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Moderate Resolution Imaging Spectroradiometer (MODIS), whose openly public data have been used for over two decades to monitor and address global issues, has 16 thermal emissive bands (TEBs) with central wavelengths that range from 3.7 to 14.4 μm and are calibrated on-orbit using observations from its on-board blackbody. To maintain MODIS' rich, well-calibrated archive of multispectral imagery and data, Earth targets are regularly used to track its long-term stability as well as the consistency between the two sensors onboard the Terra and Aqua satellites. Moreover, these scenes can be used to compare MODIS Earth view data over the complete scan-angle range and evaluate the on-orbit performance of the TEBs response-versus-scan-angle (RVS) over the mission lifetime. This article focuses on evaluating the MODIS TEBs Collection (C6.1) radiometric calibration stability for both instruments using an in situ ocean target as reference [hereafter referred to as in situ sea surface temperature (SST)]. Furthermore, it will assess the calibration consistency between the MODIS sensors. Finally, it will analyze the on-orbit RVS stability for Terra and Aqua MODIS. Only cloud-free, nighttime MODIS TEB retrievals were used for the study. A normalization methodology is applied to standardize the MODIS data to the in situ SST. In addition, spectral corrections were derived between some of the Terra and Aqua MODIS TEBs by using a combination of the MODIS Atmospheric Profile product and MODerate resolution atmospheric TRANsmission (MODTRAN) simulations. Results indicate that most MODIS TEBs exhibit mission-long trends of ±0.50 K--with Terra band 30 presenting the largest downward drift due to residual electronic crosstalk effects. Moreover, the calibration consistency analysis over a warm ocean target demonstrated that the average Terra-to-Aqua MODIS bias for most bands is well within ±0.50 K (bands 27 and 30 show the largest--electronic crosstalk-related--biases). Finally, the MODIS TEBs RVS trends display changes of ±0.50 K (except for bands 25 and 27 at the end-of-scan angles) for both instruments. Overall, the MODIS TEBs remain well-calibrated and their RVSs aptly characterized.

In the context of global warming, forest fires are expected to occur more frequently and intensively, and impose more significant impacts on human society, terrestrial ecosystems, and atmosphere. Most of the existing methods in monitoring large-scale forest fire are based either on satellite visible and infrared observations or weather-based indices. This work explored the advantages of a new satellite microwave-based vegetation index in monitoring forest fire occurrence and fire intensity in Northeastern Asia and Southern China. Specifically, we used satellite observations during 2002–2011 to investigate the correlation at different temporal scales between forest fire properties (fire count, FC; fire radiative power, FRP) and the vegetation water content proxy of the Microwave Emissivity Difference Vegetation Index (EDVI) derived from the Moderate Resolution Imaging Spectroradiometer and the Advanced Microwave Scanning Radiometer-EOS on Aqua satellite. The correlations were compared to that with three weather-based indices including the Fine Fuel Moisture Code, Initial Spread Index and Fire Weather Index (FWI) to determine whether EDVI provides new independent information of forest fires. Finally, EDVI and the weather-based indices FWI were combined to establish multivariate linear regression models to estimate FC and FRP. Results show that: 1) the temporal variations of FC and total FRP are negatively correlated with EDVI using the daily and monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between FC (and total FRP) and EDVI are observed in Northeastern Asia and Southern China; 2) compared to the weather-based indices, EDVI shows higher correlation with the temperate forest fire properties in Southern China while shows weaker correlation with the forest fire properties in Northeastern Asia; and a combination of the two kind of indices is found to improve the explained variance for fire properties in both regions; 3) multivariate linear regression models based on EDVI and FWI provide better estimation of FC and FRP compared to the linear regression models based on FWI alone. To our knowledge, this is the first work that comprehensively investigates the potential application of the microwave-based vegetation water content index in forest fire count and fire intensity.

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat-MODIS image pairs and one publicly available dataset of Sentinel-MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) products at regional and global scales have already been extensively and routinely generated from medium-resolution sensors. However, there is a lack of high-resolution LAI/FPAR product, which is especially essential for crop growth and drought monitoring of cropland in patches. This article proposes a processing framework for the derivation of decameter cropland LAI and FPAR in the Northern China plain from Sentinel-2 surface reflectance data with a random forest (RF) algorithm by exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The training database is generated from the spatially aggregated Sentinel-2 surface reflectance and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR product over homogeneous cropland, and the training samples are strictly filtered for the best quality. RF is then trained over the processed Sentinel-2 surface reflectance and the filtered MODIS LAI/FPAR under two input groups--one group is for Sentinel-2 spectral bands of 10-m resolution only, and the other group supplements the Sentinel-2 red-edge (RE) and shortwave infrared (SWIR) bands of 20-m resolution. Extensive comparisons and validation are carried out, and they demonstrate that the new method can generate spatial and temporal consistent LAI/FPAR with MODIS at high spatial resolution. The retrieval accuracy is slightly better for 20-m input groups than that for 10-m input groups, confirming the value of RE and/or SWIR in cropland LAI/FPAR estimate. This article also demonstrates that GEE is a suitable high-performance processing tool for high-resolution biophysical variables estimation.

The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA's Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of -33.45 °C, -6.35 °C, and -4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study.

Image-based fusion is a state-of-art process to extract vital information by combining the two or more images acquired from different satellite sensors. Recently launched (26th September 2016) ISRO’s (Indian Space Research Organization) Ku-band (13.5 GHz) based Scatterometer Satellite (SCATSAT-1) as an active microwave sensor can offer the day-night, all-weather monitoring services, which are not possible with the optical-based visible and infrared remote sensing satellites. Therefore, the fusion of optical and microwave data offers the cloud-free detection of earth surface transitions and helps in emergency response to natural hazards, security, and defence. The objectives of the proposed framework are (a) nearest-neighbour based fusion (NNF) of ISRO’s SCATSAT-1 and NASA’s (National Aeronautics and Space Administration) moderate resolution imaging spectroradiometer (MODIS) optical data, (b) generation of thematic maps using artificial neural network (ANN) based classification of the fused data, (c) detection of spatiotemporal variations via post-classification comparison (PCC) based change detection, (d) cross-referencing with well-defined fusion methods, i.e. Gram-Schmidt (GS), Brovey Transformation (BT) and Ehlers, and (e) Impact analysis of clouds on the input dataset and fusion methods. This study has been conducted over the Western Himalayas to estimate the snow cover changes under cloudy conditions with two datasets i.e., winter and monsoon. The experimental outcomes confirm the efficacy of the proposed framework in the effective removal of clouds, generation of classified maps, and change maps. The present study includes an exhaustive list of applicative situations for cloud-free monitoring using freely and daily based SCATSAT-1 and MODIS datasets.

Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00–07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R2 = 0.89–0.93 and RMSE = 0.071–0.091 for AOD and R2 = 0.67–0.81 and RMSE = 0.079–0.105 for FMF) and RF (R2 = 0.88–0.92 and RMSE = 0.080–0.095 for AOD and R2 = 0.59–0.76 and RMSE = 0.092–0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i.e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces.

International audience; In this study, the global surface urban heat island (SUHI) for 1711 cities during 2003–2019 was quantified by the dynamic urban-extent (DUE) scheme with the land surface temperature datasets from Moderate Resolution Imaging Spectroradiometer Terra and Aqua through the Google Earth Engine platform. The global pattern and regional contrasts of SUHI intensity (SUHII), and the interannual changing rate of SUHII (δSUHII) were revealed at the annual, summer, and winter scales. Further, the associated driving factors for long-term SUHII were explored from a temporal perspective. The main findings are as follows: (1) Globally, the global mean SUHII over 2003–2019 for annual daytime (1.32 °C) and annual nighttime (1.09 °C) by DUE are generally higher than that by previous simplified urban-extent (SUE) scheme. Accordingly, the summer daytime and nighttime SUHIIs are 1.98 °C, 1.05 ℃, while the winter daytime and nighttime SUHIIs are 0.76 ℃, and 1.10 ℃. (2) The annual, summer, and winter δSUHIIs are 0.11 °C/decade, 0.27 °C/decade, and −0.06 °C/decade, respectively, at daytime, and 0.07 °C/decade, 0.09 °C/decade, and 0.10 °C/decade, respectively, at nighttime. (3) The global SUHII and δSUHII demonstrates evident regional contrast. The warm temperate and snow zones show distinct seasonal variations from summer to winter for daytime SUHII. Specifically, the negative daytime SUHII is detected for the arid zone, which exhibits the highest day-night variation and shows decreasing trend. (4) The global SUHII and δSUHII indicate distinct latitudinal variations, and an additional flip-flop (daytime SUHII < nighttime SUHII) region is detected between 10 °S and 20 °S. (5) The long-term daytime SUHII are negatively regulated by the urban–rural difference on evaporative cooling of vegetation; while at nighttime, it is negatively affected by the urban–rural difference on surface thermophysical properties. It implies the urban greening and surface properties should be specifically concerned to increase the evaporation cooling and reduce the heat retention in SUHII mitigation.

The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of -2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial-temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

The medium-resolution spectral imager (MERSI) onboard China's new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth's environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear- and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination (R²) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m², 0.092, 0.084, and 115.305 W/m², respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

Satellite-derived land surface temperature (LST) is critical for retrieving terrestrial evapotranspiration (ET); however, its availability is limited by low spatial resolution and inclement weather conditions. This study develops a spatiotemporal regression strategy that can downscale 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to 250-m resolution and simultaneously gap-fill the missing values. The proposed methodology synergistically uses random forest (RF) model and geographically weighted regression, which are, respectively, available for demonstrating the nonlinear correlation between LST and explanatory variables and for calibrating the RF-derived residuals. The study is conducted across a region of ~1.49 million square kilometers in northern China. The coupled model creates a 250-m spatial resolution LST product with the root-mean-square error (RMSE) of 2.32 and 1.87 K when compared with field observations and reference Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST, respectively. Meanwhile, it minimizes the constraint of LST availability due to inclement weather conditions with RMSE of 2.69 and 2.31 K relative to field observations and reference images, respectively. The results further reveal that remote-sensing-derived ET using the 250-m downscaled LST data is fairly accurate with the relative errors of 6%-9% as evaluated with flux measurements. The 250-m modeled ET retrievals exhibit a more intense hydrological response to the water use conditions compared with the 1-km remotely sensed ETs and Noah land surface model ETs. This study may benefit land surface hydrology research and water resource management.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov's soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter (b) agree with values published in the literature and are corroborated with the estimated values of b using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

Generating spatially and temporally consistent biophysical products at the global scale from Landsat data for monitoring and assessing surface change dynamics remains a challenge. This paper presents an inversion framework called Land continuous Variable Estimator (LoVE) – Landsat for estimating a group of spatiotemporal continuous land surface variables with daily temporal resolution from Landsat 5, 7, and 8 top-of-atmosphere (TOA) data. LoVE – Landsat adopts a data assimilation approach originally developed for coarse-resolution satellite data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Major improvements to the approach include constructing empirical dynamic equations based on MODIS retrievals and other ancillary information, developing an artificial neural networks (ANN)-based emulator of the coupled radiative transfer (RT) model of atmosphere and land surface (vegetation, soil and snow) as the observation operator, and designing a hybrid four-dimensional variational (4DVar) and ensemble Kalman filter (EnKF) data assimilation algorithm. The approach starts with generating the initial cloud-free regularly-distributed (every 16 days) time series of Landsat data. The 4DVar is then used to assimilate clear-sky snow-free Landsat TOA observations over one year into the empirical dynamic evolution models of the land surface variables (e.g., leaf area index - LAI). The EnKF is then used to further adjust the state vector at the actual Landsat acquisition times. After determining a core set of variables (e.g., LAI), other variables, such as broadband albedo, emissivity and fraction of absorbed photosynthetically active radiation (FAPAR) are calculated by the coupled RT model. Several experimental cases are presented to demonstrate that the proposed LoVE-Landsat framework is effective to estimate daily land surface variables.

Top-of-atmosphere (TOA) albedo is a significant factor of earth energy budget, climate change, and environmental change. As tremendous regional and global changes are happening over ocean, more details are needed to monitor the ocean environment. However, there were still no high-spatial resolution TOA albedo products over ocean. In this study, a new algorithm for clear-sky TOA albedo estimation over ocean was proposed, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Instead of building angular distribution models, direct retrieval models between TOA reflectance and TOA albedo were developed based on extensive radiative transfer (RT) simulations, covering thousands of ocean and atmosphere types. Three-component ocean water albedo model was involved to take account for the ocean surface anisotropy at different wind speed, wind direction, and chlorophyll concentration, while Modtran 5 was utilized to simulate different atmospheric conditions. Our results showed good agreement with the Clouds and the Earth's Radiant Energy System (CERES) based on a global comparison on August 4, 2011, with RMSE = 0.015 and bias = 0.002. And our MODIS-based products provide more spatial details due to higher spatial resolution (1 km), which will be a good data source for regional environmental and climatic research and will also enhance the understanding of Earth's radiation budget.

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is y = 0.895x + 0.056, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD + 0.05). For the AEs, the regression line is y = 0.728x + 0.395, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4-μm channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchant et al.). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38-μm channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.

Rivers are important elements of the Earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to the coarse spatial resolution. In this paper, a cascaded spectral-spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial-spectral information in MODIS imagery.

Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth-atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing-Tianjin-Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error (f $_{=EE}$ ), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and f $_{=EE}$ 's were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.

The crops' biophysical parameters play an important role in balancing the land surface energy fluxes and are needed in crop simulation modeling, evapotranspiration, etc. The vegetation parameters' retrieval using microwave scattering model, mainly affected by the heterogeneous distribution of land targets, hampers an accurate retrieval of soil-vegetation parameters in microwave remote-sensing algorithms. To minimize the errors in biophysical parameters' retrieval, the synergetic approach of modified water cloud model (MWCM) and modified soil scattering model (MSSM) was attempted to retrieve the leaf area index (LAI) of wheat and barley crops. Due to the spatiotemporal resolution of Sentinel-1A synthetic aperture radar (SAR) mission, it could be more sensitive to vegetation condition and the retrieval accuracy than optical/IR satellites. The nonlinear least square optimization algorithms were used for the parameterization of modified scattering model. The lookup table (LUT)-based inversion algorithm was applied to compute the LAI values through the modified scattering models. The statistical analysis was performed to assess the model efficiency. In case of forward modeling, the highest R² = 0.96 and low RMSE = 0.20 dB were computed between the modeled σ⁰ (dB) and SAR-derived σ⁰ (dB) using vegetation descriptor (V) = LAI. On the other hand, for inverse modeling, the LAI values obtained were more accurate at VV polarization (R² = 0.94 and RMSE = 0.124 m²/m²), when compared with the in situ data. The overall product was also compared with the Project for On-Board Autonomy-Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS)-LAI to check the robustness of the approach.

Sea surface nitrate (SSN) is an important parameter to characterize physical and biogeochemical processes, particularly to quantify oceanic new primary production, yet its remote estimation from satellite has been difficult due to the complex relationships between environmental variables and SSN. In the central and southern sections of the California Current System (CSCCS), this challenge is attempted through modeling, validation, and extensive tests in different oceanic scenarios. Specifically, using extensive SSN datasets collected by many cruises spanning 40 years (1978-2018) and Moderate Resolution Imaging Spectroradiometer (MODIS) estimated sea surface temperature (SST) and chlorophyll-a (Chl), a stacking random forest (SRF) model of SSN has been developed and validated with a spatial resolution of ~4 km. The model showed an overall performance of root mean square difference (RMSD) = 0.83 μmol/kg, with coefficient of determination (R²) = 0.87, mean bias = -0.11 μmol/kg, and mean ratio = 1.15 for SSN ranging between 0.05 and 19.90 μmol/kg (N = 1034). Furthermore, tests of the model with its original parameterization for the upwelling period, oceanic period, and winter period all showed satisfactory performance with an overall RMSD of 1.95 μmol/kg. The sensitivity of the SRF model to uncertainties of MODIS SST and Chl was examined, with induced uncertainties of le 2.22 μmol/kg. The extensive evaluation and sensitivity tests indicated the robustness of the SRF model in estimating SSN in the study area of the CSCCS, and it could serve as a robust approach for other regions once sufficient in situ SSN data are available for model calibration.

Mixed pixels are a ubiquitous problem in remote sensing images. Spectral unmixing has been used widely for mixed pixel analysis. However, up to now, most spectral unmixing methods require endmembers and cannot consider fully intraclass spectral variation. The recently proposed spatiotemporal spectral unmixing (STSU) method copes with the aforementioned problems through exploitation of the available temporal information. However, this method requires coarse-to-fine spatial image pairs both before and after the prediction time and is, thus, not suitable for important real-time applications (i.e., where the fine spatial resolution data after the prediction time are unknown). In this article, we proposed a real-time STSU (RSTSU) method for real-time monitoring. RSTSU requires only a single coarse-to-fine spatial resolution image pair before, and temporally closest to, the prediction time, coupled with the coarse image at the prediction time, to extract samples automatically to train a learning model. By fully incorporating the multiscale spatiotemporal information, the RSTSU method inherits the key advantages of STSU; it does not need endmembers and can account for intraclass spectral variation. More importantly, RSTSU is suitable for real-time analysis and, thus, facilitates the timely monitoring of land cover changes. The effectiveness of the method was validated by experiments on four Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. RSTSU utilizes and enriches the theory underpinning the advanced STSU method and enhances greatly the applicability of spectral unmixing for time-series data.

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) images at a high spatial resolution of 500 m is proposed based on a priori bidirectional reflectance distribution function (BRDF) shape parameters database, which is reconstructed via the 3-D discrete cosine transform (DCT-PLS) method. Then, the surface reflectances are calculated from BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing-Tianjin-Hebei of China, and Southeastern United States of America regions, and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965 and root-mean-square error ~0.125; the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales.

Surface albedo is closely related to the Earth's energy budget and is usually estimated by integrating remotely sensed bidirectional reflectance distribution function (BRDF) data based on the widely used Ross-Li kernel-driven models. However, for large solar zenith angles (i.e., SZAs > 70°), albedo estimation using the operational algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS), i.e., RossThick-LiSparseReciprocal (RTLSR), is not recommended because it is reported to somewhat underestimate the black-sky albedo (BSA) at large SZAs based on ground albedo measurements. Recently, various combinations of the Ross-Li BRDF models with improved capabilities have been developed, and the assessments of these models based on worldwide satellite BRDF data with good spatial sampling, particularly at the large view and solar angles, will be important to improve an understanding of their performance in estimating intrinsic albedos. Following previous studies, the objective of this study is to further assess a series of hotspot-corrected Ross-Li models by demonstrating their ability to fit the POLarization and Directionality of the Earth's Reflectances (POLDER) data sets and estimate albedo, especially at large SZAs, based on selected concurrent POLDER and MODIS data. The hotspot-corrected RTLSR model obtained by combining the RossThickChen and LiSparseReciprocalChen kernels (RTLSR_C) shows the best fitting ability, with a high cumulative frequency of small root-mean-square errors (RMSEs), thus confirming previous conclusions. Model differences mainly appear in albedo estimates, especially BSA estimates at large SZAs. The BSAs estimated by other models are significantly different from the RTLSR_C estimates in the near-infrared (NIR) and red bands as the SZA increases to approximately 60° and 70°, respectively. In this case, RossThinChen-LiSparseReciprocalChen (RTNLSR_C) yields higher BSA estimates than those of RTLSR_C. Comparisons of the MODIS and POLDER albedos estimated with Ross-Li models show that models with the RossThinChen kernel yield higher BSA estimates than those of the RTLSR_C model as the SZA increases. The results indicate that the retrieved albedo is likely to be more accurate with appropriately selected kernels for BRDF models at large SZAs, providing guidance for selecting suitable combinations of multiple kernels.

Atmospheric carbon dioxide ( CO2 ) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

In recent decades, most of the natural and semi-natural ecosystems around the world have experienced excessive environmental loads due to human unsustainable activities that threaten their health and sustainability. Accordingly, the idea of ecosystem health has been proposed to monitor simultaneously the structure, function as well as the services that an ecosystem provides. In this research, moderate resolution imaging spectroradiometer 8-day land surface temperature product was used to measure temperature and land cover changes of Lake Urmia Basin for three months of June, July and August in a time period of 2000–2018. Time series data analysis was carried out using linear regression, Fourier series and Mann–Kendall test. Moreover, Moran index was used to analyze changes of the spatial pattern of indicators. To show the influence of Lake Urmia ecosystem services on its overall health, Total Ecosystem Health model was established by introducing normalized difference vegetation index, lake temperature, Moran index and temperature of the basin as indicators of Vigor, Structure, Resilience and Ecosystem Services, respectively. Based on output of the model, 54, 41 and 4% of the regions have obtained weak, moderate and strong ecosystem health, respectively. The results showed that despite the moderating role of Lake Urmia as a temperature regulator for its surrounding areas especially within 10 km, the decline of ecosystem health status in 95% of regions has occurred during the years 2000–2018. Therefore, to benefit from the ecosystem regulating services, the sustainability of other components involved in the total ecosystem health should be also ensured.

Snow cover plays an important role in hydrological modelling and influences the energy balance. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow products are widely used for snow cover monitoring; however, the cloud cover causes a discontinuity in spatial and temporal scale. Therefore, we have implemented a composite (five-step) methodology for cloud removal over the Karakoram and Himalayan (KH) region during 2000���2019. The cloud-gap-filled snow cover area (SCA) was compared with Landsat-8 data, and the relationship with in situ observations (snowfall and temperature) was examined. The annual SCA illustrates an increasing trend for the entire KH except in the eastern Himalayas. The snow cover day and nine snow cover timing indices were assessed to explore the snow cover characteristics. The relationships between SCA and meteorological variables were evaluated, suggesting a higher correlation with temperature and shortwave radiation. A sensitivity analysis was performed, indicating higher sensitivity of SCA to radiation than to other selected variables.

Earthquakes are among the major and destructive natural disasters on Earth. Thus, it is important to investigate whether the occurrence of earthquakes affects the behavior of atmospheric parameters. This study aims to examine the aerosol variations in the atmosphere from a multi-year perspective as the earthquake precursor for the eight Mw > 7.0 earthquakes over different locations on the world. In this study, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) derived Aerosol Optical Depth (AOD) data as an earthquake precursor. In this context, 4-year (the earthquake year and the previous 3 years) epicentral AOD values were evaluated for each earthquake. As an anomaly indicator, ±2σ (95% confidence level) interval was utilized after applying 15-day moving average filter to the AOD time series. The results showed that any significant pre-seismic fluctuation or distinctive signal was not detected for any of the events, except for the 2017 Iraq Mw = 7.3 earthquake. However, identical anomalies are also observed 8–10 times in a year, which are not related to an earthquake. Thus, it can be assumed as a coincidence rather than a precursory signal. As a consequence, according to the scientific methodology (long-term AOD analysis) used in this study, sufficient precursory events could not be associated with the earthquakes to confirm the hypothesis that pre-seismic aerosol anomalies occur in the atmosphere.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.

Biomass burning emits an estimated 25 % of global annual nitrogen oxides (NOx), an important constituent that participates in the oxidative chemistry of the atmosphere. Estimates of NOx emission factors, representing the amount of NOx per mass burned, are primarily based on field or laboratory case studies, but the sporadic and transient nature of wildfires makes it challenging to verify whether these case studies represent the behavior of the global fires that occur on earth. Satellite remote sensing provides a unique view of the earth, allowing for the study of emissions and downwind evolution of NOx from a large number of fires. We describe direct estimates of NOx emissions and lifetimes for fires using an exponentially modified Gaussian analysis of daily TROPOspheric Monitoring Instrument (TROPOMI) retrievals of NO2 tropospheric columns. We update the a priori profile of NO2 with a fine-resolution (0.25∘) global model simulation from NASA's GEOS Composition Forecasting System (GEOS-CF), which largely enhances NO2 columns over fire plumes. We derive representative NOx emission factors for six fuel types globally by linking TROPOMI-derived NOx emissions with observations of fire radiative power from Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite-derived NOx emission factors are largely consistent with those derived from in situ measurements. We observe decreasing NOx lifetime with fire emissions, which we infer is due to the increase in both NOx abundance and hydroxyl radical production. Our findings suggest promise for applying space-based observations to track the emissions and chemical evolution of reactive nitrogen from wildfires.

The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data.

Considering the current booming interest for the large-scale deployment of bifacial photovoltaic modules, the solar industry now requires accurate estimates of broadband surface albedo at high spatial resolution. In this context, the present study evaluates the adequacy and performance over North America of two Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo products (at 500-m and 1-km resolution) and the National Solar Radiation Database (NSRDB) product at 4-km resolution. Two variations of the 500-m MODIS product are also considered: black-sky albedo and all-sky albedo. Albedo observations from 36 radiometric stations during 2011–2015 are analyzed while considering the apparent homogeneity of the surface characteristics over the 4x4 km NSRDB pixel in which they are located. Even at sites where the albedo around the station has been found “homogeneous” in the literature, marked differences are found between the daily observations and the gridded estimates at any spatial resolution. Differences in seasonal behavior and between the three different albedo types also impact the accuracy of the albedo estimates, with overtones caused by local specificities and inhomogeneities. All this precludes the desirable evaluation of the local albedo at a specific site of relatively small size compared to its corresponding 4x4-km pixel if only the mean albedo over that pixel is known. Significant discrepancies are also found at snow-impacted sites, most importantly in the case of the NSRDB albedo estimates, which are typically much too high.

Analysis of the formation and evolution of urban surface thermal environment is crucial for urban planning and improving the environment of a settlement. Qingdao was selected in this study as a typical coastal city undergoing rapid urbanization, and the spatiotemporal dynamic characteristics of its urban surface thermal environment from 2010 to 2019 were evaluated. The random forest (RF) algorithm was adopted to obtain its land surface temperature (LST) map with 30-m resolution by downscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product; the remote sensing indices emphasizing different land cover types, LST calculated by the radiative transfer equation, and elevation were used as input variables in the algorithm. The heat island intensity (HII), urban heat island (UHI) volume, and UHI grade were used to analyze the spatiotemporal dynamic characteristics of the urban surface thermal environment in Qingdao. The results show an increasing trend in average HII between 1.1 and 2.52°C in the study area over the past 10 years. The northern city appeared to have the highest UHI volume, while change of the UHI volume in Huangdao District of southwestern Qingdao was the most significant. The areas with high HII have gradually expanded during the last 10 years, and the areas with a 10-yr persistently high HII are distributed mainly in old urban areas with high building density and a dense population. Different factors may influence UHI, such as artificial heat sources, surface heat sources, and hybrid heat sources. Finally, adjusting the urban structure, increasing the vegetated area, and changing building colors are proposed to mitigate UHI in the areas with continuously high HII.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65 $\mu \text{m}$ ) and near-infrared ( $2.13~\mu \text{m}$ ) bands in surface reflectance estimation and the green band ( $0.55~\mu \text{m}$ ) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and $R^{2}$ of 0.834, which is close to the MODIS EE% of 70.59% and $R^{2}$ of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

This paper presents the first results of a new way of using MODIS (Moderate Resolution Imaging Spectroradiometer) sensor data to visualize phytopigment inconstancy in the near-surface layer of water basins. Other sensors of this class alike, the MODIS spectral resolution is too low to reproduce the minimums of reflectance Rrs caused by phytopigments in water. However, MODIS is remarkable for the presence of a channel at 469 nm combined with channels at 412, 443, 488, 531, 547, and 555 nm. This makes it possible to distinguish the spectral limits of preferential light absorption by chlorophyll a (412–469 nm) and by accessory pigments (469–555 nm). These capabilities were realized thanks to spectral pixel indexation (SPI) of MODIS images of the sea surface. The SPI boils down to the fact that a user determines the presence of pigment minima in spectra of every image pixel, finds the sum of the wavelengths of these minima as a WRM code and assigns it to the image pixel as one of its attributes. WRM code = 100 is assigned to pixels free of the minima. Such indexation makes it possible to examine the inconstancy of phytopigments on the background of aquatic environment variability. Application of SPI approach to MODIS images of the Gulf of Mexico and the Baltic Sea made it possible to reveal new patterns of phytopigment dynamics during HABs events.

Satellite retrievals of cloud droplet effective radius (re) and optical depth (τ) from the Thirteenth Geostationary Operational Environmental Satellite (GOES-13) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra, based on the Clouds and the Earth's Radiant Energy System (CERES) project algorithms, are evaluated with airborne data collected over the midlatitude boundary layer during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne dataset comprises in situ re from the Cloud Droplet Probe (CDP) and remotely sensed re and τ from the airborne Research Scanning Polarimeter (RSP). GOES-13 and MODIS (Aqua and Terra) re values are systematically greater than those from the CDP and RSP by at least 4.8 (GOES-13) and 1.7 µm (MODIS) despite relatively high linear correlation coefficients (r=0.52–0.68). In contrast, the satellite τ underestimates its RSP counterpart by −3.0, with r=0.76–0.77. Overall, MODIS yields better agreement with airborne data than GOES-13, with biases consistent with those reported for subtropical stratocumulus clouds. While the negative bias in satellite τ is mostly due to the retrievals having been collected in highly heterogeneous cloud scenes, the causes for the positive bias in satellite re, especially for GOES-13, are more complex. Although the high viewing zenith angle (∼65∘) and coarser pixel resolution for GOES-13 could explain a re bias of at least 0.7 µm, the higher GOES-13 re bias relative to that from MODIS is likely rooted in other factors. In this regard, a near-monotonic increase was also observed in GOES-13 re up to 1.0 µm with the satellite scattering angle (Θ) over the angular range 116–165∘; that is, re increases toward the backscattering direction. Understanding the variations of re with Θ will require the combined use of theoretical computations along with intercomparisons of satellite retrievals derived from sensors with dissimilar viewing geometry.

Recognizing land use changes (LUC) and evaluating their relationship with producing dust sources are considered effective to manage the environment. Taking Kermanshah Province, Iran as study area, dusty days from 2008 to 2015 were selected and dust sources were identified applying thermal-infrared dust index (TDI), hybrid single-particle lagrangian integrated trajectory (HYSPLIT), false color composite (FCC) and true color composite (TCC) of MODerate resolution Imaging Spectroradiometer (MODIS) images. Afterwards, the land use change map was produced using Landsat images in 2000 and 2015. Then, the distribution and frequency of the sources in each land-use change class and important dust production areas were specified. Eventually, two non-parametric tests including Chisquare and Kruskal-Wallis were applied to examine the relationship between LUC and dust sources. Results indicated that the distribution of dust sources was not identical in the study area, and the sources were mainly generated in the areas where land-use change had occurred. In fact, different classes of LUC have different contributions to dust production, and the highest contribution refers to the deflation in gentle slope areas and lowlands where the rangeland has been converted into agriculture land. The findings from this study are useful to manage and control dust in the identified sources.

The study aimed at using Moderate Resolution Imaging Spectroradiometer (MODIS-Terra) to investigate long-term spatial and temporal aerosol variations over Zambia during 2000–2020. Based on data availability, ground truthing for MODIS-Terra was done over Mongu in western Zambia using Aerosol Robotic Network (AERONET) data for 2001–2009. The correlation coefficient between MODIS-Terra and AERONET is 0.42 and statistically significant at 95% confidence level. The aerosol optical depth at 550nm (AOD550) and Angstrom exponent (AE470-870) annual mean spatial distribution indicates high and moderate to low patterns explaining distinct features of aerosol loadings from different areas over Zambia. Generally, the AOD550 and AE470-870 spatial gradient values increase from southern to northern Zambia explaining fine mode particles mainly produced by local intensive anthropogenic and urban/small and large-scale industrial emission activities in the cities. Seasonal climatology reveals that highest AOD550 is observed during SON, followed by JJA, DJF, with the lowest recorded during MAM. It is also observed from AE470-870 that fine particles are prominent during JJA and SON. The insignificant decreasing (increasing) trend of AOD550 is revealed over Mwinilunga, Misamfu, Mfuwe, Choma, and Kabwe (Mongu). Further results showed that fire hotspot density intensifies over central parts of Africa, including Zambia from June to November. Moreover, the 5-day air mass back trajectory analysis suggests that aerosol sources over Zambia are associated with short- and long-distance ranges from East Africa, Congo Basin, Madagascar, and Atlantic and Indian oceans with diverse transport pathways in different seasons. These findings may help enhance a better understanding of climatic effects and atmospheric aerosol sources in Zambia.

The Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 chlorophyll-a (Chl-a) product is one of the widely used ocean colour products that is often used for water quality monitoring of marine ecosystems. However, this product includes a large amount of missing data due to high surface reflectance and cloudy conditions that inevitably affect its suitability for spatiotemporal analysis of water quality. The objective of this study was to compare four Machine Learning (ML) techniques including K-nearest neighbour (KNN), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Network (ANN) with well-known Data Interpolation Empirical Orthogonal Function (DINEOF) method for spatiotemporal missing imputation of MODIS Chl-a. The Southern Caspian Sea, which has a high Chl-a concentration, was selected as the case study. A cross-validation approach ranging missing data ratio from 0.1 to 0.8 was implemented to investigate the optimal parameters of the models and compare their performance for missing imputation. The results indicated that all ML models, except KNN, outperformed the DINEOF method for missing imputation of MODIS Chl-a. The SVR with the highest accuracy and the lowest variability of errors had the best performance among the five competing models, while the KNN showed the worst performance. The main reason for the better accuracy of the SVR than the other models is its structural risk minimization procedure that leads to the better generalization of the SVR model. The current results showed that the ML techniques used in the current study, the SVR in particular, are able to produce reliable imputations of the MODIS Chl-a missing data and can be a useful tool in water quality monitoring of marine ecosystems.

We derived two observation-based global monthly mean dust aerosol optical depth (DAOD) climatological datasets from 2007 to 2019 with a 2∘ (latitude) × 5∘ (longitude) spatial resolution, one based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the other on Moderate Resolution Imaging Spectroradiometer (MODIS) observations. In addition, the CALIOP climatological dataset also includes dust vertical extinction profiles. Dust is distinguished from non-dust aerosols based on particle shape information (e.g., lidar depolarization ratio) for CALIOP and on dust size and absorption information (e.g., fine-mode fraction, Ångström exponent, and single-scattering albedo) for MODIS, respectively. The two datasets compare reasonably well with the results reported in previous studies and the collocated Aerosol Robotic Network (AERONET) coarse-mode AOD. Based on these two datasets, we carried out a comprehensive comparative study of the spatial and temporal climatology of dust. On a multi-year average basis, the global (60∘ S–60∘ N) annual mean DAOD is 0.032 and 0.067 according to CALIOP and MODIS retrievals, respectively. In most dust-active regions, CALIOP DAOD generally correlates well (correlation coefficient R>0.6) with the MODIS DAOD, although the CALIOP value is significantly smaller. The CALIOP DAOD is 18 %, 34 %, 54 %, and 31 % smaller than MODIS DAOD over the Sahara, the tropical Atlantic Ocean, the Caribbean Sea, and the Arabian Sea, respectively. Applying a regional specific lidar ratio (LR) of 58 sr instead of the 44 sr used in the CALIOP operational retrieval reduces the difference from 18 % to 8 % over the Sahara and from 34 % to 12 % over the tropical Atlantic Ocean. However, over eastern Asia and the northwestern Pacific Ocean (NWP), the two datasets show weak correlation. Despite these discrepancies, CALIOP and MODIS show similar seasonal and interannual variations in regional DAOD. For dust aerosol over the NWP, both CALIOP and MODIS show a declining trend of DAOD at a rate of about 2 % yr−1. This decreasing trend is consistent with the observed declining trend of DAOD in the southern Gobi Desert at a rate of 3 % yr−1 and 5 % yr−1 according to CALIOP and MODIS, respectively. The decreasing trend of DAOD in the southern Gobi Desert is in turn found to be significantly correlated with increasing vegetation and decreasing surface wind speed in the area.

This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes. The Tanzanian coast, rich in biodiversity, is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited. This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), meteorological, and Globeland30 Landsat data sets. Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000–2018 using the Mann-Kendall test and Pearson correlation respectively. The results reveal a significant positive trend in temperature (β > 0, Z = 2.87) and a non-significant trend in precipitation (|Z| < 1.96). A positive relationship between NDVI and precipitation is observed. Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large. Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land. The analysis of land use and land cover in the region reveals an increase in cultivated land, shrubland, grassland, built-up land and bare land, while forests, wetland and water all decreased between 2000 and 2020. The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products (firewood, timber, charcoal). The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources.

The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, weather charts, objectively reanalyzed data, the observational data and station sounding data were analyzed to investigate a sea fog event occurred over the Yellow and East China Seas on March 17, 2014. The sounding profiles, weather situations and the related meteorological factors during the development and dissipation stages of this sea fog event were documented. Weather Research Forecast (WRF) model was applied to simulate this sea fog case. The simulated horizontal atmospheric visibility, cloud water, humidity, and vertical wind profile during the different stages of this fog event were analyzed. During the development stage of this sea fog, a southerly lower-jet with 16–18 ms-1, an inversion layer and a cold center over the Yellow Sea were detected. The relative humidity in the fog area was above 95%. The specific humidity over the East China Sea was higher than that over the Yellow Sea. Southerly was dominated in fog area. However, during the dissipation stage of this sea fog, westerly replaced the southerly and at the lower level, southerly jet disappeared. A dry air area formed over the Shandong Peninsula and moved eastwards. Moreover, the WRF modeling result showed that the simulated atmospheric horizontal visibility and cloud water were approximately consistent with the MODIS satellite imagery. Most of cloud water concentrated below 200–400 m, and the cloud water in the southern part of fog area extended to a higher height than the northern part. While both of air temperature and dew-point temperature were close to sea surface temperature.

Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODerate resolution Imaging Spectroradiometer (MODIS) time series preceding each LFMC sample were extracted as the primary data source for training. The proposed temporal convolutional neural network for LFMC (TempCNN-LFMC) comprises three 1-D convolutional layers that learn the multi-scale temporal dynamics (features) of one-year MODIS time series specific to LFMC estimation. The learned features, together with a few auxiliary variables (e.g., digital elevation model), are then passed to three fully connected layers to extract the non-linear relationships with LFMC. In the primary training and validation scenario, the neural network was trained using samples from 2002 to 2013 and then adopted to estimating the LFMC from 2014 to 2018, achieving an overall root mean square error (RMSE) of 25.57% and a correlation coefficient (R) of 0.74. Good consistency on spatial patterns and temporal trends of accuracy was observed. The trained model achieved a similar RMSE of 25.98%, 25.20% and 25.93% for forest, shrubland, and grassland, respectively, without requiring prior information on the vegetation type.

Fire management is increasingly acknowledged as a necessary tool to maintain diversity in desert environments such as the Great Victoria Desert of Australia, but it needs to be informed by accurate fire history data. We compared and assessed the utility of Landsat-derived and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived burnt area mapping (30 m and 250 m resolution, respectively) for sub-regional, landscape and habitat scale management. We did so by using Sentinel-2-derived, 10 m resolution, burnt area mapping as a reference, to determine the most appropriate product to support land management planning. At the landscape scale, Landsat had significantly lower average omission and commission errors (3.4% and 8.0%, respectively) compared with that of MODIS (42.2% and 19.9%, respectively). At the habitat scale, Landsat burnt area percentage was more accurate, in plots of 500 m × 500 m (root mean square error (RMSE) 0.6% to 8.6%), but offered lower accuracy when estimating partially burnt habitat plots of 120 m × 120 m (RMSE 14.1% to 23.9%). Only Landsat-derived fire scar mapping provided enough detail to produce reliable fire history maps to inform fire management and biodiversity conservation operations at a sub-regional scale, landscape scale and a habitat scale of 500 m by 500 m.

Abrupt changes are referred to as fundamental changes in the mean of a system’s variable or its variability which occur in a short term. Abrupt changes in climatic factors may, especially in arid lands, lead to the transition of plant communities as their response to such disturbing elements. However, such a relationship (between factor and response) which helps develop more sustainable policies for ecosystem management has so far been underresearched. This study, therefore, set out to identify the abrupt changes occurred in plant communities and their relationship with the sudden variations of climatic factors in Sirjan desert and semi-desert region, located in Kerman, Iran, from 2000 to 2019. To this end, first, the meteorological data were collected from the region’s representative station (Sirjan synoptic Station) and the plant communities’ maps were extracted based on field operations. Then, the Moderate Resolution Imaging Spectroradiometer’s (MODIS) monthly data from 2000 to 2019 were used to extract Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) whose maps were limited to the community’s maps under zonal statistic survey, using Arc-GIS 10.4.1 package. Finally, the sequential Mann–Kendall (SMK) test was administered to identify the abrupt changes in climatic factors and those of each plant communities. The abrupt changes in yearly total precipitation (YTP) in 2002, 2008, 2014, and 2018 occurred concurrently with the responses made by plant communities including the Artemisia sieberi-Zygophyllum atriplicoides, Ephedra intermedia-Cousinia multilobe. In this regard, the cumulative effect of the abrupt change of the winter precipitation (WP) factor in 2002 and 2014 and the abrupt increase of the average yearly temperature (YAT) factor in 2018 was highly effective in the intensification of the stimulating role of total yearly precipitation (YTP) in making the plant communities respond as a result. Despite the concurrent responses observed in the vast majority of plant communities, the reason behind some of their responses in 2006, 2011, 2013, and 2016 remained unknown, requiring more investigations in terms of other environmental variables.