Moderate Resolution Imaging Spectroradiometer Satellite Research Articles

Capturing the spatiotemporal variations in the gross primary productivity in coastal wetlands by integrating eddy covariance, Landsat, and MODIS satellite data: A case study in the Yangtze Estuary, China

Accurate monitoring of the spatiotemporal variations in the gross primary productivity (GPP) in coastal wetlands is essential for blue carbon quantification. However, the currently available moderate-spatial-resolution GPP algorithms and products may contain large uncertainties and may rarely meet the demands of tidal wetland research. In this paper, we examine two statistical methods, namely, simple linear regression and random forest regression, to capture the high spatial resolution GPP variations in the salt marshes in the Yangtze Estuary by integrating multi-source data, including eddy covariance, Landsat, and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data. The satellite-derived photosynthetically active radiation (PAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) are used individually and in different combinations to drive the two statistical methods and investigate their performances in estimating the GPP. The results show that together with the PAR, the EVI generally has the greatest potential for GPP estimation (R2 > 0.75, RMSE < 6.80 μmol m−2 s−1) based on simple linear regression. However, the NDVI outperforms the EVI in wetlands with little tidal flooding and plant litter. The random forest method improves the performance in terms of upscaling the GPP measurements to large regions, and the combination of the EVI, NDVI, NDWI, and PAR performs the best, indicating that the use of the more advanced algorithm and the incorporation of a remotely sensed index that reflects the tidal influence are beneficial to GPP estimation in coastal salt marshes. Moreover, using Landsat data with a high spatial resolution (30 m) yields a much more accurate GPP estimation than using MODIS data with a 500 m resolution. Our results demonstrate that the use of high spatial resolution data, the proper use of remotely sensed indices, and the incorporation of a good combination of these indices through an advanced algorithm such as a machine learning algorithm are vital for capturing the nearly instantaneous GPP variations in coastal wetlands at large spatial scales.

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

Canopy temperature: as an indicator of soil salinity (a case study in Syrdarya province, Uzbekistan)

A shift in the temperature of the canopy may signify stress in the plants. In laboratory and greenhouse trials, using canopy temperature for the measurement salt stress in certain agricultural crops was thoroughly examined; however, its potential application in landscape-level investigations employing remote sensing methods has not yet been investigated at different time series. A satellite thermography for measuring the soil salinity of agricultural areas at the provincial level was the subject of our investigation. The research area was the irrigated, semi-arid, and salt-affected agricultural land appertain to Syrdarya province in Uzbekistan, which was mostly planted with wheat and cotton. The provincial soil salinity map was considered as a ground truth data and the moderate-resolution imaging spectroradiometer satellite (MODIS) data were perceived as an indication for canopy temperature in this study. We investigated the relationships between the soil salinity, the normalized difference vegetation index, and canopy temperature, using analysis of variance. The findings indicated a strong inverse correlation between canopy temperature and soil salinity, although this relationship changed throughout the experimental years. For cotton, the highest correlation was shown in September. In comparison to the other variables looked at, canopy temperature had higher computed F values. Our findings indicate that soil salinity may be detected at the landscape level using satellite thermography in regions where crops are being grown.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R &amp;gt; 0.86, p &amp;lt; 0.05) and AUHII (R &amp;gt; 0.68, p &amp;lt; 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

The influence of dust aerosols on solar radiation and near-surface temperature during a severe duststorm transport episode

In the west of China, a rarely seen black storm, with a high intensity of dust aerosols and a large area of influence, occurred from April 26–29, 2015, for the first time, after more than 30 years. Based on the regional climate model (RegCM version 4.6), combined with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrieval, meteorological, and environmental data, this work presents the pollution situation, weather background, and backward trajectory during the black storm process. In addition, we analyzed the temporal–spatial distribution of aerosol optical depth (AOD) and the impacts of dust aerosols on solar radiation and near-surface temperature under this extreme weather condition. We discovered that this black storm process was caused by the surface cold high pressure and frontal transit under the background of the upper-air circulation of “two troughs and two ridges.” The pollutants primarily from Xinjiang and the Central Asia region, along with the airflow, entered northern Xinjiang almost simultaneously with the southwest airflow, piled up along the Tianshan Mountains, and then crossed the mountains into southern Xinjiang. In addition, the areas with high AOD were mostly in the desert regions and basins, whereas the low-value areas were mountainous areas with relatively high altitudes due to the effect of geographical and climatic conditions. The AOD from RegCM 4.6 was generally lower, unlike the MODIS AOD. Moreover, the dust aerosols from this black storm caused a significant decline in net short-wave radiation (NSR) both at the top of the atmosphere (TOA) and surface. The cooling effect was more significant in the region with high AOD. For the areas where the AOD was higher than 0.7, the net short-wave radiative forcing of dust aerosols (ADRF) at the surface was above −70 W•m−2; on the other hand, the cooling effect at the TOA was not as significant as that at the surface, with the ADRFTOA being only about a quarter of the ADRFSUR. The ADRFSUR could reduce the near-surface temperature, and the region with a large temperature drop corresponded to the high-value areas of ADRFSUR/AOD. During this black storm, the near-surface temperature response to dust aerosols reached 0.40°C–2.9°C with a significant temperature drop because of cold air.

A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning

Agriculture drought is a decrease in soil moisture during a growing season. In this study, a comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) was developed to monitor the agriculture drought in semi-arid environments and assess its effectiveness in rainfed agriculture regions in (A) Jordan and (B) Syria. First, remote sensed-based drought-condition spectral indices [i.e., Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI), Precipitation Condition Index (PCI), Soil Moisture Condition Index (SMCI), and Vegetation Health Index (VHI)] were calculated using data from Moderate Resolution Imaging Spectroradiometer satellite (MODIS) [Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and evapotranspiration (ET)]; the Global Precipitation Measurement (GPMs); the Soil Moisture Active Passive (SMAPs); and Sentinel-1A. Second, Random Forest (RF) was used to estimate and determine the relative importance of these indices based on Standardized Precipitation Index (SPI) values to select the three spectral indices that have the most monthly short-term relative importance in identifying the agriculture drought for semi-arid environments, which were PCI, TCI, and VCI. Third, these indices were integrated to identify the drought severity based on specific thresholds that compare the pixel-specific value with the study area average value. For instance, a severe drought condition is identified if all three indices indicate a drought condition, a moderate drought or mild drought conditions are identified if any two or any one of the indices indicate drought conditions, respectively. Lastly, a none drought condition is identified if none of the indices indicate a drought condition. Finally, the SPI sets for 1 and 3-months (SPI-1 and SPI-3) were used to evaluate the performance of the CADCI. The results showed the CADCI has a high agreement with SPI-1 classes in the study areas, with overall accuracy and Kappa-values of 85% and 0.80, for study area A and 83% and 0.76 for study area B, respectively. Consequently, the CADCI shows its ability to monitor agricultural drought in semi-arid environments. Perhaps, it could be applicable for larger areas due to the spatial resolution of the input dataset.

Time series temperature anomalies for earthquake prediction using remote sensing techniques: A case study of five major earthquakes in pakistan’s history

The earthquakes prediction based on LST anomalies remain a contradicting debate due to limited and short-term data around the earthquake’s epicenter. The results are varying in different case studies, only few research matches with pre seismic LST anomalies while many are not found to be significant for earthquakes predication. This study assessed temperature anomalies in different earthquakes events of Mw ≥ 6.0 in Pakistan from 2000 to 2020. The present study uses Moderate Resolution Imaging Spectroradiometer (MODIS) satellite thermal imagery data over the epicenter region to analyze thermal anomaly associated and earthquake prediction. The surface temperature growth before the earthquake event is estimated using the satellite thermal infrared images carrying valuable earthquake precursory information. The study area Pakistan is prone to active seismicity due to Northern Head-on Collision of Indian-and Eurasian plate; Southern Makran Subduction Zone of Indian and Arabian plate and the regional Chaman transform boundary. The results show pre seismic temperature variation range between 30 °C − 54 °C, which seems to be a good prospect for linking thermal anomalies to earthquake prediction.

Evaluation of biogenic emissions from three different vegetation distributions in South Korea

Map of plant functional type (PFT) is one of key information in estimating the bottom-up biogenic emissions. This study investigated the performance of three different maps of PFT on biogenic emissions in South Korea: i) PFT map from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite sensor onboard Terra-/Aqua-satellites (MPFT); ii) PFT map from the Environmental Geographic Information System (EGIS) provided by the Korean Ministry of Environment (EPFT); and iii) PFT map from the Forest Geographic Information System (FGIS) established by the Korea Forest Service (FPFT). These three different vegetation distributions were used as input information into the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model. The biogenic emissions estimated by the MEGAN model simulations were then evaluated by comparisons between the Community Multi-scale Air Quality (CMAQ) simulated mixing ratios of biogenic species with ground- and aircraft-borne mixing ratios during the period of the Korea–United States Air Quality (KORUS-AQ) campaign. The analyses found that the simulation using the FPFT map, which has been regarded as ‘ground truth’, produced the best results in terms of several statistical metrics. For example, among the three experiments, the simulation with the FPFT map produced the best index of agreement (IOA) of 0.76 and the lowest normalized mean bias (NMB) of 5.61%, in comparison with ground-based isoprene observations. The simulations with the MPFT map also performed reasonably, showing low NMBs of −42.13% and −14.87% for the isoprene and the first-generation products of isoprene, respectively, in comparison with the DC-8 airborne observations. In addition, the applications of the MPFT map to the MEGAN modeling showed significant overestimations in the mixing ratios of biogenic species over the SoBaek (SB) mountain ranges. It was believed that this was due to the overestimated density of broadleaf deciduous temperate trees over the SB mountain ranges in the MPFT map. On the other hand, the EPFT map showed the worst performances, which might be related to crude classification of vegetation. This study also suggests two important possibilities. First, the current isoprene-related photochemistry appears to be highly uncertain, based on large underestimations of the first-generation products of isoprene, in comparison with observations. Second, the emission factors of monoterpenes in the MEGAN model may be too small, based on large underestimations of monoterpenes, in comparison with observations.

Določevanje fenoloških faz izbranih drevesnih vrst s časovnimi vrstami posnetkov MODIS

This study investigates the usefulness of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery for determining the start, end, and length of the growing season of selected deciduous tree species. Vegetation indices derived from satellite imagery provide consistent observations in a similar temporal sequence and are useful for determining phenological phases. Using time series of NDVI (Normalised Difference Vegetation Index) vegetation index from MODIS imagery, phenological patterns were detected at several points in Slovenia and different approaches to determine seasonal phases were compared. In addition, the derived seasonal phases with field phenological and meteorological data were also compared. It has been found that the success of determining phenological phases from satellite imagery depends on many factors: the spatial resolution of the satellite data, the smoothing method for the time series data, the method for determining phenological parameters, and the field data used for comparison. The results of the study show that phenological phases determined by using MODIS data with a resolution of 250 m best match the phenological data maintained by the Slovenian Forestry Institute using the mean seasonal values method.

Outdoor heat stress assessment using an integrated multi-scale numerical weather prediction system: A case study of a heatwave in Montreal

More frequent and severe extreme weather events such as heatwaves are among the most serious challenges to society in coping with the changing climate. To evaluate the impacts of the heatwave on large-scale urban areas, a multi-scale weather forecasting system is designed by integrating different resolutions of the Canadian urbanized version of the Global Environmental Multiscale (GEM) Numerical Weather Prediction (NWP) model, cascading from 10 km to 2.5 km, and 250 m. The multi-scale model is implemented in Montreal, Canada, for modeling the 2018 heatwave. Simulation results are well-validated against measurement data, including Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and ten weather stations in the city. The Universal Thermal Climate Index (UTCI) map was calculated to identify vulnerable regions in the city against the heatwave. Land-use types in hotspots and coldspots are analyzed to find dominant factors in the formation of hot and cold areas. It is found that natural landscapes such as vegetation, trees, and water bodies are the dominant features of most coldspots. On the other hand, roads, parking lots, less tree covers, and industrial activities are the common land use features in the hotspots. A weak correlation is found between heat-related death locations and the outdoor UTCI map, implying that the assessment of an outdoor heatwave may not address overheated buildings and communities. This paper shows the importance of built environments – their properties and occupants' socio-demographic factors in the study of heat-related mortalities in cities.

Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring.

Variations in aerosols and aerosols–cloud interactions in Bangkok using MODIS satellite data during high PM2.5 concentrations

The relationships between particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5), aerosol optical depth (AOD), and cloud parameters were investigated during high PM2.5 concentrations from 1 January 2016 to 31 December 2021 in Bangkok using satelliteremote sensingdatafrom Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud parameters included: Cloud effective radius (CER), Cloud fraction (CF), Cloud optical depth (COD), Cloud liquid water (CLW), Cloud top pressure (CTP), and Cloud top temperature (CTT). The analysis showed a strong negative correlation between PM2.5 and CF. There was a negative correlation between CF and AOD. In addition, PM2.5 showed a negative relationship with CER. In contrast, PM2.5 showed a positive correlation with CTP and CTT. A negative correlation was detected between COD and PM2.5. During high PM2.5 concentrations, cloud parameters are significantly influenced by PM2.5 concentrations in the atmosphere. The three-month of CER, CF, CLW, COD, CTP, and CTT ranges from 16.34 μm to 37.05 μm, 0.22 to 0.86, 15.67 g/m2 to 169.80 g/m2, 1.14 to 19.06, 261.67 hPa to 991.67 hPa, and −66.26C to –22.17C, respectively. Consequently, Altostratus clouds most appear during high PM2.5 concentrations in Bangkok. The findings may help better understand Bangkok's cloud properties and atmospheric aerosols, providing more information for rainmaking during high PM2.5 concentrations.

Mysterious increases of whiting events in the Bahama Banks

“Whiting” events in the Bahama Banks, due to high concentrations of carbonate-rich particles suspended in the water, have been reported and discussed widely in the past 80 years. However, little is known about their distributions and particularly about their long-term changes. Here, using a deep learning (DL) model, we objectively delineate and quantify whiting features from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images (250-m resolution) and establish an 18-year data record (2003−2020) of whiting occurrences in the Bahama Banks. Both the Great Bahama Bank (GBB) and the Little Bahama Bank (LBB) show clear seasonality in whiting areas, where a primary peak in spring and a secondary peak in winter are found in the GBB but only one peak in winter is found in the LBB. Such a seasonality may be explained using a hydrodynamic hypothesis on calcium carbonate precipitation. The mean size of individual whiting patches in the GBB is about 2.4 ± 6.1 km2 (∼0.1 to 226 km2), while in the LBB is 1.4 ± 2.7 km2 (∼0.1 to 95 km2). The total whiting coverage in a typical cloud-free image is 87.1 km2 in the GBB and 32.0 km2 in the LBB, representing 0.14% and 0.76% of the entire GBB and LBB, respectively. Significant increases in the mean coverage have been found in the GBB since 2011, with peak coverage (∼200 km2) in 2013–2015 being at least 4 times higher than before (20–70 km2). Although the whiting area started to decrease after 2015, it did not reach the pre-2011 level until 2020. On the other hand, correlation analysis and principal component analysis of several environmental factors (pH, light, salinity, carbonate, aragonite, winds, currents) provided some hints on which factors may have contributed. From these, we infer a potential ‘Goldilocks’ scenario, whereby decreases in pH and carbonate concentration, concomitant with increases in Sea Surface Temperature (SST) and current speeds, created conditions increasingly favorable for whitings from 2011 to 2015. Continuation of these environmental trends after 2015, however, resulted in conditions increasingly unfavorable for whiting formation, yet without field-based measurements it is difficult to conclude the potential reasons for increases and decreases of whiting formation in the GBB.

Mid-summer snow-free albedo across the Arctic tundra was mostly stable or increased over the past two decades

Arctic vegetation changes, such as increasing shrub-cover, are expected to accelerate climate warming through increased absorption of incoming radiation and corresponding decrease in summer shortwave albedo. Here we analyze mid-summer shortwave land-surface albedo and its change across the pan-Arctic region based on MODerate resolution Imaging Spectroradiometer satellite observations over the past two decades (2000–2021). In contrast to expectations, we show that terrestrial mid-summer shortwave albedo has not significantly changed in 82% of the pan-Arctic region, while 14% show an increase and 4% a decrease. The total median significant change was 0.014 over the past 22 years. By analyzing the visible and near-/shortwave-infrared range separately, we demonstrate that the slight increase arises from an albedo increase in the near-/shortwave infrared domain while being partly compensated by a decrease in visible albedo. A similar response was found across different tundra vegetation types. We argue that this increase in reflectance is typical with increasing biomass as a result of increased multiple reflection in the canopy. However, CMIP6 global land surface model albedo predictions showed the opposite sign and different spatial patterns of snow-free summer albedo change compared to satellite-derived results. We suggest that a more sophisticated vegetation parametrization might reduce this discrepancy, and provide albedo estimates per vegetation type.

Assessment of mangrove cover dynamics and its health status in the Gulf of Khambhat, Western India, using high-resolution multi-temporal satellite data and Google Earth Engine.

Anthropogenic activity is a major driving factor of greenhouse gas emission, leading to climate change worldwide. So, the best natural approach to lowering the carbon from the atmosphere is mangroves which have more potential to sequestrate carbon. But mangroves are under threat due to land use land cover change. This research has been carried out on the mangroves of Gulf of Khambhat, Gujarat, India, where anthropic activity is affecting the mangrove forest cover with spatiotemporal heterogeneity. In the present study, multi-temporal high-resolution satellite data AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) and LISS-4 (Linear Imaging Self-Scanning Sensors-4) were used for the demarcation of various land use/land cover class (LULC), and change analysis and assessment of mangroves health for the years 2009, 2014, and 2019. The impact of saltpan/aquaculture on mangroves growth and its health status has been calculated by various MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data products such as gross primary productivity (GPP), enhanced vegetation index (EVI), and leaf area index (LAI) in Google Earth Engine (GEE), and field-based method was also considered. This study suggests that there is a marginal increase (17.11 km2) in mangrove cover during the assessment period 2009-2019; on other side, 65.42 km2 was degraded also. However, increase in saltpan/aquaculture is imposing an adverse effect on mangroves' basal area, plant density, and productivity. Change analysis also suggests a reduction in healthy mangrove area (from 25.20 to 2.84 km2), which will have an impact on ecosystem services.

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

ABSTRACT The all-wave net radiation (Rn) at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles. Many studies have been conducted to estimate from satellite top-of-atmosphere (TOA) data using various methods, particularly the application of machine learning (ML) and deep learning (DL). However, few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving. Based on extensive in situ measurements distributed at mid-low latitudes, the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) TOA observations, and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) used as a priori knowledge, this study assessed nine models for daily estimation, including six classic ML methods (random forest -RF, adaptive boosting - Adaboost, extreme gradient boosting -XGBoost, multilayer perceptron -MLP, radial basis function neural network -RBF, and support vector machine -SVM) and three DL methods (multilayer perceptron neural network with stacked autoencoders -SAE, deep belief network -DBN and residual neural network -ResNet). The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost, although they all performed poorly in certain conditions such as winter days, rugged terrain, and high elevation. ResNet had the most robust performance across different land cover types, elevations, seasons, and latitude zones, but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency. The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite (GLASS) product.

Ozone, DNA-active UV radiation, and cloud changes for the near-global mean and at high latitudes due to enhanced greenhouse gas concentrations

Abstract. This study analyses the variability and trends of ultraviolet-B (UV-B, wavelength 280–320 nm) radiation that can cause DNA damage. The variability and trends caused by climate change due to enhanced greenhouse gas (GHG) concentrations. The analysis is based on DNA-active irradiance, total ozone, total cloud cover, and surface albedo calculations with the European Centre for Medium-Range Weather Forecasts – Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) chemistry–climate model (CCM) free-running simulations following the RCP 6.0 climate scenario for the period 1960–2100. The model output is evaluated with DNA-active irradiance ground-based measurements, satellite SBUV (v8.7) total-ozone measurements, and satellite MODerate-resolution Imaging Spectroradiometer (MODIS) Terra cloud cover data. The results show that the model reproduces the observed variability and change in total ozone, DNA-active irradiance, and cloud cover for the period 2000–2018 quite well according to the statistical comparisons. Between 50∘ N–50∘ S, the DNA-damaging UV radiation is expected to decrease until 2050 and to increase thereafter, as was shown previously by Eleftheratos et al. (2020). This change is associated with decreases in the model total cloud cover and negative trends in total ozone after about 2050 due to increasing GHGs. The new study confirms the previous work by adding more stations over low latitudes and mid-latitudes (13 instead of 5 stations). In addition, we include estimates from high-latitude stations with long-term measurements of UV irradiance (three stations in the northern high latitudes and four stations in the southern high latitudes greater than 55∘). In contrast to the predictions for 50∘ N–50∘ S, it is shown that DNA-active irradiance will continue to decrease after the year 2050 over high latitudes because of upward ozone trends. At latitudes poleward of 55∘ N, we estimate that DNA-active irradiance will decrease by 8.2 %±3.8 % from 2050 to 2100. Similarly, at latitudes poleward of 55∘ S, DNA-active irradiance will decrease by 4.8 % ± 2.9 % after 2050. The results for the high latitudes refer to the summer period and not to the seasons when ozone depletion occurs, i.e. in late winter and spring. The contributions of ozone, cloud, and albedo trends to the DNA-active irradiance trends are estimated and discussed.

A New Fire Danger Index Developed by Random Forest Analysis of Remote Sensing Derived Fire Sizes

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable’s scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities.

Type dependent role of aerosols in reversing the first indirect effect over the north Indian Ocean

ABSTRACT Present study investigates the role of aerosol types in modulating warm cloud microphysics over the north Indian Ocean using long-term (2006–2016) satellite and re-analysis data. Analysis of well-mixed aerosol and marine warm cloud layers observed from daily co-located Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite over northern Indian Ocean revealed the occurrence of reverse Twomey effect in warm clouds having liquid water path (LWP) less than 75 g m−2. Such clouds are found to occur under a rather stable (Estimated Inversion Strength (EIS) > 2) and dry atmospheric conditions (Free Tropospheric Humidity (FTH) < 40%). Analysis of estimated cloud-top entrainment index (ECTEI) and cloud-top entrainment instability (CTEI) quantitatively suggested that entrainment of dry air can have a significant role in reversing the first indirect effect. Further, investigation on the role of various aerosol types in well mixed clouds revealed that dust aerosols have predominant role in reversing the Twomey effect over the north Indian Ocean.