Moderate Resolution Imaging Spectroradiometer Observations Research Articles

Global 3-D distribution of aerosol composition by synergistic use of CALIOP and MODIS observations

Abstract. For the observation of the global three-dimensional distribution of aerosol composition and the evaluation of the shortwave direct radiative effect (SDRE) by aerosols, we developed a retrieval algorithm that uses observation data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua. The CALIOP–MODIS retrieval optimizes the aerosol composition to both the CALIOP and MODIS observations in the daytime. Aerosols were assumed to be composed of four aerosol components: water-soluble (WS), light-absorbing (LA), dust (DS), and sea salt (SS) particles. The outputs of the CALIOP–MODIS retrieval are the vertical profiles of the extinction coefficient (αa), single-scattering albedo (ω0), asymmetry factor (g) of total aerosols (WS+LA+DS+SS), and αa of WS, LA, DS, and SS. Daytime observations of CALIOP and MODIS in 2010 were analyzed by the CALIOP–MODIS retrieval. The global means of the aerosol optical depth (τa) at 532 nm were 0.147±0.148 for total aerosols, 0.072±0.085 for WS, 0.027±0.035 for LA, 0.025±0.054 for DS, and 0.023±0.020 for SS. τa of the CALIOP–MODIS retrieval was between those of the CALIPSO and MODIS standard products and was close to the MODIS standard product. The global means of ω0 and g were 0.940±0.038 and 0.718±0.037; these values are in the range of those reported by previous studies. The horizontal distribution of each aerosol component was reasonable; for example, DS was large in desert regions, and LA was large in the major regions of biomass burning and anthropogenic aerosol emissions. The values of τa, ω0, g, and fine and coarse median radii of the CALIOP–MODIS retrieval were compared with those of the AERONET products. τa at 532 and 1064 nm of the CALIOP–MODIS retrieval agreed well with the AERONET products. The ω0, g, and fine and coarse median radii of the CALIOP–MODIS retrieval were not far from those of the AERONET products, but the variations were large, and the coefficients of determination for linear regression between them were small. In the retrieval results for 2010, the clear-sky SDRE values for total aerosols at the top and bottom of the atmosphere were -4.99±3.42 and -13.10±9.93 W m−2, respectively, and the impact of total aerosols on the heating rate was from 0.0 to 0.5 K d−1. These results are generally similar to those of previous studies, but the SDRE at the bottom of the atmosphere is larger than that reported previously. Consequently, comparison with previous studies showed that the CALIOP–MODIS retrieval results were reasonable with respect to aerosol composition, optical properties, and the SDRE.

Increasing global oceanic wind speed partly counteracted water clarity management effectiveness: A case study of Hainan Island coastal waters

A sustainable coastal “blue economy” is one of the most significant opportunities and challenges in the new era. However, the management and conservation of marine ecosystems must recognize the interdependence in the coupled human and natural systems. In this study, we employed satellite remote sensing to map the spatial and temporal distribution of Secchi disk depth (SDD) in Hainan coastal waters, China for the first time, and quantitatively revealed the impacts of environmental investments on the coastal water environment in the context of global climate change. Based on the moderate resolution imaging spectroradiometer (MODIS) in situ concurrent matchups (N = 123), a simple green band (555 nm)-based quadratic algorithm was first developed to estimate the SDD for the coastal waters of Hainan Island in China (R2 = 0.70, root mean square error (RMSE) = 1.74 m). The long time-series SDD dataset (2001–2021) for Hainan coastal waters was reconstructed from MODIS observations. Spatially, SDD showed a pattern of high water clarity in eastern and southern coastal waters and low water clarity in the western and northern coastal areas. This pattern is attributed to unbalanced distributions of bathymetry and pollution from seagoing rivers. Seasonally, the humid tropical monsoon climate drove the SDD into a general pattern of high in the wet season and low in the dry season. Annually, the SDD in Hainan coastal waters improved significantly (p < 0.1), benefiting from environmental investments over the last 20 years. However, the increasing global oceanic wind speed in recent years has exacerbated sediment resuspension and deep ocean mixing, counteracting approximately 14.14% of the remedial management's effectiveness in protecting and restoring the coastal ecosystem. This study offers ways to improve the ecological and environmental regulations under global changes and to strengthen the public service capacity for aquatic management authorities with methods that support the sustainable development of coastal areas.

Frailty Risk in Older Adults Associated With Long-Term Exposure to Ambient PM2.5 in 6 Middle-Income Countries.

BackgroundA series of studies have explored the health effects of long-term exposure to ambient PM2.5 among older adults. However, few studies have investigated the adverse effect of long-term exposure to ambient PM2.5 on frailty, and the results are inconclusive. This study sought to investigate the associations between long-term exposure to ambient PM2.5 and frailty in 6 low- and middle-income countries.MethodsWe included an analytical sample of 34 138 individuals aged 50 and older from the Study on global AGEing and adult health Wave 1 (2007/2010). Air pollution estimates were generated using a standard methodology derived from Moderate Resolution Imaging Spectroradiometer observations and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model. A 3-level hierarchical logistic model was used to evaluate the association between frailty index and long-term PM2.5 exposure at 3 levels (individual, province, and country).ResultsIn rural areas, each 10 μg/m3 increase in ambient PM2.5 was associated with a 30% increase in the odds of frailty (OR = 1.30, 95% CI: 1.21–1.39) after adjusting for various potential confounding factors. The gender-stratified analysis showed that the association seemed to be slightly stronger in men (OR = 1.31, 95% CI: 1.18–1.46) than in women (OR = 1.21, 95% CI: 1.07–1.36) in rural areas.ConclusionIn a large sample of community-based older adults from 6 middle-income countries, we found evidence that long-term PM2.5 exposure was associated with frailty in rural areas.

Estimating the Clear-Sky Longwave Downward Radiation in the Arctic from FengYun-3D MERSI-2 Data

Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W/m2 and 4.36 W/m2, respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W/m2 and −0.14 W/m2, respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W/m2, which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

ABSTRACT Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P<0.001) with that obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data – the average correlation coefficient was 0.90 and the average RMSE was 7.87 days. The minimum RMSE was 4.39 days for CIF. The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations. The PMW LIP dataset provides the basic freeze–thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.

Impact of lockdown and crop stubble burning on air quality of India: a case study from wheat-growing region.

The emergence of COVID-19 has brought the entire planet to a halt. Many countries, including India, were compelled to shut down most urban, industrial, social and other activities as a result of the pandemic. Due to a series of complete lockdowns imposed in India from March 24 to May 17, 2020, and state-wise local level restrictions afterward, have resulted in significant reduction of emissions of numerous atmospheric pollutants. The objective of this study is to analyse the change in concentration of various pollutants such as nitrogen oxide (NO2), carbon monoxide (CO) and aerosol optical depth (AOD) due to lockdown and also to quantify the contribution of crop stubble burning to air pollution. The Sentinel-5P based NO2 and CO observations for 2019 and 2020 and Moderate Resolution Imaging Spectroradiometer (MODIS)-based AOD observations for 2016–2020 were used for detecting the variations. The obtained results showed a significant decrease in NO2 levels during various stages of lockdown. Small decrease in CO levels was observed across most part of the India. With a few exceptions, such as coastal and desert regions, there was a moderate decrease in AOD levels. Furthermore, to study the contribution of NO2, CO and AOD from crop stubble burning, MODIS observations on active fire events were obtained from Visible Infrared Imaging Radiometer Suite (VIIRS). The burning of crop stubble increased NO2 emissions by 22 to 80%. CO levels, on the other hand, have risen by 7 to 25%. A considerable variation in AOD was reported, ranging from 1 to 426%.

A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States

AbstractThis study compares the US National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow‐covered area fraction (SCAF) at snow telemetry (SNOTEL) sites across the Western United States. SWE was obtained from SNOTEL sites, while SCAF was obtained from moderate resolution imaging spectroradiometer (MODIS) observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modelled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under‐predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under‐modelling of SWE. There was also under‐modelling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modelled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modelled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.

Observed HIRS and Aqua MODIS Thermal Infrared Moisture Determinations in the 2000s

This paper compares the tropospheric moisture data records derived from High-resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements from the years 2003 through 2013. Total Precipitable Water Vapor (TPW) and Upper Tropospheric Precipitable Water Vapor (UTPW) are derived using the infrared spectral bands in the CO2 and H2O absorption bands as well as in the atmospheric windows. Retrieval of TPW and UTPW uses a statistical regression algorithm performed using clear sky radiances (and Brightness Temperatures) measured over land and ocean for both day and night. The TPW and UTPW seasonal cycles of HIRS and MODIS observations are found to be in synchronization with zonal mean values for one degree latitude bands within 2.0 mm and 0.07 mm, respectively.

An Improved Method for Soil Moisture Monitoring With Ensemble Learning Methods Over the Tibetan Plateau

Soil moisture (SM) is a key parameter of the hydrological process, which affects exchanges of water and heat at the land/atmosphere interface. The “trapezoid” (or “triangle”) method has been widely applied to SM monitoring based on the pixel distribution within the thermal and optical remote sensing observations. However, the trapezoid method is a linear empirical model highly related to the retrieval accuracy of the surface temperature. In the article, the moderate-resolution imaging spectroradiometer (MODIS) data were applied to retrieve SM through an improved method over the Tibetan Plateau. The improved method is integrated with the “trapezoid” model and multiple learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Meanwhile, RF and XGBoost were both trained with SM target data (the scale of SM and soil temperature) derived from the Tibetan Plateau observations, and the input variables were derived from MODIS observations. Compared with the SM measured, the results showed the root mean square error, the mean absolute error, and the correlation coefficient of the ensemble retrievals were 0.046–0.081 m3m−3, 0.030–0.065 m3m−3, and 0.60–0.87, respectively, which is better than that of the separate model. The ideas to implement the combination of traditional inversion algorithms and machine learning methods are helpful for researches in remote sensing fields.

Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape

Satellite image fusion methods that improve spatial and temporal resolution have significant potential to advance understanding of ecosystem dynamics in space and time. However, systematic evaluations of image fusion methods against in situ spectral data are lacking. Here, we used a suite of in situ spectral data collected at 60 elementary sampling units (10 × 10 m) covering 15 Landsat pixel (30 × 30 m) plots and one Moderate Resolution Imaging Spectroradiometer (MODIS) pixel (250 × 250 m) throughout the entire growing season in a heterogeneous rice paddy landscape to evaluate four state-of-the-art image fusion NDVI products. They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. All fusion products showed strong linear relationships against in situ data when combining all spatial and temporal observations (R2: 0.73 to 0.93) although there were partly negative biases (–1% to –9%). These biases resulted from forcing data to image fusion algorithms, such as Landsat (–4%) and MODIS (–7%). Performance difference between fusion methods were considerably larger for spatial than for temporal variation. Furthermore, Landsat NDVI explained only 17–22% of spatial variation against in situ spectral data, which can be translated into weak performance of image fusion products to predict spatial variability in NDVI. Image fusion products that relied on spatial interpolation showed large biases (–15% to –30%) for a vegetation plot surrounded by mixed land cover plots. Our results highlight key sources of uncertainty and will be instrumental in improving satellite image fusion methods to monitor land surface phenology in space and time.

Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica

Abstract. The surface reflection of solar radiation comprises an important boundary condition for solar radiative transfer simulations. In polar regions above snow surfaces, the surface reflection is particularly anisotropic due to low Sun elevations and the highly anisotropic scattering phase function of the snow crystals. The characterization of this surface reflection anisotropy is essential for satellite remote sensing over both the Arctic and Antarctica. To quantify the angular snow reflection properties, the hemispherical-directional reflectance factor (HDRF) of snow surfaces was derived from airborne measurements in Antarctica during austral summer in 2013/14. For this purpose, a digital 180∘ fish-eye camera (green channel, 490–585 nm wavelength band) was used. The HDRF was measured for different surface roughness conditions, optical-equivalent snow grain sizes, and solar zenith angles. The airborne observations covered an area of around 1000 km × 1000 km in the vicinity of Kohnen Station (75∘0′ S, 0∘4′ E) at the outer part of the East Antarctic Plateau. The observations include regions with higher (coastal areas) and lower (inner Antarctica) precipitation amounts and frequencies. The digital camera provided upward, angular-dependent radiance measurements from the lower hemisphere. The comparison of the measured HDRF derived for smooth and rough snow surfaces (sastrugi) showed significant differences, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven bidirectional reflectance distribution function (BRDF) model, the measured HDRF of snow surfaces was parameterized as a function of solar zenith angle, surface roughness, and optical-equivalent snow grain size. This allows a direct comparison of the HDRF measurements with the BRDF derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product MCD43. For the analyzed cases, MODIS observations (545–565 nm wavelength band) generally underestimated the anisotropy of the surface reflection. The largest deviations were found for the volumetric model weight fvol (average underestimation by a factor of 10). These deviations are likely linked to short-term changes in snow properties.

Crop residue burning practices across north India inferred from household survey data: Bridging gaps in satellite observations

Abstract In north India, agricultural burning adversely affects local and regional air quality during the post-monsoon season (October to November), when the prevailing meteorology is favorable for smog and haze formation. Quantifying the contribution of smoke to air pollution in this region, however, is challenging. While the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard NASA's Terra and Aqua satellites, provides a nearly 20-year record of global fire activity, the sensor cannot adequately capture small, short-lasting agricultural fires due to its moderate spatial resolution (500 m–1 km) and limited overpasses (twice daily for each satellite), as well as the hazy conditions that typically obscure the north India land surface at this time of year. Moreover, current global fire emissions inventories based on MODIS observations can differ by up to an order of magnitude in this region. Here we incorporate household survey data to bridge gaps in MODIS observations and improve estimates of fire emissions over the states of Punjab, Haryana, Uttar Pradesh, and Bihar during the 2003–2018 post-monsoon burning seasons. We develop a novel method that adjusts MODIS Fire Radiative Power (FRP) for: (1) small fires detected by the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375-m spatial resolution, (2) cloud/haze gaps in satellite observations, (3) partial-field burning practices, and (4) the diurnal cycle of fire activity. Adjusting FRP for the fire diurnal cycle yields the largest boost to emissions due to the short lifetime of the fires (~1/2 h) and the brief windows of satellite detection. Using adjusted FRP, we estimate on average 10.4 ± 3 Tg dry matter (DM) burned each year, yielding emissions of 65 ± 18 Gg organic carbon (OC), 5.6 ± 1.6 Gg black carbon (BC), 791 ± 225 Gg CO, and 14.9 ± 4.2 Tg CO2. On average, our OC + BC emissions are 3.4 times (min: 0.6, max: 6.6) the estimates from five widely used global fire emissions inventories. Our estimate for Punjab, which contributes more than two-thirds of emissions in the region, is consistent with our bottom-up validation, which uses burn rates from the household survey and government crop production statistics in 2016 and 2017. We spatially disaggregate the state-level emissions to construct a gridded inventory at daily, 0.25 ° × 0.25 ° resolution over north India from 2003 to 2018. The inventory, SAGE-IGP (Survey Constraints on FRP-based Agricultural Fire Emissions in the Indo-Gangetic Plain), improves modeling assessments of air quality impacts from agricultural burning, thus supporting effective policy development.

MODIS and VIIRS Calibration and Characterization in Support of Producing Long-Term High-Quality Data Products

Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully operated since their launches in 1999 and 2002, respectively, and generated various data products to support the Earth remote sensing disciplines and users worldwide for their research activities and applications, including studies of the Earth system, and its changes over time and geographic regions. The MODIS data have also significantly contributed to the continuity of multi-decadal satellite data records and led to major advances in the Earth remote sensing field. The long-term data records from MODIS observations have been and will continue to be extended by the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, currently operated aboard the Suomi-National Polar-Orbiting Partnership (NPP) and NOAA-20 satellites. The data quality of satellite instruments strongly depends on their calibration accuracy and stability. In order to help scientists and users gain a better understanding of MODIS and VIIRS data quality, this paper provides an overview of their on-orbit calibration methodologies, approaches, and results derived from instrument on-board calibrators and lunar observations, as well as select Earth view targets. What is also discussed is the calibration consistency between MODIS and VIIRS and its potential impact on producing multi-sensor long-term data records. As illustrated, the overall performance of both MODIS and VIIRS continues to meet their design requirements.

An improved dust identification index (IDII) based on MODIS observation

ABSTRACT Satellite remote sensing may serve as an ideal technique to detect dust storms for high temporal and spatial scales. In this paper, we propose an improved dust identification index (IDII) based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The IDII algorithm was used to monitor 129 dust storm events over the West Asia region from 2016 to 2018. Ground-based observations of synoptic stations, RGB images, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Ozone Monitoring Instrument (OMI) were implemented for validating IDII algorithm. In addition, the performance of the proposed algorithm was compared with the Global Dust Detection Index (GDDI). The results show that the accuracy (Ac), Probability Of Correct positive Detection (POCD), and Probability Of False-positive Detection (POFD) for the IDII and GDDI are 82%, 85%, 33%, and 71%, 74%, 27%, respectively. Also over the water as a challenging object, it is clearly seen that the IDII performs much better than GDDI method. Our results suggest that the proposed approach can deal with the common limitations of dust detection algorithms, i.e. dust detection over different surfaces (land and water), seasonal changes, and similarity between dust and other objects like clouds.

Interannual variability of the Chlorophyll-a concentration over Sri Lankan Dome in the Bay of Bengal

ABSTRACT This study investigates the interannual variability of the surface and subsurface Chlorophyll-a Concentration (CC) over the Sri Lankan Dome (SLD) in the Bay of Bengal (BoB) and its possible linkages to local wind-induced upwelling and advection-driven CC transport via southwest monsoon current (SMC) from surrounding waters. A regional ocean modelling system (ROMS) coupled with a nitrogen-based nutrient, phytoplankton, zooplankton, and detritus (NPZD) ecosystem model has been used to simulate the three-dimensional SMC and the upwelling system over the SLD region (5–10N, 82–87E) during 2003–2016. A deep chlorophyll maximum (DCM: 0.8–1.8 mg m−3) is observed at the intermediate depths of 10 to 55 m. The model-simulated CC in the SLD region shows a seasonal variation with maximum value during June through September (JJAS), compares well with Bio-Argo observations available nearby, and significantly correlates (r = 0.78) with satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The subsurface chlorophyll distribution demonstrated strong interannual variability, especially during JJAS and is associated with concurrent upwelling. The Ekman Pumping correlates well with the thermocline depth (r = −0.70) and with the deep chlorophyll maximum (r = −0.74). The simulated zonal transport of chlorophyll across 80E is moderately correlated with the surface chlorophyll on SLD from MODIS observation (0.69) and from the biophysical simulation (0.79), which signifies the role of SMC intrusion from the Arabian Sea to the southwestern BoB. A data-model composite analysis showed that a favourable wind stress curl and shallower thermocline enhanced the Chlorophyll concentration in the SLD region in the high-chlorophyll years (2004, 2014, and 2016). In contrast, reduced advection from the Arabian Sea, deeper thermocline, and the weakening of the deep chlorophyll maxima are responsible for lowering the chlorophyll concentration during the low-chlorophyll years (2008, 2009, and 2011).

Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions

Abstract. The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.

Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations

The Landsat program, since its commencement in 1972, has acquired millions of images of our planet. Those images are one of the most valuable Earth Observation resources for local, regional and global land surface monitoring and study due to their moderate spatial resolution and rich spectral information. However, their applications are impeded largely by their relatively low revisit frequency and cloud contamination on images. In order to improve their usability, a number of studies have been conducted to blend Landsat images with Moderate Resolution Imaging Spectroradiometer (MODIS) images to take merits of the two sensors. All blending models reported that they can predict synthetic Landsat images with various degrees of accuracy. However, only a couple of models reported that they can explicitly estimate uncertainty for their blended images.In this study, we propose a new surface reflectance blending model based on a Kalman Filter algorithm (Kalman Filter Reflectance Fusion Model - KFRFM) to predict time-series synthetic Landsat images from Landsat and MODIS images, and simultaneously to estimate uncertainty of the predicted synthetic images to quantify the quality of the synthetic images. Using the model, we predicted a time-series of 38 synthetic Landsat images with a temporal interval of 4 days for a vegetation growing season spanning about 6 months from Nadir Bi-directional Reflectance Distribution Function adjusted MODIS product (MCD43A4), and their corresponding uncertainties. From this time-series, we calculated five vegetation indices involving all the spectral bands of the synthetic images, and compared them to those from Landsat observations. The results demonstrated that the proposed method is able to produce high quality synthetic Landsat images to meet various application demands for higher spatial and temporal resolution images. Uncertainty analysis reveals that cropland has the largest uncertainty followed by grassland while forests have the smallest uncertainties among the seven vegetation land cover types of the study area. For performance evaluation, we compared KFRFM to several published models. The comparison results reveal that KFRFM performs the best based on the assessed image quality indices.

A Comprehensive Evaluation of 4-Parameter Diurnal Temperature Cycle Models with In Situ and MODIS LST over Alpine Meadows in the Tibetan Plateau

Diurnal variation of land surface temperature (LST) is essential for land surface energy and water balance at regional or global scale. Diurnal temperature cycle (DTC) model with least parameters and high accuracy is the key issue in estimating the spatial–temporal variation of DTC. The alpine meadow is the main land cover in the Tibetan Plateau (TP). However, few studies have been reported on the performance of different DTC models over alpine meadows in the TP. Four semi-empirical types of DTC models were used to generate nine 4-parameter (4-para) models by fixing some of free parameters. The performance of the nine 4-para DTC models were evaluated with four in situ and MODIS observations. All models except GOT09-dT-ts (dT means the temperature residual between T0 and T (t→∞); ts means the time when free attenuation begins) had higher correlation with in situ data (R2 &gt; 0.9), while the INA08-ts model performed best with NSE of 0.99 and RMSE of 2.04 K at all sites. The GOT09-ts-τ (τ is the total optical thickness), VAN06-ts-ω1 (ω1 means the half-width of the cosine term in the morning), and GOT01-ts models had better performance, followed by GOT09-dT-τ, GOT01-dT, and VAN06-ts-ω2 (ω2 means the half-width of the cosine term in the afternoon) models. All models had higher accuracy in summer than in other seasons, while poorer performance was produced in winter. The INA08-ts model showed best performance among all seasons. Models with fixing ts could produce higher accuracy results than that with fixing dT. The comparison of INA08-ts model driven by in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated that the simulation accuracy mainly depended on the accuracy of MODIS LST. The daily maximum temperature generated by the nine models had high accuracy when compared with in situ data. The sensitivity analysis indicated that the INA08-dT and GOT09-dT-ts models were more sensitive to parameter dT, while all models were insensitive to parameter ts, and all models had weak relationship with parameters ω and τ. This study provides a reference for exploring suitable DTC model in the TP.

Observed HIRS and MODIS High-Cloud Frequencies in the 2000s

AbstractThis paper compares the cloud parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements from the years 2003 through 2013. Cloud-top pressure (CTP) and effective emissivity (εf; cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the CO2 absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. The high-cloud (HiCld; with CTP less than 440 hPa) seasonal cycles of HIRS and MODIS observations are found to be in sync, but the HIRS frequency of detection is about 10% higher than that of MODIS (which is attributed to a lower threshold for cloud detection in the HIRS CO2 bands). Differences are largest during nighttime and at the beginning of the time series (2003–06). Both show Northern Hemisphere (NH) and Southern Hemisphere (SH) seasonal HiClds are out of phase and both agree within 2% on NH–SH HiCld differences. During the summer, maximum HiCld frequency averages 5% more in the NH.

Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm

The angular variation of land surface emissivity (LSE) is rarely considered in the split-window algorithm for retrieving land surface temperature (LST), and this can cause large uncertainties in LST retrievals. To analyze the influence of angular LSE variation on LST retrievals, we built a look-up table (LUT) of directional emissivities from the MYD21A1 LST/LSE product in the Moderate Resolution Imaging Spectroradiometer (MODIS) split-window channels. The extracted directional emissivities were then input into the MODIS generalized split-window (GSW) algorithm to substitute for the classification-based emissivities. A simulation analysis was first conducted based on the LUT. Furthermore, the LST retrievals estimated from MODIS observations using the directional emissivities were compared with those estimated using the classification-based emissivities. In-situ measurements from the US SURFRAD and China’s HiWATER networks were used to evaluate LST retrievals obtained using the two different emissivities. The results showed that angular LSE variations in the split-window channels for vegetated surfaces were generally minor during the daytime, but more pronounced during the night-time (approximately 0.005 between nadir and 60°). For barren surfaces, the angular LSE variation in the ˜12 μm channel was negligible but reached approximately 0.01 in the ˜11 μm channel. In the simulation, the influence of angular LSE variation was minor for view-zenith angles (VZA) <40°, but pronounced for VZA >40° reaching approximately 1.0 and 0.7 K at VZA 65° for barren and vegetated surfaces, respectively. In the evaluation, the LST estimated using the directional emissivities showed a higher accuracy than those estimated using the classification-based emissivities, especially over barren surfaces where the improvement reached >1 K. We conclude that angular LSE variation cannot be ignored in LST estimation using the GSW algorithm when VZA is >40°, especially over barren surfaces. The accuracy of the GSW algorithm is improved pronouncedly by using the directional emissivities extracted from the MYD21 product.