime Aerosol Optical Depths Using Atmospheric Infrared Sounder Longwave Radiances

AbstractAerosol remote sensing typically relies on reflected shortwave radiation and thus lacks nighttime retrievals. Here we made an original attempt to retrieve nighttime aerosol optical depth (AOD) by utilizing longwave measurements in the atmospheric window region from the Atmospheric InfraRed Sounder (AIRS) instrument. A machine‐learning based algorithm is developed using AIRS longwave radiance and auxiliary data as the input and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) as well as reanalysis surface temperature as the output. Independent validation indicates good agreement with lunar AOD derived from surface photometers. An overall increase in nighttime AOD compared to daytime is also uncovered, which is further corroborated by surface and space lidar measurements. The theoretical basis of the algorithm is further verified using radiative transfer simulations. Our approach substantially extends the potential of hyperspectral longwave measurements and offers valuable insights into nighttime aerosol properties.

Planetary Energy Flow and Entropy Production Rate by Earth from 2002 to 2023.

In this work, satellite data from the Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are analyzed to determine how the global absorbed sunlight and global entropy production rates have changed from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of Earth's surface and atmosphere. The results indicate that both the rate of absorbed sunlight and global entropy production have increased over the last 20 years, which is consistent with the predictions of both hypotheses. Given the acceptance of the MPP or MEP, some peripheral extensions and nuances are discussed.

Impact of Wildfires on Land Surface Cold Season Climate in the Northern High-Latitudes: A Study on Changes in Vegetation, Snow Dynamics, Albedo, and Radiative Forcing

Anthropogenic climate change is increasing the occurrence of wildfires, especially in northern high latitudes, leading to a shift in land surface climate. This study aims to determine the predominant climatic effects of fires in boreal forests to assess their impact on vegetation composition, surface albedo, and snow dynamics. The influence of fire-induced changes on Earth’s radiative forcing is investigated, while considering variations in burn severity and postfire vegetation structure. Six burn sites are explored in central Alaska’s boreal region, alongside six control sites, by utilizing Moderate Resolution Imaging Spectroradiometer (MODIS)-derived albedo, Leaf Area Index (LAI), snowmelt timing data, AmeriFlux radiation, National Land Cover Database (NLCD) land cover, and Monitoring Trends in Burn Severity (MTBS) data. Key findings reveal significant postfire shifts in land cover at each site, mainly from high- to low-stature vegetation. A continuous increase in postfire surface albedo and negative surface shortwave forcing was noted even after 12 years postfire, particularly during the spring and at high-severity burn areas. Results indicate that the cooling effect from increased albedo during the snow season may surpass the warming effects of earlier snowmelt. The overall climate impact of fires depends on burn severity and vegetation composition.

Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China

Abstract. The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.1<AOD<0.3) over the ECS, whereas, in contrast, CER increases with increasing AOD (positive S) in the polluted atmosphere (AOD>0.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.