Long-term Data Record Research Articles

Absolute Intercalibration of Spaceborne Microwave Radiometers

Abstract Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and nonlinearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature matchups between GMI and the other instruments over both open ocean and rainforest scenes. To assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multidecadal climate data record from passive microwave satellite observations. Significance Statement Creating a long-term climate data record of satellite observations of ocean winds, water vapor, and other variables requires careful and accurate calibration of the various sensors that are used. In particular, it is important to achieve the best possible consistency between the measurements from all the different instruments. This is a challenging task as the configuration and accuracy of these instruments can differ widely. The purpose of our paper is to demonstrate and validate the basic methodology for performing this intercalibration. The backbone of our method is data observed by a well-calibrated sensor that measures the passive microwave emission from Earth’s surface and atmosphere. We show how to transfer its calibration standard to other sensors.

Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record

Abstract. For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach ∼ 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ∼ 10 %–15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients >0.98 and overall differences <10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

Water Composition, Biomass, and Species Distribution of Vascular Plants in Lake Agmon-Hula (LAH) (1993–2023) and Nearby Surroundings: A Review

A significant change to the land cover in the Hula Valley was carried out during the 1950s: A swampy area densely covered by aquatic vegetation and the old shallow lake Hula were drained. The natural shallow lake and swamps land cover were converted into agricultural development land use in two stages: (1) Drainage that was accomplished in 1957; (2) Implementation of the renovated hydrological system structure, including the newly created shallow Lake Agmon-Hula (LAH), was completed in 2007. The long-term data record of the restored diversity of the submerged and emerged aquatic plant community, and its relation to water quality in the newly created shallow Lake Agmon-Hula LAH, was statistically evaluated. Internal interactions within the LAH ecosystem between aquatic plants and water quality, including nitrification, de-nitrification, sedimentation, photosynthetic intensity, and plant biomass and nutrient composition, were statistically evaluated. The plant community in LAH maintains a seasonal growth cycle of onset during late spring–summer and dieback accompanied by decomposed degradation during fall–early winter. The summer peak of aquatic plant biomass and consequent enhancement of photosynthetic intensity induces a pH increase during daytime and carbonate precipitation. Nevertheless, the ecosystem is aerobic and sulfate reduction and H2S concentration are negligible. The Hula reclamation project (HP) is aimed at the improvement of eco-tourism’s integration into management design. The vegetation research confirms habitat enrichment.

Development and evaluations of the broadband ocean bottom seismometer (Yardbird-BB OBS) in Taiwan

This study focuses on developing and evaluating the broadband ocean bottom seismometer (Yardbird-BB OBS) in Taiwan. The Yardbird-BB OBS is a crucial instrument for recording seismic signals in deep-sea environments. Rigorous testing ensures optimal performance and data recording capabilities. Following assembly, the Yardbird-BB OBS undergoes a 3–6 month deployment test in the deep sea, capturing seismic signals worldwide. Data from 2016 and 2017 deployments in the Okinawa Trough analyze significant seismic events, including a magnitude 7.8 earthquake in New Zealand and a magnitude 6.3 earthquake from a North Korean nuclear test. Waveform analysis, focusing on tele-seismic events and waveform quality, assesses the OBS’s performance, highlighting successful automatic leveling adjustment. These high-quality recordings benefit research, aiding the study of plate tectonics, crustal age estimation, seafloor ambient noise determination, and earthquake location accuracy improvement. The study also details methods for verifying instrumental self-noise, dynamic range, digitization sensitivity, linearity error, clock drift, and data logger power consumption. Calibration procedures and evaluation methods provide insights into Yardbird-BB OBS performance characteristics, contributing to its understanding and enhancement for effective long-term underwater data recording and valuable scientific research.

Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives

Time series data provided by the Sentinel-2 and Landsat satellite missions offer manifold opportunities for grassland monitoring. The high intra-annual observation density of Sentinel-2 combined with the continuous long-term data record of Landsat enable analyses at seasonal, annual, and decadal scales. Fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil provide essential information to describe grassland conditions and processes. Yet, retrieving consistent grassland fractional cover time series from Landsat and Sentinel-2 imagery represents a major challenge. In this study, we implemented a multisensor spectral unmixing approach for retrieving multidecadal (i.e., 1984 to 2021) fractional cover time series of PV, NPV, and soil for Germany's permanent grasslands from the Landsat and Sentinel-2 archives. The spectral consistency of Landsat 5/7/8 and Sentinel-2A/B imagery as well as the coherency of a Sentinel-2-based spectral library to be used across Landsat and Sentinel-2 sensors served as the foundation for implementing the unmixing approach. We then employed regression-based unmixing using synthetic training data from spectral libraries for developing spatially and temporally generalized models. Applying these models to the Landsat and Sentinel-2 data facilitated multidecadal fractional cover mapping at a national-scale. We evaluated the quality of our multidecadal grassland fractional cover time series using statistical validation and linear correspondence analysis. The statistical validation was based on a multitemporal reference dataset spanning 2017 to 2021, derived from very high-resolution (VHR) imagery. Landsat 7/8- and Sentinel-2A/B-derived fractions showed similar Mean Absolute Errors (MAEs), i.e., 0.067 and 0.08 for PV, 0.149 and 0.15 for NPV, and 0.135 and 0.129 for soil. Linear correspondence analysis confirmed consistent PV and NPV fractional cover estimates among Landsat and Sentinel-2 sensors, suggesting similar errors beyond the statistical validation period. However, higher errors and weaker linear correspondence pointed to remaining uncertainties in soil fractional cover estimates. We further showed that the differences in spatial and spectral resolutions, i.e., the pixel size and the number of spectral bands, between Landsat and Sentinel-2 had a minor effect and were well mitigated by the spectral unmixing approach. We finally illustrated the value of the dense time series available for more recent years for describing seasonal trajectories of grassland conditions and land use intensities, as well as the use of the entire time series for analyzing long-term grassland dynamics based on annual fraction anomalies. Our study emphasizes the efficacy of generalized multisensor spectral unmixing approaches for retrieving consistent PV, NPV, and soil cover fractions across space, time, and sensors, providing a valuable means for grassland monitoring.

A decade-long chlorophyll-a data record in lakes across China from VIIRS observations

Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012−2021) in 217 large lakes (> 50 km2) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (Rrs(λ)) products were reliable over 28 of China's examined lakes (N = 340, bias = −12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1–300 μg L−1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 μg L−1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity.

Surface Reflectance and Aerosol Retrieval from SPOT-VGT and PROBA-V in the Mission Exploitation Platform Environment

Observations acquired by the SPOT-VEGETATION and PROBA-V missions offer a unique opportunity to improve our understanding of the climate, providing global and continuous data over the land surface over 20 years. The possibility of generating a long-term climate data record from the entire archive, stored on the Mission Exploitation Platform (MEP), is here explored. For this purpose, in the framework of the ESA-funded SPAR@MEP project, the Combined Inversion of Surface and Aerosols (CISAR) algorithm has been applied to the SPOT-VGT and PROBA-V archive, following the harmonization of the observations according to the Fidelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) principles. CISAR has been applied to the full 20-year harmonized archive over key areas, as well as to one year of global acquisition from PROBA-V, processed at 5 km resolution, to derive aerosol single-scattering properties and surface reflectance. The retrieval is evaluated in terms of consistency among the three sensors and against reference datasets, including ground-based observations, models, and other sensor products. This activity has revealed the importance of characterizing the radiometric uncertainty for every processed pixel.

The Salinity of the Great Salt Lake and Its Deep Brine Layer

The Great Salt Lake is a highly saline terminal lake with considerable fluctuations in water surface elevation and salinity. The lake is divided into two arms by a railroad causeway. River inflows enter the larger south arm, while the north arm only receives minimal surface runoff. Evaporation from both arms and limited exchange of water and salt through causeway openings result in complex water and salinity processes in the lake. The north arm is typically homogeneous and close to saturation. The south arm is typically stratified with periodic occurrences of a deep brine layer. This paper analyzes the lake’s long-term historical salinity and water surface elevation data record. Its purpose is to better document the movement of salt and changes to salinity in time and space within the lake and the occurrence and extent of its deep brine layer. This work is important because of the lake’s salinity-dependent ecosystem and industries as well as the role played by the deep brine layer in the concentration of salt and contaminants. We documented that the deep brine layer in the south arm is intermittent, occurring only when causeway exchange supports flow from the north to the south arms. We found that the overall mass of salt in the lake is declining and quantified this in terms of mineral extraction records and historical density measurements.

VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.

Evaluation and comparison of VIIRS dark target and deep blue aerosol products over land

The dark target (DT) and deep blue (DB) algorithms have been applied to the VIIRS to construct a long-term climate data recording of atmospheric aerosols. This study provides the first evaluation and comparison of two updated VIIRS aerosol products over global land based on Version 3 Level 1.5 AERONET measurements. Overall, both AOD products agree well with AERONET measurements with correlation coefficients >0.85 and over 75 % of AOD matchups falling within the expected error (EE). The DB product is superior to the DT product with more AOD matchups meeting the EE. Meanwhile, the DB AOD performs better in spatiotemporal accuracy, adaption to different aerosol types, and addresses the effects of large view geometry. DT AOD shows higher accuracy in the summer months in the northern hemisphere and evergreen broadleaf forest areas than DB AOD. The two products exhibit similar spatial distribution patterns of AOD, but higher values are seen in the DT product, especially in Asia and Western North America. The spatial completeness of the DB AOD is higher than DT, especially in brighter surface regions. In contrast, higher temporal completeness of the DT AOD is found in vegetated areas. A case study of the Western United States wildfires in 2020 indicates both products can capture extreme smoke aerosols. In addition, the DB AOD shows a distinct advantage in spatial and temporal continuity and in displaying the regional transport of smoke aerosols. However, the accuracy of the AOD retrieval for both products still needs to be improved in sparse vegetation regions, northern hemisphere summers, fine particle aerosols, and during extreme aerosol events. The overall evaluation of DB SSA and AE products shows that they are unsuitable for quantitative scientific research. This study can provide users a guide on how to select VIIRS aerosol products for different research purposes.

Performance assessment and validation of ocean color sensor-specific algorithms for estimating the concentration of particulate organic carbon in oceanic surface waters from satellite observations

Global observations of ocean color from space provide a unique capability to conduct multi-decadal studies of biogeochemical constituents and processes in the upper ocean layers at the regional, basin, and global scales. The concentration of particulate organic carbon (POC) in the surface ocean waters is one of the biogeochemically important data products derivable from satellite ocean color observations. The data record of standard global POC product is available from NASA and has been obtained from the empirical power-function relationship between POC and a single blue-to-green band ratio of ocean remote-sensing reflectance, Rrs(λ), which is here referred to as the BR-PF algorithm. Recently, we formulated a new suite of satellite ocean-color sensor-specific global POC algorithms that are referred to as hybrid algorithms because they contain two components, one based on the maximum band-ratio (MBR) and the other on the band ratio difference index (BRDI) of reflectance (Stramski et al., 2022). The present study describes the validation analysis and provides performance assessment of hybrid algorithms for SeaWiFS, MODIS, and VIIRS-SNPP satellite ocean color sensors. For comparison, the standard global (BR-PF) algorithms and the color-index (CI) algorithm from the literature were also analyzed. The analyses were made with an in situ validation dataset containing concurrent measurements of POC and Rrs(λ) as well as three satellite validation datasets containing data matchups of field measurements of POC and satellite-derived Rrs(λ) from the SeaWIFS, MODIS-Aqua, and VIIRS-SNPP missions. The hybrid algorithms showed an overall improvement of performance compared to other algorithms, in particular in waters with relatively high POC compared to the standard BR-PF algorithms. The aggregate bias of hybrid algorithm-derived POC is small or negligible (<10%) for the validation datasets, and a measure of median percentage difference is about 20% for the in situ validation dataset and ranges between about 20% and 30% for the satellite validation datasets. In addition, comparisons of POC retrievals from the three sensor-specific hybrid algorithms show good inter-sensor consistency. The validation results of this study indicate that hybrid algorithms have high potential to provide improved POC retrievals within a broader range of POC than the predecessor global algorithms and the capability for generating a consistent long-term POC data record from multiple satellite missions.

A satellite-based burned area dataset for the northern boreal region from 1982 to 2020

Background Fires in the boreal forest occur with natural frequencies and patterns. Burned area (BA) is an essential variable in assessing the impact of climate change in boreal regions.Aims Spatial wildfire occurrence data since the 1950s are available for North America. However, there are no reliable data for Eurasia, mainly for Siberia, during the 1980s and 1990s.Methods A Bayesian-network algorithm was applied to the Long-Term Data Record (LTDR) Version 5 to generate a BA DataSet (BA-LTDR-DS) for the Boreal region from 1982 to 2020, validated using official reference data and compared with the MODIS MCD64A1 product.Key results A high correlation (>93%) with all the reference BA datasets was found. BA-LTDR-DS data grouped by decades estimated a linear increase in BA of 4.47 million ha/decade. This trend provides evidence of how global warming affects fire activity in these boreal forests.Conclusions BA-LTDR-DS constitutes a unique data source for the pre-MODIS era, and becomes a reliable source when other products with higher spatial/spectral resolution are not available.Implications The BA-LTDR-DS dataset constitutes the longest time series developed for the boreal region at this spatial resolution. BA-LTDR-DS could be used as input in global climate models, helping improve wildfire prediction capabilities and understand the interactions between fire, climate and vegetation dynamics.

A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for Estimating Clear-Sky Surface Longwave Upwelling Radiation From MODIS Data

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 16.23 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 18.51 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 17.44 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

A Physical-Based Method for Pixel-by-Pixel Quantifying Uncertainty of Land Surface Temperature Retrieval From Satellite Thermal Infrared Data Using the Generalized Split-Window Algorithm

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’ field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can well characterize the spatial variation in LST uncertainties over various land cover types.

Satellite Observation of the Long-Term Dynamics of Particulate Organic Carbon in the East China Sea Based on a Hybrid Algorithm

The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas.

Assessing the future of an intertidal seagrass meadow in response to sea level rise with a hybrid ecogeomorphic model of elevation change

To assess the effect of sea level rise (SLR) on a 3,800-ha eelgrass meadow in Padilla Bay National Estuarine Research Reserve in Puget Sound, Washington, USA, we coupled the Marsh Equilibrium Model (MEM) with the Relative Elevation Model (REM), combining their respective strengths in simulating aboveground and belowground processes. We then modified the hybrid model to reflect an empirical relationship between eelgrass stem density and sediment accretion, making it the first model of its kind to do so. We used field data to initialize and calibrate the model, then simulated surface elevation change under various SLR and suspended sediment scenarios and tested it against a 12-year surface elevation table dataset from the site. 100-year simulations projected relative elevation loss along at least half of the elevation gradient for all SLR scenarios, and along the entire gradient for three SLR scenarios, with greater loss at higher elevations. The current suspended sediment concentration is thus insufficient for the entire eelgrass meadow to keep pace with SLR, with up to a four-fold increase required, however this presents a management conundrum in that the required sediment load may prevent eelgrass from meeting its light requirements. The main contributions of this study thus include: the novel combination of MEM and REM models, the inclusion of stem density as a factor controlling accretion, the use of a long-term data record for model initialization, calibration, and validation, and the finding that increasing sediment inputs to maintain the elevation of the habitat in the long term may be detrimental to eelgrass health in the short term.

Ocean color algorithms to estimate the concentration of particulate organic carbon in surface waters of the global ocean in support of a long-term data record from multiple satellite missions

As the concentration of particulate organic carbon (POC) in the surface ocean plays a key role in marine biogeochemical cycles and ecosystems, its assessment from satellite observations of the global ocean is of significant interest. To achieve a global multi-decadal data record of POC by merging observations from multiple satellite ocean color missions, we formulated a new suite of empirical POC algorithms for several satellite sensors. For the algorithm development we assembled a field dataset of concurrent POC and remote-sensing reflectance, Rrs(λ), measurements collected in all major ocean basins encompassing tropical, subtropical, and temperate latitudes as well as the northern and southern polar latitudes. This dataset is characterized by a globally-representative probability distribution of POC with a broad range of values between about 10 and 1000 mg m−3. This development dataset was created with the use of additional inclusion and exclusion criteria based on well-assured and documented consistency of measurement protocols as well as specific bio-optical and particle characteristics of seawater which are consistent with vast areas of open-ocean pelagic environments.To formulate the algorithms the development dataset was subject to parametric regression analysis. Overall we evaluated over seventy formulas for estimating POC from Rrs(λ) using seven distinctly different algorithmic categories, each with a fundamentally different definition of independent variable involving Rrs(λ). Through the goodness-of-fit analysis, we selected the best candidate POC algorithms, referred to as the hybrid algorithms, which are tuned specifically for the spectral bands of SeaWiFS, MODIS, VIIRS, MERIS, and OLCI satellite sensors. These hybrid algorithms consist of two components, the MBR (Maximum Band Ratio)-OC4 cubic polynomial function and BRDI (Band Ratio Difference Index) quintic polynomial function. The MBR-OC4 uses four spectral bands and the BRDI three spectral bands from the blue-green spectral region. The MBR-OC4 algorithm is used for POC > 25 mg m−3 and the BRDI for POC < 15 mg m−3. In the transition region the weighting approach is applied to POC derived from the two algorithmic formulas. While the main role of the BRDI is to improve POC estimates in ultraoligotrophic waters where POC is very low, the MBR-OC4 provides improvements, compared with the predecessor algorithms, over a broader range of POC but especially for relatively high POC values. A preliminary analysis of field-satellite matchup datasets based on SeaWiFS and MODIS-Aqua observations shows improved performance of hybrid algorithms compared with current standard algorithms for both SeaWiFS and MODIS. In addition, a reasonable consistency is demonstrated between POC derived from hybrid algorithms applied to example satellite observations with SeaWiFS, MODIS-Aqua, and VIIRS-SNPP. The suite of newly developed algorithms provides the potential next generation version of global algorithms that better represents the spatial and temporal variability within a broader range of POC than the predecessor global algorithms, while also offering a capability to generate a long-term sensor-to-sensor consistent data record of POC that begins with the launch of SeaWiFS mission in 1997.

DINEOF reconstruction for creating long-term cloud-free sea surface temperature data records: A Case study in Lombok Strait, Indonesia

A long-term reliable sea surface temperature (SST) satellite data record is requisite resources for monitoring to understand climate variability. Creating a long-term data record especially for climate variability requires a combination of multiple satellite products. Consequently, missing data issues are inevitable. Hence, DINEOF (Data Interpolating Empirical Orthogonal Functions) has been applied to attain a complete and coherent multi-sensor SST data record with EOF-based technique by reconstructing the missing data. Unfortunately, the technique can lead to large discontinuities in the data reconstruction due to images depiction within long time series data. For that reason, filtering the temporal covariance matrix had been applied to reduce the spurious variability and more realistic reconstructions are obtained. However, this approach has not yet tested in tropical region with higher evaporation which cause incomplete satellite image coverage. Therefore, the objective of this research is to reconstruct SST of Lombok strait with data gaps up to 58.16% in one year. It is successfully reconstructed until the last iteration of 42 optimal EOF modes with the convergence achieved up to 0.9806×10-3, including previous set-aside data for internal cross-validation. The results highlight that the DINEOF method can effectively reconstruct SST data in Lombok Strait.

Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA

Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.

Trends in land surface phenology across the conterminous United States (1982-2016) analyzed by NEON domains.

Tracking phenological change in a regionally explicit context is a key to understanding ecosystem status and change. The current study investigated long-term trends of satellite-observed land surface phenology (LSP) in the 17 National Ecological Observatory Network (NEON) domains across the conterminous United States (CONUS). Characterization of LSP trends was based on a high temporal resolution (3-d) time series of the two-band enhanced vegetation index (EVI2) derived from a long-term data record (LTDR) of the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We identified significant trend patterns in LSP and their seasonal climate and land use/land cover drivers for each NEON domain. Key findings include (1) the start of season (SOS) predominantly shifted later in 13 out of 17 domains (24.3% of CONUS by area) due potentially to both a lack of spring warming in the eastern United States and changes in agronomic practices over agricultural lands; (2) the end of season (EOS) became predominantly later in nine domains dominated by natural vegetation (14.1% of CONUS by area) in response to widespread warming in autumn; (3) the EOS predominantly shifted earlier in three domains (10.6% of CONUS by area) over primarily agricultural lands as potentially affected by changes in crop growth cycles; and (4) earlier shift in the SOS was mostly found in the Northwest (3.6% of CONUS by area) and was predominant only in the moist Pacific Northwest (27.7% of the domain by area) in response to more pronounced spring warming in the region. The overall patterns of SOS and EOS trends across CONUS appeared constrained by continental-scale temperature trends as characterized by a west-east dipole and the distribution of the nation's agricultural lands. In addition, seasonal trend analysis revealed that most NEON domains (15/17) became predominantly greener in part of or throughout the growing season, potentially contributed by both climate change-induced growth increase and improved agricultural productivity. The domain-wide LSP trends with their underlying drivers identified here provide important contextual information for NEON science as well as for investigations within CONUS using other distributed observatories (e.g., LTER, LTAR, FLUXNET, USA-NPN, etc.).