Cloud Contamination Research Articles

A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images

Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data.

A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data

Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%–52.7% lower than those of RSDAST, 35.4%–38.7% lower than those of IMA, and 38.5%–46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.

Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance.

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

Mass, morphing, metallicities: the evolution of infalling high velocity clouds

ABSTRACT We revisit the reliability of metallicity estimates of high velocity clouds with the help of hydrodynamical simulations. We quantify the effect of accretion and viewing angle on metallicity estimates derived from absorption lines. Model parameters are chosen to provide strong lower limits on cloud contamination by ambient gas. Consistent with previous results, a cloud traveling through a stratified halo is contaminated by ambient material to the point that <10 per cent of its mass in neutral hydrogen consists of original cloud material. Contamination progresses nearly linearly with time, and it increases from head to tail. Therefore, metallicity estimates will depend on the evolutionary state of the cloud, and on position. While metallicities change with time by more than a factor of 10, well beyond observational uncertainties, most lines-of-sight range only within those uncertainties at any given time over all positions. Metallicity estimates vary with the cloud’s inclination angle within observational uncertainties. The cloud survives the infall through the halo because ambient gas continuously condenses and cools in the cloud’s wake and thus appears in the neutral phase. Therefore, the cloud observed at any fixed time is not a well-defined structure across time, since material gets constantly replaced. The thermal phases of the cloud are largely determined by the ambient pressure. Internal cloud dynamics evolve from drag gradients caused by shear instabilities, to complex patterns due to ram-pressure shielding, leading to a peloton effect, in which initially lagging gas can catch up to and even overtake the head of the cloud.

Surface urban heat island detected by all-weather satellite land surface temperature

Since the existing satellite thermal infrared (TIR) land surface temperature (LST) is susceptible to cloud contamination and other factors, surface urban heat island (SUHI) studies based on TIR LST are limited to clear-sky conditions and are not representative of SUHI under all-weather conditions, which result in a possible clear-sky bias for SUHI. This study introduces a newly released 1-km all-weather LST product (TRIMS LST), which is spatiotemporally seamless, to investigate the real SUHI under all-weather conditions for five megacities (i.e. Harbin, Beijing, Shanghai, Guangzhou, and Chengdu) in China. Firstly, this study compares TRIMS SUHI with MODIS SUHI under clear-sky, partial-cloudy, and cloudy conditions. Secondly, the extent of the influence of cloudiness on SUHI is quantified. Finally, the monthly TRIMS SUHI is used to analyze the clear-sky bias that is caused by using only clear-sky data for the SUHI. Results indicate that (i) the absence of pixel data leads to negative offsets in the SUHI intensities calculated by MODIS LST, and these offsets expand gradually with increases in the number of missing-pixel data, causing the maximum offset to reach −1.83 °C under cloudy conditions in Chengdu; (ii) cloud can mitigate the SUHI for most cities: when the cloud coverage in Guangzhou reaches 90–100%, the daytime SUHI intensity decreases from 2.66 °C for clear-sky conditions to 1.70 °C; the mitigating effect differs at daytime and nighttime; and (iii) clear-sky bias varies significantly across climate zones and seasons, with a varying range of −1.6–1.2 °C for the five selected cities.

Benefits of satellite XCO2 and newly proposed atmospheric CO2 observation network over India in constraining regional CO2 fluxes

Top-down modeling estimates are among the most reliable information available on the CO2 fluxes of the earth system. The inadequate coverage of CO2 observing stations over the tropical regions adds a limitation to this estimate, especially when the satellite XCO2 is strictly screened for cloud contamination, aerosol, dust, etc. In this study, we investigated the potential benefit of a global ground-based observing station network, 17 newly proposed stations over India, and global satellite XCO2 in reducing the uncertainty of terrestrial biospheric fluxes of Tropical Asia-Eurasia in TransCom cyclo-stationary inversion. The data from selected 80 global ground-based CO2 observation stations, together with two additional stations from India (i.e., Cape Rama and Sinhagad) and satellite XCO2, helps to reduce the temperate Eurasian terrestrial flux uncertainty by 23.8%, 26.4%, and 36.2%, respectively. This further improved to 54.7% by adding the newly proposed stations over India into the inversion. By separating the Indian sub-continent from temperate Eurasia (as inspired by the heterogeneity in the terrestrial ecosystems, prevailing meteorological conditions, and the orography of this vast region), the inversion evinces the capacity of existing CO2 observations to reduce the Indian terrestrial flux uncertainty by 20.5%. The largest benefit (70% reduction of annual mean uncertainty) for estimating Indian terrestrial fluxes could be achieved by combining these global observations with data from the newly proposed stations over India. The existing two stations from India suggest Temperate Eurasia as a mild source of CO2 (0.33 ± 0.57 Pg C yr−1), albeit with prominent anthropogenic influences visible in these two stations during the dry seasons. This implies that the proposed new stations should be cautiously placed to avoid such effects. The study also finds that the newly proposed stations over India also have an impact in constraining nearby oceanic CO2 fluxes.

Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

Multi‐Year Seasonal Trends in Sea Ice, Chlorophyll Concentration, and Marine Aerosol Optical Depth in the Bellingshausen Sea

AbstractThis study presents seasonal trends in marine tropospheric aerosol arising from the interplay between physical and biological processes in the Western Antarctic Peninsula (WAP). Remote sensing‐based studies focused on aerosol distribution and links to chlorophyll‐a concentration and sea ice predominantly use passive sensor retrievals of aerosol optical properties. However, these data are subject to cloud presence, signal uncertainties, and lack of retrievals in high latitude wintertime. To address these concerns, we have developed a method of quantifying tropospheric marine aerosol with the NASA Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), which we term marine aerosol optical depth (MAOD). MAOD may be retrieved in nighttime conditions of high‐latitude winter and is devoid of cloud contamination, thereby advancing upon prior aerosol optical depth (AOD) measurements linked with biogenic aerosol. To examine trends in tropospheric marine aerosol, we undertook a multi‐year remote sensing analysis in the Bellingshausen Sea from 2006 to 2018. A seasonal increase in MAOD was observed alongside a seasonal increase in chl‐a and sea ice melt, suggesting a biogenic component to aerosol presence. To the best of our knowledge, this study is also the first to further distinguish a late winter to early spring temporal MAOD signal, likely tied to an aerosol source from either venting of biogenic aerosol from breaks in sea ice or to sea spray/salt aerosol resulting from pulses in wind speed. Our work extends upon previous findings of AOD in polar environments and now more fully characterizes interactions during polar winter.

Mass, morphing, metallicities: The evolution of infalling high velocity clouds

We revisit the reliability of metallicity estimates of high velocity clouds with the help of hydrodynamical simulations. We quantify the effect of accretion and viewing angle on metallicity estimates derived from absorption lines. Model parameters are chosen to provide strong lower limits on cloud contamination by ambient gas. Consistent with previous results, a cloud traveling through a stratified halo is contaminated by ambient material to the point that <10% of its mass in neutral hydrogen consists of original cloud material. Contamination progresses nearly linearly with time, and it increases from head to tail. Therefore, metallicity estimates will depend on the evolutionary state of the cloud, and on position. While metallicities change with time by more than a factor of 10, well beyond observational uncertainties, most lines-of-sight range only within those uncertainties at any given time over all positions. Metallicity estimates vary with the cloud's inclination angle within observational uncertainties. The cloud survives the infall through the halo because ambient gas continuously condenses and cools in the cloud's wake and thus appears in the neutral phase. Therefore, the cloud observed at any fixed time is not a well-defined structure across time, since material gets constantly replaced. The thermal phases of the cloud are largely determined by the ambient pressure. Internal cloud dynamics evolve from drag gradients caused by shear instabilities, to complex patterns due to ram-pressure shielding, leading to a peloton effect, in which initially lagging gas can catch up to and even overtake the head of the cloud.

Forest classification using synthetic GF-1/WFV time series and phenological parameters

High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.

The Methodology and Results from Ground Validation of Satellite Observations at Gas Flaring Sites in Nigeria

This study examines the need/importance for ground validation of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) observation at 2 gas flaring sites in Rivers State of Niger Delta, Nigeria. 12 Landsat imageries (3 Landsat 5 TM and 9 Landsat 7 ETM+) acquired from 25/03/1987 to 08/03/2013 with &amp;lt; 5 % cloud contamination were used. Both sites are located within a single Landsat scene (Path 188, Row 057). Fieldwork measurements and observations at both sites took place from (04/08/2012-21/09/2012) and (05/08/2019-22/09/2019). The parameters measured are coordinates of points and features, air temperature and relative humidity; and photographs of locations and features were taken. Both air temperature and relative humidity were measured at 3 different levels above the ground surface at 1 minute interval. The closer the distance to the flare stack, the higher the air temperature measured and vice versa. Land Surface Temperature (LST) retrieved from Landsat 5 TM and Landsat 7 ETM+ also shows that the closer the distance to the flare stack, the higher the LST and vice versa. The locational error of points obtained from Landsat 5 TM and Landsat 7 ETM+, and fieldwork measurements give negligible difference of 1.0 × 10-6 to 7.3 × 10-6. Several features, and 4 land use and land cover (LULC) within the sites were clarified. The pattern of plumes from the flare stacks moves outwardly at both sites. Results show that the spatial variability in ground air temperature and derived LST from Landsat 5 TM and Landsat 7 ETM+ differs within 0.8 to 6.0 K because air temperature is different from LST. The results of locational error, LULC, air temperature, LST, and the pattern of plumes observed at both sites show that ground validation through measurement of in-situ data is indispensable to the use of Landsat 5 TM and Landsat 7 ETM+ data and remote sensing technology in the Niger Delta.

A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter

Normalized Difference Vegetation Index (NDVI) data derived from Landsat satellites are important resources for vegetation monitoring. However, Landsat NDVI time-series data are usually temporally discontinuous owing to the nominal 16-day revisit cycle, frequent cloud contamination, and other factors. Although several methods have been proposed to reconstruct continuous Landsat NDVI time-series data, some challenges remain in the existing reconstruction methods. In this study, we developed a simple but effective Gap Filling and Savitzky–Golay filtering method (referred to as “GF-SG”) to reconstruct high-quality Landsat NDVI time-series data. This new method first generates a synthesized NDVI time series by filling missing values in the original Landsat NDVI time-series data by integrating the MODIS NDVI time-series data and cloud-free Landsat observations. Then, a weighted Savitzky-Golay filter was designed to remove the residual noise in the synthesized time series. Compared with three previous typical methods (IFSDAF, STAIR, and Fill-and-Fit) in two challenging areas (the Coleambally irrigated area in Australia and the Taian cultivated area in China) with heterogeneous parcels and complex NDVI profiles, we found that GF-SG performed the best with three obvious improvements. First, GF-SG improved the reconstruction of long-term continuous missing values in Landsat NDVI time series, whereas the other methods were less reliable for reconstructing these long data gaps. Second, the performance of GF-SG was less affected by the residual noise caused by cloud detection errors in the Landsat image, which is due to the incorporation of the weighted SG filter in the new method. Third, GF-SG was simple and could be implemented on the computing platform Google Earth Engine (GEE), which is particularly important for the practical application of the new method at a large spatial scale. The GEE code is freely available at https://code.earthengine.google.com/3a883c9e84ad119045bcb88e4de77b47?noload=true. We expect that this practical approach can further popularize the use of Landsat NDVI time-series data in ecological, geographical, and environmental research.

Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model

Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study

Abstract. The eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm has been designed for the top-of-atmosphere reflectance measured by the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3 to derive snow properties: snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) under cloud-free conditions. This is the first part of the paper, to describe the retrieval method and the sensitivity study. Nine pre-defined SPSs (aggregate of 8 columns, droxtal, hollow bullet rosette, hollow column, plate, aggregate of 5 plates, aggregate of 10 plates, solid bullet rosette, column) are used to describe the snow optical properties. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach. The SSA is then calculated using another pre-calculated LUT for the retrieved SGS and SPS. The optical properties (e.g., phase function) of the ice crystals can reproduce the wavelength-dependent and angular-dependent snow reflectance features, compared to laboratory measurements. A comprehensive study to understand the impact of aerosols, SPS, ice crystal surface roughness, cloud contamination, instrument spectral response function, the snow habit mixture model and snow vertical inhomogeneity in the retrieval accuracy of snow properties has been performed based on SCIATRAN radiative transfer simulations. The main findings are (1) snow angular and spectral reflectance features can be described by the predefined ice crystal properties only when both SGS and SPS can be optimally and iteratively obtained; (2) the impact of ice crystal surface roughness on the retrieval results is minor; (3) SGS and SSA show an inverse linear relationship; (4) the retrieval of SSA assuming a non-convex particle shape, compared to a convex particle shape (e.g., sphere), shows larger retrieval results; (5) aerosol/cloud contamination due to unperfected atmospheric correction and cloud screening introduces underestimation of SGS, “inaccurate” SPS and overestimation of SSA; (6) the impact of the instrument spectral response function introduces an overestimation into retrieved SGS, introduces an underestimation into retrieved SSA and has no impact on retrieved SPS; and (7) the investigation, by taking an ice crystal particle size distribution and habit mixture into account, reveals that XBAER-retrieved SGS agrees better with the mean size, rather than with the mode size, for a given particle size distribution.

A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

Recognition of Autumn Crop Based on Polsar Data and Feature Selection

Optical remote sensed images have been intensively used to map global and regional agriculture information. However, few optical images could be collected due to cloud contamination during the crop growth period. Therefore, synthetic aperture radar (SAR) images could be used to extract crop distribution since it is capable of acquiring data without regard to bad weather conditions. Although numerous studies have been successfully carried out to highlight the potential of SAR images in crop monitoring, there are still some problems to be solved. The high dimensionality of multi-temporal SAR images remains a major issue, classification using all features is limited to efficiency. In this study, a method of autumn crop recognition based on PolSAR data and feature selection was proposed. Using Radarsat-2 PolSAR images acquired during the autumn crop growing season, the optimal subset of features was obtained by 3 feature selection methods and 15 polarimetric target decomposition methods. With optimal feature subset and train samples, crops and other ground objects were classified by SVM. The classification result was assessed by test samples.

Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

Object-based spectral-phenological features for mapping invasive Spartina alterniflora

Spartina alterniflora (S. alterniflora), a prevailing invasive species in the coastal zones, has resulted in significant ecosystem degradation and economic losses since its introduction to China in 1979. Among the existing studies that incorporate remote sensing to map S. alterniflora, the pixel-based phenological feature composite method (Ppf-CM) has proved to be successful due to its merits to overcome cloud contamination while exaggerating the spectral separability between S. alterniflora and its co-dominant native species. However, one major limitation of the Ppf-CM method is that it extracts phenological features from a single-pixel without accounting for its surrounding geospatial information that proved essential for more accurate mapping. In this study, we aim to ameliorate this problem by incorporating geometric, texture, and contextual information. To this end, we developed a new object-based phenological feature composite method (OPpf-CM). Specifically, we first generated a composite image by stacking two images that were respectively acquired during the distinctive phenological periods of S. alterniflora. Then we derived spectrally homogenous objects on the composite image through an unsupervised multiscale segmentation method. Various features derived from objects served as the input of support vector machine (SVM) classifier to produce a S. alterniflora map. To evaluate the performance of OPpf-CM, we carried out a comparison of classification performance between our developed OPpf-CM method and the previously developed Ppf-CM one (Tian et al., 2020) with the aid of visual interpretation and accuracy statistics (i.e. confusion matrix). The results showed that OPpf-CM achieved a higher overall accuracy (98%) than Ppf-CM (96.67%) when validated with the ground reference data that was visually interpreted by the high spatial resolution imagery and field survey. Meanwhile, the identification of fragmented S. alterniflora patches along with mixed-cover areas can be significantly improved with the OPpf-CM method. We envision that the developed object-based spectral-phenological feature has the potential to be applied to mapping a wide spectrum of coastal vegetations.