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Previous studies have indicated and shown the feasibility of retrieving snow density from ground based passive microwave measurements at the L band (1 – 2 GHz) from theoretical and experimental viewpoints. This paper expands the previous studies by presenting a case study of the retrieval problem with space borne brightness temperature measurements from the SMOS satellite over Sodankylä, Finland. To successfully retrieve snow density, also ground and forest parameters were included to the retrieval process. The retrieved variables were validated against in-situ ground, snow, and forest measurements made around the Sodankylä area over 12 winters from 2010 to 2022. A Bayesian framework was used to account for and quantify the uncertainties associated with the retrieval process. The results show good agreement between the retrieved ground and forest variables with the respective reference values. The snow density retrievals were seen to suffer from multiple sources of geophysical noise. However, the monthly average bulk snow density was successfully retrieved under stable midwinter conditions. The best agreement with the in-situ measurements was found for February, with a bias of -0.4 kg/m3, an unbiased root mean square difference of 12.2 kg/m3, and a correlation coefficient of 0.75. • Ground, snow, and forest parameters were retrieved over 12 winters. • The results were validated against comprehensive in-situ measurements. • The study site represents a typical heterogeneous boreal forest site. • Retrieved vegetation optical depth follows the modelled dynamics and value range. • Retrieved snow density agrees with measurements during the cold mid-winter period. [ABSTRACT FROM AUTHOR]

The monitoring of forest ecosystems is significantly affected by the lack of consistent historical data of low-severity (forest partially disturbed) or gradual disturbance (e.g. eastern spruce budworm epidemic). The goal of this paper is to explore the use of a subset of Landsat time series and deep learning models to identify both the type and the year of disturbances, including low-severity and gradual disturbances, in the boreal forest of eastern Canada at the pixel level. Remote sensing data such as the spectral information from Landsat time series are the best available option for large scale observations of disturbances that go back decades. Traditional modeling approaches, like LandTrendr, require substantial handcrafted pre-processing to remove noise and to extract temporal features from the image sequences before using them as input to a classical machine-learning model. Deep-learning models can autonomously discern which features are relevant within the coarse temporal and spectral information from the Landsat annual dense time series. We evaluated the performance of TempCNN and Transformer model in detecting and classifying the type and the year of the forest disturbance using Landsat time series subsequences. Our findings resulted in the generation of four disturbance maps outlining the forest history from 1986 to 2021 within the eastern Canadian boreal forest. Our experimental outcomes demonstrate several significant benefits of employing deep learning models. Firstly, using noisy Landsat time series they achieve comparable accuracy for classifying fire and total harvesting than existing publicly available disturbance maps. Secondly, the use of shorter time series subsequence with deep learning models enables to map adequately different overlapping disturbances occurring in the complete time series. Finally, they increase the number of distinguishable disturbance classes by adding partial harvesting, gradual disturbances, and forest recovery from older events, making them useful approaches for obtaining the first remote sensing-based map for areas affected by the eastern spruce budworm. • TempCNN, Transformer tested on Landsat time series for disturbance classification. • The type and the year of disturbance events were classified with good accuracy. • Deep learning effective for identifying partial and progressive disturbances. • Landsat subsequences extract overlapping disturbances. [ABSTRACT FROM AUTHOR]

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ± 0.05 + 15 % ∗ AOD sites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ± 0.05 + 15 % ∗ AOD sites . The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results. • Time series and multi-wavelength retrieval of AOD with deep learning for Himawari-8. • Trained with >1.4 million AOD samples from ground-based network measurement. • Accuracy of AOD is high at all wavelengths with RMSE = 0.101 and R = 0.949 at 550 nm. • Improvement over NASA MAIAC AOD at 550 nm with RMSE = 0.186 and R = 0.832. • Derived AE is accurate with RMSE = 0.620 and R = 0.291 due to multi-wavelength retrieval. [ABSTRACT FROM AUTHOR]

Geothermal systems can be used to produce low-emission energy throughout the day and night, regardless of the weather conditions. These features make geothermal systems a sustainable and reliable energy source, which can be exploited on a much larger scale than it is now. Remote sensing techniques can support detecting areas potentially suitable for geothermal energy production, thereby reducing the costs of preliminary exploration. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can provide nighttime thermal imagery, which can be used for geothermal anomaly detection. This paper presents a method for automated detection of geothermal anomalies using nighttime ECOSTRESS data of the study area in Olkaria, Kenya. The proposed detection method is a kernel-based one, and includes adaptions of kernel size for the cases of large geothermal anomalies. The accuracy of the method is verified with reference data acquired during field work. A producer's accuracy of 82% is achieved, which is on average 56% points better than in randomised anomaly maps. The possible sources of errors in detection are heat capacity of surfaces, terrain features and vegetation masking the thermal signatures. The high producer's accuracy proves potential for application in global mapping of geothermal anomalies. [ABSTRACT FROM AUTHOR]

Surface waves are the most widely distributed texture in synthetic aperture radar (SAR) images acquired over the oceans, and long ocean waves are generally imaged in SAR images with a spatial resolution of 5 m as wave-looking structures. If these ocean-wave textures cause interference when detecting or extracting small-scale ocean features on the ocean surface, it is necessary to suppress them. However, owing to the complexity and unknown nature of ocean-wave distribution, existing filtering methods based on image spatial domain or transform domain and deep-learning-based methods cannot effectively suppress ocean-wave components. To solve this problem, this paper proposed a depth counteraction method to take advantage of the time-varying characteristics of ocean waves and other ocean phenomena to counteract ocean-wave components in deeper feature spaces. The proposed method first used sub-aperture segmentation to obtain multi temporal SAR images, determining the depth features of these images using encoder networks. Next, counteraction techniques were used in this depth-feature space to suppress the ocean-wave components, after which the SAR image could be reconstructed without ocean-wave components using the decoder network. Additionally, to further exploit the information in the images, parameter-sharing encoder networks were employed. Compared with seven other state-of-the-art SAR filtering methods, the proposed method can improve the average peak signal-to-noise ratio (PSNR) to 13 dB on simulated data and reduce the average target–to–clutter ratio (TCR) to 0.16 on real world SAR data. These indicators show that the proposed method has the potential to make useful contributions to detection or extraction of small-scale ocean features such as oil detection. • The ocean-wave distribution is more complex than speckle noise distributions. • Traditional filtering methods cannot effectively suppress ocean-wave components. • Sub-aperture segmentation can extract time-varying characteristic of ocean waves. • Simple neural network structure has powerful representation ability. • The counteraction method is more effective than fitted wave distribution method. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatiotemporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery remains problematic due to the presence of ambiguous signal and noise within the image. Conversely, ice and water are easily distinguishable using multispectral imagery (MSI), but in the polar regions the ocean's surface is often occluded by cloud or the sun may not appear above the horizon for many months. To address some of these limitations, this paper proposes a new tool trained using concurrent multispectral Visible and SAR imagery for sea Ice Detection (ViSual_IceD). ViSual_IceD is a convolution neural network (CNN) that builds on the classic U-Net architecture by containing two parallel encoder stages, enabling the fusion and concatenation of MSI and SAR imagery containing different spatial resolutions. The performance of ViSual_IceD is compared with U-Net models trained using concatenated MSI and SAR imagery as well as models trained exclusively on MSI or SAR imagery. ViSual_IceD outperforms the other networks, with a F1 score 1.30% points higher than the next best network, and results indicate that ViSual_IceD is selective in the image type it uses during image segmentation. Outputs from ViSual_IceD are compared to sea ice concentration products derived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight how ViSual_IceD is a useful tool to use in conjunction with PMW data, particularly in coastal regions. As the spatial-temporal coverage of MSI and SAR imagery continues to increase, ViSual_IceD provides a new opportunity for robust, accurate sea ice coverage detection in polar regions. • ViSual_IceD is a convolutional neural network (CNN) trained for sea ice detection. • The CNN inputs multispectral and radar images outperforming single image tools. • The network selects the most informative image type during segmentation. • Network outputs are more accurate than coastal AMSR2 sea ice concentration data. [ABSTRACT FROM AUTHOR]

Surface emissivity (ε) is used to characterize surfaces and to determine surface temperature from thermal radiation data. While in many applications it is treated as a constant, it is known to change with surface water content. Several ASTER/SEVERI based studies have speculated that diurnal changes in ε over deserts are linked to diurnal soil water content cycles resulting from water vapor adsorption during the night and subsequent evaporation during the day. This paper aims, for the first time, to validate the relationship between diurnal changes in surface ε and the changes in soil water content due to water vapor adsorption and evaporation under natural conditions. Measurements were conducted with a 6-band infrared radiometer, designed to validate ASTER bands 10–14, with study-specific recalibration for improved accuracy of ε. The evaluation included two different approaches to determine ε : using a single reference band (1B) and using the temperature/emissivity separation algorithm (TES). water content. While the TES has proven itself in many applications, it was found that for the soils studied (sand and loess) the use of 1B approach gave more consistent results for ε changes with soil water content than TES. Emissivity could be a powerful tool to characterize little studied soil water content changes in arid regions, but will require better characterization of surface properties to quantify the relationship between ε and soil water content for various soil types. Additional challenges to upscale this method include properly accounting for air irradiance and spatial heterogeneity. Meeting these main challenges will lead the way to detect small changes in soil water content under dry conditions at larger scales. Whether these are a result of water vapor adsorption or other processes, detecting such small changes in soil water content will provide new insights into desert hydrology. • On-ground validation of an infrared radiometer with ASTER bands. • Calibration and measuring protocol for minute changes in emissivity. • Observed emissivity changes of 0.2–0.6 due to water vapor adsorption. • Main challenges: air radiance, surface heterogeneity, and soil spectral properties. • Potential for new insights into desert hydrology. [ABSTRACT FROM AUTHOR]

Knowledge of spatio-temporal patterns of global land surface evapotranspiration (ET) is essential for understanding the exchanges of energy, water and carbon between the earth surface and atmosphere and responses to human activities, climate changes, and extreme weather events. This paper comprehensively reviewed accuracies and spatio-temporal patterns of 25 state-of-the-art global datasets of land surface ET from 2001 to 2018, where the global and/or local distributions of annual mean, interannual variability, and annual trend for both ET and its components [canopy transpiration (Ec), soil evaporation (Es), and interception evaporation (Ei)] were investigated. Overall, 23 out of the 25 ET products exhibited good consistency with monthly eddy covariance observations at 83 globally distributed sites, with the root mean square error (RMSE) ranging from ∼19 mm/month to ∼29 mm/month and the coefficient of determination (R2) ranging from 0.45 to 0.76. Comparatively, almost all ET products were in good agreement with the water balance-based observations at 137 large basins worldwide, with the R2 of >0.60 and the RMSE ranging from ∼200 mm/yr to ∼260 mm/yr. In general, remote sensing-based, machine learning-based, and hybrid-based ET products generally agreed better with site observations than reanalysis-based products. Global annual ET ranged from 530 mm/yr to 700 mm/yr with the ensemble mean of 628 ± 44 mm/yr (excluding areas of the urban, barren, wetlands, permanent snow and ice, and water bodies). Most products captured consistent global spatial distributions of annual mean, interannual variability, and annual trends of land surface ET and its components. Both mean annual ET and its components generally decreased with increasing latitudes though with an increasing trend in time. Interannual variability also decreased with increasing latitudes and with an increasing trend in time, but only in the Northern Hemisphere; these signals were not clear in the Southern Hemisphere. Most of the 25 products presented unimodal shapes of frequency curves, and displayed a sequenced multi-year mean of seasonal ET of JJA > MAM > SON > DJF. The Ec exhibited the largest mean annual value, in most areas followed successively by the Es and Ei. The ET products displayed a relatively high consistency in the spatial distribution of the Ec/ET with higher estimates of Ec/ET in the Southern Hemisphere than in the Northern Hemisphere. This up-to-date review could benefit the understanding of the states and advances of global ET datasets, afford scientific instruction for the development and improvement of ET models and products, and serve as a basis for the choice of ET datasets for specific use. • Spatio-temporal patterns of 25 global datasets of land surface ET are reviewed. • The 25 global ET products are validated against EC- and WB-based observations. • Global and local patterns for both ET and its components are investigated. [ABSTRACT FROM AUTHOR]

Recent studies have shown that radar temporal coherence over tropical and boreal forests undergoes a diurnal cycle as a result of a combined effect of the wind-induced motion of scatterers and of the change and displacement of water within the plant in response to the transpiration process. Within this context, the objective of this paper is to investigate, for the first time, the diurnal cycle of temporal coherence over wheat crops in relation to its development and physiological functioning throughout the agricultural season. A ground-based experiment was installed in Morocco, targeting a wheat field during the 2020 agricultural season. The radar system, essentially based on a Vector Network Analyzer (VNA) connected to 6C-band antennas installed at the top of a 20 m tower, has enabled quad-polarimetric acquisitions every 15 min. In parallel, evapotranspiration, soil moisture and meteorological variables are automatically measured in addition to above-ground biomass and vegetation water content collected during field campaigns. The results show that the temporal coherence with a 15 min baseline follows a marked diurnal cycle characterized by variable amplitude according to the phenological stage, with high values during the night, a significant morning drop to reach the lowest values in the late afternoon followed by an increase to recover the high nighttime values. The rate of the drop at dawn is shown to be related to the increase of evapotranspiration (r = 0.80 at VV polarization) when the wheat is covering the soil and the transpiration dominate the evapotranspiration process. This supports the assumption of a physiological effect related to water movement entailing a decorrelation. By contrast, the daily minimum of temporal coherence occurring in the late afternoon correlates well to the daily maximum of wind (r = 0.7). Interestingly enough, the amplitude of the diurnal cycle exhibit a marked seasonal evolution characterized by an increase of 85% from tillering to maturity in relation to the wheat development. At the early start of the season when the soil is almost bare, irrigation events impact slightly the diurnal cycle of temporal coherence. Likewise, it is shown that the presence of dew in the early morning has led to a decrease of the decorrelation rate. Temporal coherence dynamic has also been investigated for longer baselines up to 22 days. Results indicate a stronger decorrelation than what has been observed on tropical and boreal forests by previous studies with values below 0.4 for baselines above 2 days. Taken together, the results of this work demonstrate the unique potential of sub-daily C-band data for monitoring crop water status by future geostationary radar missions such as Hydroterra. • Diurnal cycles of 15-min C-band temporal coherence over wheat crops are analyzed. • The morning drop of the temporal coherence is related to the transpiration process. • Temporal coherence exhibits a marked seasonal dynamic according to phenological stages. • Irrigation events and morning dew have an impact on sub-daily temporal coherence. • Early morning hours showed potential for monitoring the vegetation physiological functioning. [ABSTRACT FROM AUTHOR]

Floods are a great threat to Pakistan with increasing concern. As the consequences of increased extreme weather related to climate change, Pakistan experiences severe floods almost every year. This study aims to explore and analysis the actual inundated situation, magnitude, the possible causes of the 2022 devastating floods, and future trends. We presented an enhanced nationwide flood mapping method and compared with other pixel-based image processing techniques including active contours and change detection. These algorithms were applied to Sentinel-1 Ground Range Detected (GRD) Synthetic Aperture Radar (SAR) imagery (10 m spatial resolution) with various land types and inundation scenarios in Pakistan, and were evaluated using other reference flood products. Accuracy evaluation analysis demonstrated that our algorithm has high robustness and accuracy, with the overall accuracy (OA) higher than 0.83 and critical success index (CSI) up to 0.91, and is suitable for automated flood monitoring in near real time. Nearly one-third of the lands were flooded in 2022, and more than half were inundated croplands. Punjab and Sindh provinces were the most severely affected regions, with the proportions of inundated area in 2022 (21.26% and 20.55%) nearly twice of that in 2010 (11.40% and 12.70%), indicating an intensified flooding trend. Analysis of possible influential factors showed that the intense and cumulative rainfall during the monsoon season (June to August) was the major cause of the 2022 flood event. Although the snow melted rapidly in June (the average change in snow depth is ∼10 mm), the overall ablation contributed insignificant amount to the flood water. The glacial lake outburst floods (GLOFs) induced by abnormal April–May heatwave provide water flowed into the tributaries of the Indus River, but are difficult to spread for thousands of kilometers from mountains to the plain downstream. The combination of the intrinsic arid climate and extreme floods exacerbate the already severe situation. • This paper presents an automated method based on tile thresholding and fuzzy logic. • The inundation in Punjab and Sindh in 2022 are nearly twice of that in 2010. • The cumulative rainfall is the major cause of the 2022 flood event. • The snow melting and GLOFs are likely small relative to the flood water gains. • These observations imply an intensified flooding and water cycle in Pakistan. [ABSTRACT FROM AUTHOR]

The marine shipping industry is among the strong emitters of nitrogen oxides (NO x) – a substance harmful to ecology and human health. Monitoring of emissions from shipping is a significant societal task. Currently, the only technical possibility to observe NO 2 emission from seagoing ships on a global scale is using TROPOMI data. A range of studies reported that NO 2 plumes from some individual ships can be visually distinguished on selected TROPOMI images. However, all these studies applied subjectively established pre-determined thresholds to the minimum speed and length of the ship — variables that to a large extent define the emission potential of a ship. In this study, we investigate the sensitivity limits for ship plume detection as a function of their speed and length using TROPOMI data. For this, we train a classification model to distinguish TROPOMI image patches with a ship, from the image patches, where there were no ships. This way, we exploit ground truth ship location data to potentially exceed human visual distinguishability. To test for regional differences, we study four regions: the Mediterranean Sea, Biscay Bay, Arabian Sea, and Bengal Bay. For the Mediterranean and the Arabian Sea, we estimate the sensitivity limit to lie around a minimum speed of 10 knots and a minimum length of 150 m. For the Biscay Bay — around 8 knots and 100 m. We further show that when focusing the analysis on the biggest emitters (junctions of several ships in the area), the detectability can be improved up to above 0.8 ROC-AUC. Finally, we show that increasing the size of the dataset, beyond the dataset used in this study, yields further improvements in the detectability of smaller/slower ships. The rate of improvement in both experiments is dependent on the region studied. This paper is the first comprehensive study on the real-world sensitivity of the TROPOMI instrument to distinguish the NO 2 emission produced by seagoing ships. • We established sensitivity limits for NO 2 ship plume detection with TROPOMI. • Obtained sensitivity limits: Med/Arabian Sea — 10 kt, 150 m; Biscay Bay — 8 kt, 100 m. • Detectability of ship plumes from short/slow ships can be improved with more data. • Detectability of NO 2 plumes differs between the regions. [ABSTRACT FROM AUTHOR]

Quantifying the mangrove height growth rate is important for understanding mangrove growth as well as its coupling with the other ecosystems and the climate. However, research on this topic has been limited due to the lack of multi-temporal data on mangrove tree height (TH) and methods for tree-level analysis. This study investigates the use of multi-year UAV-LiDAR data for mangrove TH growth characterization. We evaluated the UAV-LiDAR data by comparing it with field measured THs, observing a deviation ranging between −9 and +11 cm. Notably, the deviation was minimal at only 1 cm when comparing datasets collected using the same LiDAR sensor, indicating the validity of adopting multi-year UAV-LiDAR data for mangrove TH growth analysis. Methodologically, we assessed a tree-based approach and a grid-based approach. The tree-based approach is considered more feasible as it mirrors field measurements, allowing for direct comparisons with field surveys. In addition, statistically significant differences were revealed in TH growth rates among mangrove species and clumpiness groups (p < 0.001), while the invasion of Spartina alterniflora did not significantly affect the growth rates. This paper presents the inaugural effort in mangrove TH growth quantification using multi-temporal UAV-LiDAR data, showcasing the potential of this quickly emerging remote sensing technique in individual mangrove change analysis. We proposed a recommended framework for UAV-LiDAR-based TH growth rate measurement. The limitations and uncertainties embedded in the data sources, methodology, and results interpretation were also discussed. • First analysis of tree height growth using multitemporal UAV-LiDAR. • Proposed first diagram for mangrove height growth monitoring with UAV remote sensing. • Individual tree growth can be monitored using UAV-LIDAR. • Registration of multi-year UAV-LiDAR data hinders its application in change analysis. [ABSTRACT FROM AUTHOR]

More accurate, spatio-temporally, and physically consistent land surface temperature (LST) estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a mechanism-guided ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable mechanistic guidance is incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which only uses remote sensing data as input, serves as the pure ML model. Mechanistic guidance (MG) is coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the MG-LGBM model, which incorporates surface energy balance (SEB) guidance underlying the data in CLM-LST modeling within a biophysical framework. Results indicate that, MG-LGBM model shows a good accuracy for the sample-based validation, with a root-mean-square error of 1.23–2.03 K, and a Pearson correlation coefficient of 0.99. Validation with four independent ground measurements shows that MG-LGBM can generate clear-sky LST that is comparable to the original Moderate Resolution Imaging Spectroradiometer- (MODIS) LST under fully clear-sky conditions and can correct for the likely cloud-contaminated LST pixels. The generated LST also presents a high accuracy (RMSE = 2.91–3.66 K and R = 0.97–0.98) under cloudy-sky conditions. Compared with a pure mechanistic method and pure ML methods, the MG-LGBM model improves the prediction accuracy and mechanistic interpretability of LST. It also demonstrates a good extrapolation ability in the regions without valid samples, suggesting that the predictions of MG-LGBM model not only exhibit low errors on the training dataset but also align consistently with the known mechanistic laws in the unlabeled set. Compared with other popular ML methods and sophisticated gapless products, the MG-LGBM model delivers a superior validation accuracy and image quality. The proposed method represents an innovative way to map accurate and mechanistically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation. • An input variable mechanism-guided ML method is proposed for mapping gapless LST. • Improvement in the interpretability and generalization ability of pure ML for LST estimation • Highly accurate estimation results under all-weather conditions based on extensive validation. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter in global ecological and climate system, while its accurate estimation under cloudy-sky conditions remains a challenge. In this paper, a practical two-step framework has been proposed for all-sky LST estimation, which effectively combined physical mechanisms and machine learning algorithms. The first step was the estimation of hypothetical clear-sky LST. The Bayesian optimization-based Extreme Gradient Boosting (BO-XGB) model was developed to establish the implicit and complex relationship between hypothetical clear-sky LST and corresponding independent variables, including hypothetical clear-sky radiation energy input, surface biophysical parameters, atmospheric conditions, as well as spatial and temporal features. The second step was the cloud radiative forcing effect (CRFE) correction for hypothetical clear-sky LST under cloudy-sky conditions. Based on the surface energy balance (SEB) equation and conventional force-restore method, the analytical expressions of CRFE correction terms were derived, which speeded up the calculation and facilitated error analysis. Based on several remote sensing products, including Advanced Baseline Imager (ABI) LST and auxiliary data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Global LAnd Surface Satellite (GLASS), as well as ERA5 reanalysis data, the proposed framework was applied to recovered spatially continuous hourly all-sky LST information over the contiguous US (CONUS) in 2018. The accuracy of the proposed method has been validated by ground measurements from 60 representative sites operated by the Surface Radiation Budget (SURFRAD) and AmeriFlux. For clear-sky conditions, the overall root mean square error (RMSE) was 2.67 K, with a bias of −0.47 K and R2 of 0.96. For cloudy-sky conditions, the corresponding accuracy metrics were 2.60 K, −0.49 K, and 0.96, respectively, and RMSEs for all sites were below 4 K, which indicated the stability of the proposed method. Besides, daily average LST has also been calculated based on the estimated hourly LSTs with RMSE of 1.68 K and bias of −0.48 K. • A new and fast hypothetical clear-sky LST estimation model is established. • Analytic expression of LST correct term caused by cloud radiation forcing is given. • The proposed framework combines physical mechanisms and machine learning model. • Accuracy metrics for predictions under clear and cloudy skies are quite similar. [ABSTRACT FROM AUTHOR]

Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community going forward, while offering a vision of what global phytoplankton community composition will look like through the "eyes" of PACE. • Each approach to derive phytoplankton composition has strengths and weaknesses. • Users need a variety of temporal, spatial, phytoplankton diversity resolution. • Hyperspectral missions will increase our ability to monitor phytoplankton diversity. • Need for appropriate validation products, variety of approaches, FAIR distribution. [ABSTRACT FROM AUTHOR]

Total precipitable water (TPW) is a key parameter in the study of water cycle and climate change. High-resolution TPW is critical for understanding regional water cycle processes. Besides, high-resolution TPW is also an important input parameter for atmospheric correction which helps to eliminate atmospheric impact and improve surface parameter retrieval accuracy. However, obtaining high-resolution TPW in all-weather condition from a single satellite sensor still has many limitations and challenges. This paper proposed a new fusion method to obtain all-weather TPW with a high spatial resolution of 0.05° × 0.05° by taking advantage of various water vapor related variables derived from microwave and near-infrared (NIR) remote sensing. At the same time, the contribution of variables in the fusion algorithm was analyzed to find the best fusion scheme. There are two major improvements in the newly developed fusion method. First, a new water vapor sensitive parameter – ratio of brightness temperature polarization difference between frequencies 89 and 36.5 GHz (∆ Tb 89 / ∆ Tb 36.5) was first introduced in the fusion algorithm to provide more high spatial resolution TPW information in both clear and cloudy sky conditions, and the accuracy of the fused TPW was significantly improved when the parameter ∆ Tb 89 / ∆ Tb 36.5 was introduced. Second, a two-steps fusion method was proposed to produce a fused high spatial resolution TPW by fully taking advantage of high quality TPW information from AMSR2 TPW and ∆ Tb 89 / ∆ Tb 36.5 in cloudy condition in the first step, and high quality TPW from MODIS in clear sky condition in the second step. The TPW derived from global navigation satellite system (GNSS) stations of Crustal Movement Observation Network of China (CMONOC) was used to validate the fused TPW and original TPW from MYD05 and AMSR2 at spatial resolution of 0.05° × 0.05°. According to the validation, the correlation coefficient, root mean square error, and relative root mean square error between the fused TPW and the GNSS TPW are 0.96, 4.23 mm and 23.67% respectively. The root mean square errors were improved by 19.96% and 27.32%, respectively, compared to the original MYD05 TPW and AMSR2 TPW. • A new two-step TPW fusion method was proposed based on MW and NIR remote sensing. • The influence of ∆Tb 89 /∆Tb 36.5 on fused TPW was analyzed in all weather conditions. • The fused TPW had a significant improvement compared to the original TPW datasets. [ABSTRACT FROM AUTHOR]

Time-series interferometric synthetic aperture radar (InSAR) provides a unique tool for measuring large-scale and long-term land surface deformation. Under the assumption of a single linear deformation model in conventional InSAR, it is difficult to quantify and interpret the impacts of multiple environmental factors that presumably induce nonlinear deformations. In this paper, we propose a SAR-Transformer method to decompose InSAR time-series signals into various physics-related components and apply the method to evaluate the deformation of the world's longest cross-sea bridge, the Hong Kong-Zhuhai-Macao Bridge (HZMB). We first developed an improved bridge geometry-based InSAR network to monitor the deformation of the HZMB using Sentinel-1 and COSMO-SkyMed images from 2019 to 2022, which were validated using the leveling and GPS data. The SAR-Transformer model was trained using synthetic InSAR time-series samples and applied to decompose the monitored InSAR measurements. Compared with that of conventional curve-fitting and seasonal-trend decomposition using LOESS, SAR-Transformer reduced the mean absolute error at least by 58.32% and mean absolute percentage error at least by 8.84% for time-series signal reconstruction. We evaluated the decomposed patterns according to the geotechnical, meteorological, and marine processes, and found that: 1) Seasonal thermal expansion owing to temperature changes was significant in all parts of the bridge, and deflection due to concrete shrinkage and creep was observed on cable-stayed bridges. 2) The artificial islands experienced evident ground subsidence with a decelerating trend. In particular, the newly adopted non-dredged reclamation method resulted in a lower decelerated settlement than that of fully-dredged reclamation areas. 3) The seawall showed linear horizontal movement from the outward stretching of the reclaimed soil consolidation and periodic displacement related to sea tidal loading. Furthermore, typhoons and coastal earthquakes had limited effects on the permanent movement of the bridge. These results improve the understanding of the interactions between artificial super-infrastructures and environmental factors, and provide valuable guidelines for the maintenance and management of the HZMB. • An improved BGN was developed to monitor bridge deformations in TSInSAR. • The SAR-Transformer model was proposed to decompose the InSAR time series. • Three indexes were generated for time-series deformation characterization. • The decomposed patterns were evaluated using various environmental factors. [ABSTRACT FROM AUTHOR]

Nearly two decades ago, the idea of the ‘spectral invariants theory’ was put forth as a new tool to model the shortwave radiation absorbed or scattered by vegetation. The theory states that the amount of radiation absorbed by a canopy should to a great accuracy depend only on the wavelength and a wavelength-independent parameter describing canopy structure. The revolutionary idea behind this theory was that it would be possible to approximate vegetation canopy absorptance, transmittance and reflectance based on only the optical properties of foliage elements and the spectrally invariant parameter(s). This paper explains how this so-called spectral invariant is related to photon recollision probability and to canopy structural variables. Other spectral invariants were later introduced to quantify the directionality of canopy scattering. Moreover, the paper reviews the advances in the theoretical development of the photon recollision probability ( p ) concept and demonstrates some of its applications in global and local monitoring of vegetation using remote sensing data. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) is known to relate directly to leaf and canopy scale photosynthesis. Retrieving SIF from space can thus provide an indication on the temporal and spatial patterns of the terrestrial gross primary productivity (GPP). Recent studies have successfully demonstrated the serendipitous retrieval of SIF from satellite remote sensing instruments originally destined to atmospheric studies. However, the finest spatial resolution achieved by these products is 0.5°, which remains too coarse for many applications, including the early detection of drought impacts on vegetation and the integration with ground GPP measurements from flux-towers. This paper proposes a methodology to spatially disaggregate the information contained within each coarse SIF pixels by using a non-linear model based on the concept of light use efficiency (LUE). The strategy involves the aggregation of high-resolution (0.05°) remote sensing biophysical variables to calibrate the downscaling model locally and independently at each time step, which can then be applied to non-aggregated data to create a new layer, denoted SIF*, with a spatial resolution of 0.05°. A global SIF* dataset is generated by applying this methodology globally to 7 years of monthly GOME-2 SIF data. SIF* is shown to be a better proxy for GPP than the original coarse spatial resolution product according to flux-tower eddy covariance measurements. Its performance is comparable to dedicated GPP products despite that (unlike SIF*) these are calibrated based on the same flux towers, driven by meteorological data and not hampered by the large noise caused by the SIF retrieval. To further illustrate the added-value of the global SIF* product, this paper also presents: (1) an ecosystem level assessment showing a considerable reduction of noise with respect to the original SIF; (2) a spatio-temporal inter-comparison with existing GPP products; and (3) estimations of global terrestrial productivity per selected vegetation types based on SIF*. [ABSTRACT FROM AUTHOR]

On August 29, 2014, a lava eruption commenced in the Holuhraun plain, north-east of the Bárðarbunga caldera in Iceland. The eruption ended on February 27, 2015, thus lasting for a period of about 6 months. During these months the magma chamber below the caldera gradually deflated, feeding the eruption and causing the rare event of a slow caldera collapse. In this scenario, TanDEM-X remote sensing data are of particular interest. By producing medium-high resolution and accurate elevation models of the caldera, it is possible to evaluate volume losses and topographical changes to increase the knowledge about the ongoing activity. In particular, five TanDEM-X bistatic acquisitions have been commanded between August 01, 2014 and November 08, 2014, two of them before the volcanic eruption and three of them during the event. Additionally, to fully cover the volcanic activity, the lava field 48 km north-east of the caldera has also been monitored. In the first part of the paper, the expected elevation accuracy is studied on two levels. Absolute and relative height accuracies are analyzed by inspecting the X-band signal propagation into snow and by investigating the impact of the main geometrical system parameters and the local geomorphology. In the second part of the paper, the analysis is applied to the Bárðarbunga volcanic system, including a validation performed using complementary altimeter data. The main geophysical outcome is the accurate temporal height tracking of the entire caldera and the glacial volcanic system. The measured volume loss at the caldera location is about 1 billion cubic meters in two months with an average rate of subsidence of nearly 50 cm/day. These numbers confirm other independent sources and can be compared to lava volume measurements. Finally, the last segment of the dyke that propagated from the Bárðarbunga caldera to the Holuhraun lava field is mapped and a graben structure with a width of up to 1 km and a sinking of a few meters is reported. [ABSTRACT FROM AUTHOR]

The multifractal fusion is a non-parametric technique that merges remotely-sensed maps of different ocean variables to produce higher quality remote sensing products. This method was first introduced to reduce the noise level in a map of a given ocean variable provided that a higher-quality map of a second ocean variable is available. In this work, we go one step further in both the theoretical justification and the application of the multifractal fusion. In the first part of the paper we explore if the quality of the derived maps can be improved by using a higher-dimensional tensor relating the gradient of one variable with that of the other. Higher-dimension tensors require the introduction of appropriate integration schemes to avoid inducing long-range artifacts, something that is not completely solved yet. The results show that although higher-dimension tensors lead to an improvement in the spatial consistency of the derived maps (i.e., a higher quality of the associated singularity exponents), the comparison with in situ data still show significant reconstruction errors as compared to the simplified scalar approach. In the second part of the paper we have used the scalar approach to increase the spatial and temporal resolution of the current Soil Moisture and Ocean Salinity (SMOS) Level 3 sea surface salinity (SSS) maps produced at the Barcelona Expert Center (BEC). As such, new daily SMOS SSS global maps are generated at a 0.05 ° × 0.05 ° grid resolution. The quality of the resulting products has been assessed using in-situ comparison, singularity analysis and power density spectra. It is concluded that the multifractal fusion can be used to significantly increase the spatial and temporal resolution of the SSS maps. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on vegetation, in particular plant available water and water content in a canopy, drought index and flood risks, surface ocean winds in storms, freeze/thaw state and sea ice and its effect on ocean–atmosphere heat fluxes and dynamics affecting large-scale processes of the Earth's climate system. Significant progress has been made over the course of the now 6-year life time of the SMOS mission in improving the ESA provided level 1 brightness temperature and level 2 soil moisture and sea surface salinity data products. The main emphasis of this paper is to review the status of the mission and provide an overview and performance assessment of SMOS data products, in particular with a view towards operational applications, and using SMOS products in data assimilation. SMOS is in excellent technical condition with no limiting factors for operations beyond 2017. The instrument performance fulfils the requirements. The radio-frequency interference (RFI) contamination originates from man-made emitters on ground, operating in the protected L-band and adding signal to the natural radiation emitted by the Earth. RFI has been detected worldwide and has been significantly reduced in Europe and the Americas but remains a constraint in Asia and the Middle East. The mission's scientific objectives have been reached over land and are approaching the mission objectives over ocean. This review paper aims to provide an introduction and synthesis to the papers published in this RSE special issue on SMOS. [ABSTRACT FROM AUTHOR]

The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta-analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN. [ABSTRACT FROM AUTHOR]

The wet tropospheric correction (WTC) is still a significant error source in most altimetric products. For studies such as sea level change, as those performed in the scope of the ESA Sea Level Climate Change Initiative (SL_cci) project, the use of uniform and consistent WTC datasets are of major importance. For this purpose, a set of improved WTC, using the Global Navigation Satellite System (GNSS) derived Path Delay (GPD) algorithm, was envisaged for the main six altimetric missions: the so-called reference missions (TOPEX/Poseidon, Jason-1 and Jason-2) and the three ESA missions (ERS-1, ERS-2 and Envisat). The GPD methodology is based on the combination of wet path delays derived from zenith total delays calculated at a network of coastal GNSS stations and valid microwave radiometer (MWR) measurements at altimeter nearby points. At each altimeter point with an invalid MWR value, the WTC is estimated from the set of observations, along with the associated mapping error, using a linear space-time objective analysis technique that takes into account the spatial and temporal variability of the WTC field and the accuracy of each data set used. In the absence of observations, tropospheric delays from the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis (ERA) Interim model are adopted. Originally designed to improve the WTC in the coastal zone, the GPD evolved to include the global ocean, correcting for land and ice contamination in the MWR footprint, or spurious measurements due to e.g. instrument malfunction. This paper presents an overview of the GPD implementation for the afore-mentioned six altimetric missions. The GPD products have been validated by comparison with the WTC adopted as the reference correction by the Archiving, Validation, and Interpretation of Satellite Data in Oceanography (AVISO): the so-called composite correction (Comp) for all missions except Jason-2, for which the version D of the Geophysical Data Records (GDR-D) Advanced Microwave Radiometer (AMR) WTC is adopted. Various sea level anomaly (SLA) statistical analyses have been performed and are summarised in this paper: differences in SLA variance calculated along satellite tracks and at crossovers; SLA variance difference function of distance from the coast or function of latitude. Results show that the GPD WTC evidence a very significant improvement with respect to the Comp correction, particularly at polar and coastal regions, for all ESA and TOPEX/Poseidon missions. For the last, the impact is particularly significant in the second part of the mission, since detected anomalies present in the TOPEX Microwave Radiometer products are corrected by the algorithm. For Jason-1 and Jason-2, some improvements are observed in the coastal regions, although globally not very significant, particularly for Jason-2. This is attributed to the good performance of the WTC present in the most recent Jason-1 and Jason-2 products. The GPD WTC constitutes a coherent dataset of global and continuous corrections, for most missions a major improvement with respect to the baseline MWR and the Comp wet tropospheric corrections. [ABSTRACT FROM AUTHOR]

The first seven bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used widely for global land-cover/land-use (LCLU) monitoring (e.g., deforestation over the Amazon basin). However, the spatial resolution of MODIS bands 3–7 (i.e., 500 m) is coarser than that of bands 1 and 2 (i.e., 250 m), and may be too coarse for a large number of applications. In this paper, a new geostatistical approach based on area-to-point regression kriging (ATPRK) is proposed for downscaling coarse spatial resolution bands 3–7 such as to produce a complete set of MODIS images at 250 m. ATPRK takes advantages of the fine spatial resolution information in bands 1 and 2 by regression modeling, and uses area-to-point kriging to downscale the coarse residuals from the regression. ATPRK was compared to four existing methods, including the principal component analysis, wavelets, high-pass filter and kriging with external drift (KED) methods for downscaling in two experiments on MODIS data from the Brazilian Amazon. Both visual and quantitative evaluations (in terms of the root mean square error, correlation coefficient, relative global-dimensional synthesis error, universal image quality index, spectral angle mapper and spectral information divergence) showed that ATPRK produced sharpened images with the greatest quality. In addition, ATPRK perfectly preserved the spectral properties of the original coarse data and was faster than KED. The results reveal the great potential of ATPRK applied to MODIS data for a wide variety of applications, including global monitoring of deforestation. The ATPRK proposed in this paper is an entirely new image fusion approach based on a new conceptualization. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide observation-based estimates of the effects of aerosols on climate. The Aerosol-cci project, part of the European Space Agency's Climate Change Initiative (CCI), was designed to provide essential climate variables for aerosols from satellite data. Eight algorithms, developed for the retrieval of aerosol properties using data from AATSR (4), MERIS (3) and POLDER, were evaluated to determine their suitability for climate studies. The primary result from each of these algorithms is the aerosol optical depth (AOD) at several wavelengths, together with the Ångström exponent (AE) which describes the spectral variation of the AOD for a given wavelength pair. Other aerosol parameters which are possibly retrieved from satellite observations are not considered in this paper. The AOD and AE (AE only for Level 2) were evaluated against independent collocated observations from the ground-based AERONET sun photometer network and against “reference” satellite data provided by MODIS and MISR. Tools used for the evaluation were developed for daily products as produced by the retrieval with a spatial resolution of 10 × 10 km 2 (Level 2) and daily or monthly aggregates (Level 3). These tools include statistics for L2 and L3 products compared with AERONET, as well as scoring based on spatial and temporal correlations. In this paper we describe their use in a round robin (RR) evaluation of four months of data, one month for each season in 2008. The amount of data was restricted to only four months because of the large effort made to improve the algorithms, and to evaluate the improvement and current status, before larger data sets will be processed. Evaluation criteria are discussed. Results presented show the current status of the European aerosol algorithms in comparison to both AERONET and MODIS and MISR data. The comparison leads to a preliminary conclusion that the scores are similar, including those for the references, but the coverage of AATSR needs to be enhanced and further improvements are possible for most algorithms. None of the algorithms, including the references, outperforms all others everywhere. AATSR data can be used for the retrieval of AOD and AE over land and ocean. PARASOL and one of the MERIS algorithms have been evaluated over ocean only and both algorithms provide good results. [ABSTRACT FROM AUTHOR]

Distribution of land cover has a profound impact on the climate and environment; mapping the land cover patterns from global, regional to local scales are important for scientists and authorities to yield better monitoring of the changing world. Satellite remote sensing has been demonstrated as an efficient tool to monitor the land cover patterns for a large spatial extent. Nevertheless, the demand on land cover maps at a finer scale (especially in urban areas) has been raised with evidence by numerous biophysical and socio-economic studies. This paper reviews the small-footprint LiDAR sensor — one of the latest high resolution airborne remote sensing technologies, and its application on urban land cover classification. While most of the early researches focus on the analysis of geometric components of 3D LiDAR data point clouds, there has been an increasing interest in investigating the use of intensity data, waveform data and multi-sensor data to facilitate land cover classification and object recognition in urban environment. In this paper, the advancement of airborne LiDAR technology, including data configuration, feature spaces, classification techniques, and radiometric calibration/correction is reviewed and discussed. The review mainly focuses on the LiDAR studies conducted during the last decade with an emphasis on identification of the approach, analysis of pros and cons, investigating the overall accuracy of the technology, and how the classification results can serve as an input for different urban environmental analyses. Finally, several promising directions for future LiDAR research are highlighted, in hope that it will pave the way for the applications of urban environmental modeling and assessment at a finer scale and a greater extent. [ABSTRACT FROM AUTHOR]

Monitoring and understanding the physical, chemical and biological status of global inland waters are immensely important to scientists and policy makers alike. Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at local to global scales. Furthermore, as sensors, methodologies, data availability and the network of researchers and engaged stakeholders in this field develop, increasingly widespread use of remote sensing for operational monitoring of inland waters can be envisaged. This special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms. The papers variably focus on the retrieval of in-water optical and biogeochemical parameters as well as information on the biophysical properties of shoreline and benthic vegetation. Methodological advances include refined approaches to adjacency correction, inversion-based retrieval models and in situ inherent optical property measurements in highly turbid waters. Remote sensing data are used to evaluate models and theories of environmental drivers of change in a number of different aquatic ecosystems. The range of contributions to the special issue highlights not only the sophistication of methods and the diversity of applications currently being developed, but also the growing international community active in this field. In this introductory paper we briefly highlight the progress that the community has made over recent decades as well as the challenges that remain. It is argued that the operational use of remote sensing for inland water monitoring is a realistic ambition if we can continue to build on these recent achievements. [ABSTRACT FROM AUTHOR]

This paper assessed the SMOS soil moisture values from Level 3 (SMOS L3SM) product provided by the French CNES-CATDS. The evaluation was conducted at the local scale through comparison with ground-based soil moisture measurements acquired in Mali, Niger and Benin from 2010 to 2012. The SMOS L3SM product was compared to three other satellite-based soil moisture products. It was found that, in average over the three sites, the SMOS L3SM product provided the best coefficients of correlation and the lowest root mean square errors (RMSE). The second part of the paper is devoted to retrieve soil moisture estimates between successive SMOS soil moisture measurements in order to increase the temporal resolution. The result of the methodology allows obtaining 3-hour soil moisture mapping over West Africa with a coefficient of correlation greater than 0.82, and an RMSE lower than 0.030 m 3 m − 3 in Niger and Mali and lower than 0.044 m 3 m − 3 in Benin. [ABSTRACT FROM AUTHOR]

Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90. • L8 and S2 products are registered to better than 6 m CE90 when GRI is used. • Continent-specific bias was not observed between L8 and S2 products. • Sentinel-2 products are co-registered to better than 6 m CE90. • Landsat (L8 and L9) products are co-registered to better than 3 m CE90. • A spatial normalization method was developed to resample the S2 10 m band to 15 m. [ABSTRACT FROM AUTHOR]

Significant errors can arise if the adjacency effect (i.e., the contribution of neighbouring pixels to the radiance of a pixel) is neglected in the interpretation of remote sensing images. For example, adjacency radiances can account for >30% of the signal at the top of the atmosphere (TOA) of a white sand-lined coastline (Bulgarelli and Zibordi, 2018). This paper shows that 3D radiative transfer (RT) modelling can quantify this phenomenon. It presents a new 3D Monte Carlo surface-atmosphere RT modelling in the DART RT model, and a resulting virtual 3D Earth-Atmosphere laboratory for accurate simulation of atmospheric RT, including the adjacency effect. It was first validated with the atmosphere model SMART-G for 2D scenes: relative difference is 0.20% in TOA directional reflectance of infinite black surface for solar zenith equal to 60° , and 0.03% in TOA nadir reflectance of a black disc inside infinite white Lambertian surface, for solar zenith equal to 0° and 30°. Then, the adjacency effect on the TOA radiance and TOA albedo of a 3D scene was studied for a circular city (radius 2 km) surrounded by a forest (dimension 10 km), for four Sentinel-2A bands (blue, green, red, near infrared). Its contribution to TOA nadir radiance reaches ∼20% in the near infrared band, and increases with viewing zenith angle (VZA): ∼60% if VZA = 80°. The 3D scene TOA albedo was calculated after adapting the DART Bidirectional Reflectance Factor (BRF) camera to simulate TOA radiance for all upward directions at any angular resolution. For Sentinel-2A's four bands, the adjacency effect influences the TOA albedo of the circular city by up to 10% for the green band and up to 27% for the near infrared band, well above the maximum uncertainty 5% usually required in land surface applications. The adjacency effect of the city neighbourhood 3D structure was studied by replacing it by a Lambertian surface with its albedo. The city nadir TOA radiance changed by up to 1.3% with very small change in TOA albedo (<0.5%). This new modelling greatly improves DART potential for accurate simulation of atmospheric effects. It is in the DART version freely available for research and education (https://dart.omp.eu). • New 3D Monte Carlo surface-atmosphere RT modelling in DART model. • A virtual Earth-Atmosphere laboratory for simulating the adjacency effect. • Study of the impact of the surface 3D structure on the adjacency effect. • Study of the adjacency effect contribution on radiance and albedo at TOC and TOA. [ABSTRACT FROM AUTHOR]

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources. • Exploring a deep learning method for seven-resolution land surface variable estimation. • The finest resolution of estimated variable is 10 m and the temporal continuity is improved. • Estimated variables at seven resolution have consistent spatial distribution. • The estimated multi-resolution variables have high accuracy based on extensive validation. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly increasing amounts of remote sensing data for environmental monitoring. Yet ML models often require a substantial amount of ground truth labels for training, and models trained using labeled data from one domain often demonstrate poor performance when directly applied to other domains. Transfer learning (TL) has emerged as a promising strategy to address domain shift and alleviate the need for labeled data. Here we provide the first systematic review of TL studies in environmental remote sensing. We start by defining the different forms of domain shift and then describe five commonly used TL techniques. We then present the results of a systematic search for peer-reviewed articles published between 2017 and 2022, which identified 1676 papers. Applications of TL in remote sensing have increased rapidly, with nearly 10 times more publications in 2022 than in 2017. Across seven categories of applications (land cover mapping, vegetation monitoring, soil property estimation, crop yield prediction, biodiversity monitoring, water resources management, and natural disaster management) we identify several recent successes of TL as well as some remaining research gaps. Finally, we highlight the need to organize benchmark datasets explicitly for TL in remote sensing for model evaluation. We also discuss potential research directions for TL studies in environmental remote sensing, such as realizing scale transfer, improving model interpretability, and leveraging foundation models for remote sensing tasks. • A review of transfer learning (TL) in environmental remote sensing is provided. • Three types of domain shift and five commonly used TL techniques are described. • Progress on TL in environmental remote sensing of seven research areas is reviewed. • Potential research directions for TL in environmental remote sensing are discussed. [ABSTRACT FROM AUTHOR]

The fate of the carbon stored in frozen peatlands in Canada's Hudson Bay Lowlands (HBL) depends strongly on water, with wetter conditions favoring long-term storage. Dynamic surface water products generated from historical Landsat data exist to inform surface water trends in the HBL based on binary classifications of land vs water at 30 m spatial resolution. However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to fully capture their dynamics. In this paper, we leverage an existing binary dynamic surface water product to generate a spatially and temporally comprehensive Landsat sub-pixel surface water time-series over the HBL. We first generate Landsat Red, NIR and SWIR composites from 1985 to 2021 and evaluate data-driven machine learning and physical models to derive sub-30 m water fractions across the entire mapping domain. Data-driven models were calibrated on 30 m water fractions obtained from 16 of 17 WorldView scene water fraction maps and assessed on the 17th held-out scene in a jackknife validation. Data-driven models were also calibrated by relating scaled Landsat reflectance to water fractions obtained from binary water masks at corresponding scales from 90 to 270 m. Physical models involved simple linear unmixing using a new approach that samples land endmembers in the vicinity of each pixel to represent land reflectance, and a global water endmember sampled beneath permanent water in each image. The assumption of linear scaling was verified at sub-30 m to 270 m resolution for NIR, SWIR and NDVI, which were all highly correlated with water fraction and had no significant differences among water fraction – spectral feature relationships across the full range of scales. Each of 11 different methods and input spectral features were evaluated against WorldView water fractions and ranked based on nine criteria. We found that machine learning using all spectral features including local NIR land endmembers performed best among data-driven approaches, and linear unmixing of the NIR band using local land endmembers performed best among physical. Both best-performing methods were applied to the entire time-series and surface water area trends were calculated and compared with trends obtained from binary water masks. All three methods agree on the direction of surface water change towards wetting, with sub-pixel inter-annual variation agreeing with stream discharge noted in other studies. • Physical and data-driven Landsat water fraction methods tested in the Hudson Bay Lowlands. • Physical methods perform linear unmixing between local land and global water endmembers. • Data-driven methods consist of machine learning calibrated using high-resolution water. • Best methods applied to 1985–2021 Landsat time-series reveal overall wetting trends. • Spatial trends generally suggest drying in bogs and wetting in fens. [ABSTRACT FROM AUTHOR]

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradation of existing forests, natural forest regeneration, and the implementation of sustainable agricultural practices like agroforestry. Assessments of tree canopy height and crown projected area at a high spatial resolution are also important for monitoring carbon fluxes and assessing tree-based land uses, since forest structures can be highly spatially heterogeneous, especially in agroforestry systems. Very high resolution satellite imagery (less than one meter (1 m) Ground Sample Distance) makes it possible to extract information at the tree level while allowing monitoring at a very large scale. This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. Specifically, we produce very high resolution canopy height maps for the states of California and São Paulo, a significant improvement in resolution over the ten meter (10 m) resolution of previous Sentinel / GEDI based worldwide maps of canopy height. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps. We also introduce a post-processing step using a convolutional network trained on GEDI observations. We evaluate the proposed maps with set-aside validation lidar data as well as by comparing with other remotely sensed maps and field-collected data, and find our model produces an average Mean Absolute Error (MAE) of 2.8 m and Mean Error (ME) of 0.6 m. [Display omitted] • Very high resolution canopy height maps at jurisdictional scale are released. • Improved performance from vision transformers based on Self-Supervised Learning (SSL). • First use of SSL and vision transformers for canopy height estimation. • Low resolution GEDI and high resolution aerial lidar predictions are combined. • Model generalizes well to aerial imagery, even though trained with satellite images. [ABSTRACT FROM AUTHOR]

Wide-swath and high-resolution Thermal infrared (TIR) data are limited by the mutual constraint between swath and resolution. In this paper, we propose a whisk-broom imaging method with the long-linear-array (LLA) 4-stage TDI detector (a total of 2048 pixels along splicing direction) and high-precision one-dimensional scanning mirror to achieve 30 m resolution and 300 km swath. And it is being implemented in the first SDGs satellite (SATSAT-1) thermal infrared spectrometer (TIS). Specially, the integration time of whisk-broom imaging system is limited by the speed of scanning mirror, and the shorter integration time is a huge challenge to meet the requirement of high sensitivity and dynamic range. The HgCdTe detector has been improved in its annealing process to reduce dark current and noise, and the opto-mechanical system has been regulated at 195 K to reduce instrument self-emission. To address the limitations of small satellite resources, TIS is equipped with a highly integrated and lightweight design, weighing 247.5 kg and consuming 248 watts respectively. As a result of the complexity of imaging mechanisms and the low frequency of deep space observations, positioning error and radiometric calibration resources have increased, and geometric and radiometric calibration methods have been improved. On-orbit test results show that TIS is capable of detecting temperature differences with a NEdT (noise equivalent differential temperature) less than 0.08 K@300 K, an absolute radiometric accuracy of less than 1 K @ 300 K, and MTF of greater than 0.125. Presently, a collection of more than 201,856 scenes covering land and marine and some precious TIR datasets of planes and naval vessels have been obtained and provided for United Nations for the realization of SDGs goals. • An imaging method with long-linear-array detector and whisk-broom mirror is proposed. • Biweekly and every orbit radiometric calibration modes are effective. • A optimal pixel selection for 4-TDI detectors enhances TIS sensitivity. [ABSTRACT FROM AUTHOR]

Accurate mapping of crops with high spatiotemporal resolution plays a critical role in achieving the Sustainable Development Goals (SDGs), especially in the context of integrated crop-livestock systems (ICLS). Stakeholders can make informed decisions and implement targeted strategies to achieve multiple SDGs related to agriculture, rural development, and sustainable livelihoods by understanding the spatial dynamics of these systems. Accurate information on the extent of ICLS derived from multitemporal remote sensing and emerging map techniques such as deep learning can help in the implementation of sustainable agricultural practices. However, far too little attention has been paid to ICLS map accuracy because it may not be at the forefront of research agendas compared to those of other agricultural practices. This paper aims to map ICLS using high spatiotemporal resolution imagery and deep learning neural network classifiers at two different sites located in Brazil. The pipeline involves four interpretation approaches based on the ICLS class: evaluating deep neural network classifiers with different image composition intervals, explaining commission and omission errors, evaluating the temporal transferability of the method, and evaluating the influence of variables. The study area consists of two locations in São Paulo (study site 1, SS1) and Mato Grosso state (study site 2, SS2), Brazil. We derived nine spectral variables from PlanetScope (PS) images and four metrics through object-based image analysis (OBIA) using two time intervals, 10 and 15 days, to generate the image compositions. These input variables were used in three deep neural network classifiers: convolutional neural network in one dimension (Conv1D), long short-term memory (LSTM), and LSTM with a fully convolutional network (LSTM-FCN). Our results showed that mapping dynamic land use such as ICLS is possible by using high-spatiotemporal-resolution imagery and deep neural network classifiers. The 15-day LSTM-FCN classifier returned the highest map accuracies for both sites, with the following class-level accuracies: producer accuracy (PA) = 97.0% and user accuracy (UA) = 97.0% for SS1 and PA = 82.0% and UA = 96.5% for SS2. Meanwhile, we found map uncertainties arising from the diverse crop calendars and spectro-temporal similarities between ICLS and other land use. The best approaches revealed that temporal generalization was suitable for mapping ICLS, but some classifiers could not generalize due to the inherent characteristics of the class. Most variables were considered efficient for predicting ICLS, although spectral indices revealed better functional relationships, while the PS bands had a lower influence on the predictions. The accuracies achieved with the proposed method represent promising opportunities for the sufficiently accurate mapping of ICLS and other complex crop activities. • A novel approach is proposed for integrated-crop livestock system (ICLS) mapping. • PlanetScope time series and deep learning effectively map ICLS. • Vegetation indices are powerful predictors for ICLS mapping. • ICLS mapping models can be transferred across years to avoid sample collection. [ABSTRACT FROM AUTHOR]

High quality independent ground measurements that are traceable to metrology standards, with a full uncertainty budget, are required for validation over the lifetime of ocean-colour satellite missions. In this paper, we used radiometric Fiducial Reference Measurements (FRM) collected during four Atlantic Meridional Transect (AMT) field campaigns from 2016 to 2019 to assess the performance of radiometric products from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3A (S-3A) and 3B (S‐3B), the Moderate Resolution Imaging Spectroradiometer instrument aboard Aqua (MODIS-Aqua), and the Visible Infrared Imaging Radiometer Suite instrument aboard Suomi NPP and NOAA-20 (Suomi-VIIRS and NOAA-20 VIIRS). The AMT provides one of the few sampling platforms that make high-quality in situ radiometric measurements in oligotrophic, low chlorophyll-a oceanic waters for ocean colour satellite validation. In situ data were acquired and processed following established FRM protocols, calibrated to metrology standards, referenced to inter-comparison exercises and with a full uncertainty budget. From these we selected an uncertainty threshold, which we used as part of a matchup procedure that takes into account the temporal and spatial variability of both the in situ and satellite data. Three atmospheric correction models were compared for S-3A and S‐3B OLCI radiometric products; the standard OLCI IPF-OL-2, POLYMER and NASA SeaDAS l2gen. Based on the round-robin comparison, POLYMER provided the best performance in the retrieval of water-leaving radiances. The analysis showed that Suomi-VIIRS and MODIS-Aqua performed better than NOAA-20 VIIRS, and comparably with S‐3B OLCI standard products. The S-3A OLCI standard product outperformed the NASA products. The S-3A OLCI and S‐3B OLCI instruments were also compared during their tandem phase, which showed that S‐3B OLCI radiances were systematically higher than S-3A OLCI across the spectrum. • Validation of OLCI, MODIS and VIIRS water-leaving radiances in the Atlantic Ocean. • In situ uncertainty used for matchup selection. • POLYMER atmospheric correction performed best on OLCI. • Sentinel-3A OLCI standard product performed better than MODIS and VIIRS. • Good agreement between Sentinel-3A and Sentinel-3B OLCI during Tandem Phase. [ABSTRACT FROM AUTHOR]

Wetlands are vital habitats which play a critical role in global biodiversity. Their functioning is intrinsically linked with their hydrological regime and so the monitoring of water level data is critical to their management and conservation. However, monitoring the hydrology of wetlands can be challenging. Their large number and wide distribution can make them impractical to monitor using traditional field instrumentation. In this context, this paper presents a novel approach for monitoring water levels in gauged and ungauged seasonally flooded wetlands using Sentinel-1 Synthetic Aperture Radar (SAR) data. The methodology, which was fully automated, used multitemporal sequences of SAR imagery to reconstruct hydrometric data at 44 seasonally flooded wetland sites across the Republic of Ireland. The procedure downloaded, processed and checked the suitability of SAR images for water classification. Suitable images were classified and filtered to remove common sources of misclassification, and the flood area in each image was cross-referenced against a predefined stage-area relationship (derived from a digital terrain model) to determine water level. The methodology was calibrated using observed field data and performance evaluated in two ways: 1) generic calibration whereby the process was optimised collectively to achieve the best accuracy for ungauged sites, and 2) site-specific calibration whereby the process was optimised for each site individually to achieve the best possible accuracy for each gauged site. The generic calibration yielded an average Nash-Sutcliffe Efficiency of 0.80 for both calibration and validation datasets while the site-specific optimisation produced results of 0.87 for both calibration and validation datasets. Results show the methodology was capable of accurately reproducing water levels in easonal wetlands for floods as small as 3 Ha. This demonstrates its potential as a viable tool for the remote monitoring of ecologically significant wetlands, and a means of providing the observational data necessary for their long-term sustainable management. • Automated approach for monitoring wetlands using Sentinel-1 data. • Multitemporal sequences of SAR imagery used to reconstruct water level data. • Hydrometric data produced for 44 wetland sites across the Republic of Ireland. [ABSTRACT FROM AUTHOR]

The land surface albedo product of visible infrared imaging radiometer suite (VIIRS) in National Oceanic and Atmospheric Administration (NOAA)'s operational system provides real-time, global daily mean surface albedo, which is a required parameter in the estimation of the daily shortwave net radiation budget. The global gridded VIIRS albedo product is derived from the level-2 granule surface albedo product, which is generated using a Direct Estimation Method. Special gridding and compositing algorithms were developed for aggregating the granular albedo data into the gridded albedo product. This paper describes the design and evaluation of the NOAA VIIRS gridded daily surface albedo product. The cloudy condition, retrieval path, retrieval method, and observing geometry are the criteria in deciding the priority order in the composition processing. The proposed albedo product possesses a complete spatial coverage over global land and ice surface and provides a timely response to surface dynamics. The validation of satellite retrieved daily mean albedo against ground counterparts over a series of well-maintained networks demonstrates the reliability of the composed albedo considering the interference of the seasonal surface heterogeneity conditions around each site. The inter-comparison between the S-NPP and NOAA-20 VIIRS albedo shows good agreement, except for a minor bias related to solar/view angle differences. The cross-comparison between VIIRS albedo and MODIS albedo shows good consistency with some deviations related to the controversy between their upstream snow mask. • Daily global gap-free albedo product from VIIRS operational at NOAA. • Direct estimation of albedo using clear-sky observations. • Comprehensive validation with in-situ observations indicates good product quality. • Major sources of product uncertainty and their mitigation are discussed. • Products from SNPP and NOAA-20 are highly consistent. [ABSTRACT FROM AUTHOR]

In this paper, it is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas – when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) – using NASA's Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015–03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales. • A deep neural network (DNN) based SM data are proposed for the SMAP mission. • Quality of DNN SM is examined through the inter-comparison with other five products. • DNN SM exhibits more similar spatial patterns with SMAP products than ERA5. • DNN SM is superior to other SMAP products in terms of R, RMSE, and ubRMSE. [ABSTRACT FROM AUTHOR]

Greenhouse gases are an essential component in Earth's energy budget, and increasing concentrations of greenhouse gases in Earth's atmosphere are responsible for the primary forcing of increasing global temperature. Quantification of greenhouse gas emissions is a rapidly evolving application of remote sensing, spanning a wide range in remote sensing technologies, wavelengths, platforms, and spatial resolutions. This special issue provides 23 new papers covering the breadth of remote sensing of greenhouse gas emissions. Our summary provides a brief description of each of the papers in the special issue. [ABSTRACT FROM AUTHOR]

Mangrove deforestation has rapidly declined by an order of magnitude compared to that reported for the 20th century, but the remaining mangrove ecosystems are still undergoing biodiversity loss and degradation. Labor-intensive ground surveys that are usually used for terrestrial plant biodiversity assessment are difficult to conduct in the mangrove forests. Assessing plant biodiversity from space does offer a novel perspective, but existing solutions are mainly focused on estimating α- or β-diversities (i.e., diversity within communities or diversity among communities) alone. This paper applied a novel holistic biodiversity approach that could partition γ-diversity into α- and β-diversities for plant diversity mapping with operational satellites (WorldView-2, Sentinel-2, and Zhuhai-1) and field plots in the Qinglangang Provincial Nature Reserve, Hainan, China. We compared the resulting outputs of the α-diversity from the holistic method (SD α) to those by the coefficient of variation (CV) and the Rao's Q index, and the contributions of individual spectral features to α- and β-diversity were also measured. Results indicated that α- and β-diversities accounted for ~30% and ~ 70% of the total diversity in the Reserve, respectively. α-diversity derived from the WorldView-2 images showed statistically higher correlations with the observed Shannon's index (R2: 0.20–0.42) compared to that from Sentinel-2 and Zhuhai-1 (R2: 0.03–0.15). β-diversity derived from WorldView-2 images had the highest accuracy (90.00%), followed by that from Sentinel-2 and Zhuhai-1 (83.33% and 73.33%, respectively). Red-edge and near-infrared spectral features were the most informative features for diversity mapping while shortwave infrared (SWIR) features were also valuable for β-diversity mapping. Concurrent mapping of α- and β-diversities of mangrove forests represents the first step toward achieving rapid biodiversity monitoring schemes of mangrove forests over a national or global scale. • This paper used satellite imagery to map the biodiversity of a mangrove forest. • The γ-diversity was partitioned into α- and β-diversities by a novel method. • The contributions of individual spectral features were quantitatively evaluated. • We analyzed the possibilities and challenges for mapping mangrove biodiversity. [ABSTRACT FROM AUTHOR]

Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. • Developing a new change detection algorithm for multivariate time series. • Exploring the changing pattern of multivariate time series. • Creating a multivariate time series graph. • Enabling the detection of trend changes with short duration. [ABSTRACT FROM AUTHOR]

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities. • Ten cloud masking algorithms for Landsat 8 and Sentinel-2 are evaluated. • Algorithm performance varied depending on the reference dataset. • Average overall accuracy for Sentinel-2 was 80.0 ± 5.3% to 89.4 ± 2.4%. • Average overall accuracy for Landsat 8 was 79.8 ± 7.1% to 97.6 ± 0.8%. • Performance of algorithms improved when thin/semi-transparent clouds not considered. [ABSTRACT FROM AUTHOR]

The paper “A neural network model for remote sensing of diffuse attenuation coefficient in global oceanic and coastal waters: Exemplifying the applicability of the model to the coastal regions in Eastern China Seas” uses the ratio of water-leaving radiance to downward irradiance multiple by π as the remote sensing reflectance for NOMAD dataset and the implement routine of diffuse attenuation coefficient retrieval models. Our results indicate that the quasi-analytical algorithm-based semi-analytical model (QSM) and spectrum-based neural network model (SNNM) produce a superior performance to these mentioned in the original paper, but these results do not affect the main conclusions in the original paper. [ABSTRACT FROM AUTHOR]

Abstract: A number of applications rely on the availability of high resolution TIR data, both in terms of spatial and temporal resolution, as well as in terms of spectral configuration. A consensus exists among the thermal community regarding the lack of such a kind of data, since only one operational sensor is currently providing multispectral TIR data at high spatial resolution (the case of ASTER, at 90m spatial resolution and a 16-day revisit time). In this paper we propose a minimum multispectral configuration to allow the retrieval of both land surface temperature (LST) and emissivity (ε) from thermal infra-red (TIR) data. For this purpose, we propose a minimum configuration based on three TIR bands at around 8.9, 10.6 and 12.0μm, which allows the application of two-channel (or split-window) algorithms as well as the application of a temperature and emissivity separation (TES) method (or a combination of both) for LST retrieval. Results presented in this paper are based on studies performed for different candidate missions, but most of the presented results can be also valid in other contexts. [Copyright &y& Elsevier]

Abstract: Surface reflectance is one of the key products from VIIRS and as with MODIS, is used in developing several higher-order land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritage MODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites show that with the few adjustments recommended, the accuracy is within the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record. [Copyright &y& Elsevier]

Abstract: Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3K to 2K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption. [Copyright &y& Elsevier]

Abstract: Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements. [Copyright &y& Elsevier]