Search Results (1,779)

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

Monitoring land cover/use dynamics and wildfire occurrences is very important for land management planning and risk mitigation practices. In this research, moderate-resolution imaging spectroradiometer (MODIS) annual land cover images for the period 2001–2023 are utilized for the twenty administrative regions of Italy. Monthly MODIS burned area images are utilized for the period 2001–2020 to study wildfire occurrences across these regions. In addition, monthly Global Precipitation Measurement images for the period 2001–2020 are employed to estimate correlations between precipitation and burned areas annually and seasonally. Boxplots are produced to show the distributions of each land cover/use type within the regions. The non-parametric Mann–Kendall trend test and Sen’s slope are applied to estimate a linear trend, with statistical significance being evaluated for each land cover/use time series of size 23. Pearson’s correlation method is applied for correlation analysis. It is found that grasslands and woodlands have been declining and increasing in most regions, respectively, most significantly in Abruzzo (−0.88%/year for grasslands and 0.71%/year for grassy woodlands). The most significant and frequent wildfires have been observed in southern Italy, particularly in Basilicata, Apulia, and Sicily, mainly in grasslands. The years 2007 and 2017 experienced severe wildfires in the southern regions, mainly during July and August, due to very hot and dry conditions. Negative Pearson’s correlations are estimated between precipitation and burnt areas, with the most significant one being for Basilicata during the fire season (r = −0.43). Most of the burned areas were mainly within the elevation range of 0–500 m and the lowlands of Apulia. In addition, for the 2001–2020 period, a high positive correlation (r > 0.7) is observed between vegetation and land surface temperature, while significant negative correlations between these variables are observed for Apulia (r ≈ −0.59), Sicily (r ≈ −0.69), and Sardinia (r ≈ −0.74), and positive correlations (r > 0.25) are observed between vegetation and precipitation in these three regions. This study’s findings can guide land managers and policymakers in developing or maintaining a sustainable environment. Full article

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article

Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (D_e) advanced markedly across the entire plateau (0.29 days yr⁻¹, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (D_o) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of D_e variability stems from increased net shortwave radiation (+0.38 Wm⁻² yr⁻¹) and rising temperatures (+0.06 °C yr⁻¹) during the melting season, with net shortwave radiation exerting stronger control (R² = 0.73) than temperature (R² = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C₂H₂, C₂H₆, CH₃OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms. Full article

In protected areas of the Brazilian savannas (Cerrado), Land Surface Phenology (LSP) is influenced by both precipitation and fire, but the nature of these relationships remains unexplored. Here, we assessed the impacts of precipitation and fire on LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) at Emas National Park (ENP). Using TIMESAT, along with the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and 30-m Harmonized Landsat Sentinel (HLS) products, we investigated these effects in both grassland and woodland areas. To evaluate the effects of precipitation, we identified the driest and wettest seasonal cycles between 2002 and 2023 and analyzed the relationships between accumulated rainfall during the rainy season and each of the 13 TIMESAT metrics. To assess the effects of fire, three major events were examined: 1 September 2005 (affecting 45% of the park’s area), 12 August 2010 (90%), and 10 July 2021 (21%). The burned grassland area and the subsequent vegetation recovery following the 2021 event were analyzed in detail using a non-burned control site and LSP metrics extracted from the HLS product, covering both pre- and post-disturbance cycles. The results indicated that the metrics most positively correlated to precipitation were Amplitude (AMP), End of Season (EOS), Large and Small Seasonal Integrals (LSI and SSI), and Rate of Increase at the Beginning of the Season (RIBS). The highest correlation coefficients were found in woodland areas, which were less affected by fire disturbance than grassland areas. Similar trends were observed in the behavior of AMP, EOS, and SSI in response to both precipitation and fire, with fire exerting a stronger influence. By decoupling the fire effects from rainfall influence using the control site, we identified Base Level (BL), SSI, EOS, AMP, and Values at the End and Start of the Season (VES and VSS), as the metrics most sensitive to fire and subsequent vegetation recovery in burned areas. The effects of fire were evident for most metrics, both during the disturbance cycle and in the post-fire cycle. Our study underscores the importance of combining MODIS and HLS time series to understand vegetation phenology in the Cerrado. Full article

The escalation in the population of livestock coupled with inadequate precipitation has caused a reduction in pasture biomass, thereby resulting in diminished grassland carrying capacity (GCC) and pasture degradation. In this research, net primary productivity (NPP) data, sourced from the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets from 1982 to 2020, were initially transformed into aboveground biomass (AGB) estimates. These estimates were subsequently utilized to evaluate and assess the long-term trends of GCC across Mongolia. The MODIS data indicated an upward trend in AGB from 2000 to 2020, whereas the GLASS data reflected a downward trend from 1982 to 2018. Between 1982 and 2020, climatic analysis uncovered robust positive correlations between AGB and precipitation (R > 0.80) and negative correlations with temperature (R < −0.60). These climatic alterations have led to a reduction in AGB, further impairing the regenerative capacity of grasslands. Concurrently, livestock numbers have generally increased since 1982, with a decrease in certain years due to dzud and summer drought, leading to the increase in the GCC. GCC assessment found that 37.5% of grasslands experienced severe overgrazing and 31.9–40.7% was within sustainable limits. Spatially, the eastern region of Mongolia could sustainably support current livestock numbers; the western and southern regions, as well as parts of northern Mongolia, have exhibited moderate to critical levels of grassland utilization. A detailed analysis of GCC dynamics and its climatic impacts would offer scientific support for policymakers in managing grasslands in the Mongolian Plateau. Full article

Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area. Full article

Water use efficiency (WUE), as an important metric for ecosystem resilience, has been identified to play a significant role in the coupling of carbon and water cycles. The farming–pastoral ecotone of Northern China (FPENC), which is highly susceptible to drought due to water scarcity, has long been recognized as an ecologically fragile zone. The ecological restoration projects in China have mitigated land degradation and maintain the sustainability of dryland. However, the process of greening in drylands has the potential to impact water availability. A comprehensive analysis of the WUE in the FPENC can help to understand the carbon absorption and water consumption. Using gross primary production (GPP) and evapotranspiration (ET) data from a MODerate resolution Imaging Spectroradiometer (MODIS), alongside biophysical variables data and land cover information, the spatio-temporal variations in WUE from 2003 to 2022 were examined. Additionally, its driving force and the ecosystem resilience were also revealed. Results indicated that the annual mean of WUE fluctuated between 0.52 and 2.60 gC kgH₂O⁻¹, showing a non-significant decreasing trend across the FPENC. Notably, the annual averaged WUE underwent a significant decline before 2012 (p < 0.05), and then showed a slight increased trend (p = 0.14) during the year afterward (i.e., 2013–2022). In terms of climatic controls, temperature (Temp) and soil volumetric water content (VSWC) dominantly affected WUE from 2003 to 2012; VPD (vapor pressure deficit), VSWC, and Temp showed comprehensive controls from 2013 to 2022. The findings suggest that a wetter atmosphere and increased soil moisture contribute to the decline in WUE. In total, 59.2% of FPENC was shown to be non-resilient, as grassland occupy the majority of the area, located in Mu Us Sandy land and Horqin Sand Land. These results underscore the importance of climatic factors in the regulation WUE over FPENC and highlight the necessity for focused research on WUE responses to climate change, particularly extreme events like droughts, in the future. Full article

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications. Full article

Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (T_a) and daily minimum temperatures (T_min) can provide spatially continuous monitoring. The MOD07 Level-2 and MYD07 Level-2 atmospheric profile product provides air temperature at various altitude levels, facilitating a more direct estimation of T_a and T_min. However, previous validations mainly focused on daytime, with a lack of validation for nighttime. Therefore, this study comprehensively evaluated the MOD07 Level-2 and MYD07 Level-2 derived T_a by 2168 hourly meteorological measurements over 5000 m altitude spanning in China. Furthermore, a detailed evaluation of their capability to estimate T_min was also compared with MOD11 Level-2 and MYD11 Level-2 land surface temperature. Our results show that the highest available pressure method (HAP) estimated that, in instantaneous nighttime T_a, there was severe underestimation especially in high-altitude areas for both MOD07 (r = 0.95, Bias = −0.27 °C, and RMSE = 4.53 °C) and MYD07 data (r = 0.96, Bias = −0.17 °C, and RMSE = 3.73 °C). The adiabatic lapse rate (ALR) correction effectively reduced these errors, achieving optimal accuracy with MYD07 data (r = 0.97, Bias = −0.05 °C, and RMSE = 3.29 °C). However, the underestimation by the HAP method was still insufficient compared to T_min estimation by land surface temperature (LST). The LST method offers improved accuracy (r = 0.98, Bias = −0.16 °C, RMSE = 2.89 °C). In general, MYD-based estimations consistently outperformed MOD-based estimations. However, seasonal and elevational variability was observed in all methods, with errors increasing notably in mountainous areas (RMSE rapidly increases to 5 °C and above when the altitude exceeds 2000 m). These findings can provide practical guidance for selecting appropriate inversion methods according to terrain and season and support the development of more accurate air temperature products for a range of climate- and environmental-related applications. Full article

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km² in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less). Full article