Satellite Sensors Research Articles

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

Evaluating Predictive Models of Tree Foliar Moisture Content for Application to Multispectral UAS Data: A Laboratory Study

Water supply is a critical component of tree physiological health, influencing a tree’s photosynthetic activity and resilience to disturbances. The climatic regions of the western United States are particularly at risk from increasing drought, fire, and pest interactions. Existing methods for quantifying drought stress and a tree’s relative resilience against disturbances mostly use moderate-scale (20–30 m) multispectral satellite sensor data. However, tree water status (i.e., water stress) quantification using sensors like Landsat and Sentinel are error-prone given that the spectral reflectance of pixels are a mixture of the dominant tree canopy, surface vegetation, and soil. Uncrewed aerial systems (UAS) equipped with multispectral sensors could potentially provide individual tree water status. In this study, we assess whether the simulated band equivalent reflectance (BER) of a common UAS optical multispectral sensor can accurately quantify the foliar moisture content and water stress status of individual trees. To achieve this, water was withheld from groups of Douglas-fir and western white pine saplings. Then, measurements of each sapling’s foliar moisture content (FMC) and spectral reflectance were converted to BER of a consumer-grade multispectral camera commonly used on UAS. These bands were used in two classification models and three regression models to develop a best-performing FMC model for predicting either the water status (i.e., drought-stressed or healthy) or the foliar moisture content of each sapling, respectively. Our top-performing models were a logistic regression classification and a multiple linear regression which achieved a classification accuracy of 96.55% and an r2 of 82.62, respectively. These FMC models could provide an important tool for investigating tree crown level water stress, as well as drought interactions with other disturbances, and provide land managers with a vital indicator of tree resilience.

Evaluation of Changes in the Scale and Direction of Thermal Pollution Flows in the Kalinin NPP Cooling Lakes From 1985 to 2020

The article evaluates the change in the scale and direction of thermal pollution flows of the cooling lakes of the Kalinin NPP from 1985 to 2020 based on the use of Earth remote sensing data. A retrospective analysis was carried out using satellite images obtained by the sensors of the Landsat series satellites. An analysis of the distribution of surface water temperature over the water area of NPP cooling lakes has been carried out. The change in the structure of thermal pollution after the increase in the capacity of the Kalinin NPP is determined. The temperature of the cooling lakes of the Kalinin NPP was estimated relative to the background indicators in the lakes-analogues, which were used as nearby Lake Navolok and Lake Kezadra. The study identified five stages of transformation of the water mass circulation system in the cooling lakes of the Kalinin NPP.

Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network

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.

A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing.

Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation

ABSTRACTDespite the importance of the Amazonian rainforests in the global carbon cycle, their phenological responses measured by large field-of-view satellite sensors are still not completely understood. In this scenario, close-to-nadir observations at high spatial and temporal resolutions made by satellite constellations may contribute to improve this knowledge. Here, we investigated the sensitivity of five vegetation indices (VIs) to canopy shadows over 15 protected forests of the Amazon, and the possible existence of coupled effects of solar illumination and vegetation phenology on the VI determination. The VIs are the Enhanced Vegetation Index (EVI), Green-Red Normalized Difference (GRND), Modified Photochemical Reflectance Index (MPRI), Normalized Difference Vegetation Index (NDVI), and RedEdge Normalized Difference (REND). They were calculated from 432 images obtained in 2022 by the Planet’s eight-band SuperDove instrument. Few daily images acquired on the same day with distinct Solar Zenith Angle (SZA) were used to disentangle the effects of solar illumination from those of vegetation phenology. The results showed the presence of coupled effects of solar illumination and vegetation phenology on the EVI determination regardless of the site location, especially over dense forests. Such effects were not observed significantly in the GRND, MPRI, NDVI, and REND data. When the vegetation phenology was kept fixed in the analysis, solar illumination generated pseudo-greening effects from the EVI, even for small differences in SZA between daily observations. As the most sensitive VI to illumination conditions, the EVI increased from the beginning to the end of the dry season tracking solar angles and shade fractions. This dry-season trend was not observed for GRND, MPRI, NDVI, and REND, which presented low correlations with SZA and shade fractions. These four VIs were correlated with each other over most sites, which explained the agreement observed between their seasonal profiles. From the analysis of 15 sites distributed throughout the Amazon, our findings did not confirm patterns of large-scale greening at the end of the dry season. Local changes in greening (vegetation productivity) and browning were captured by the VIs over a few sites but in different periods of the dry season (June to September). At the high spatial scale of SuperDove observations, our results highlight the necessity of correcting solar and, in some cases, terrain illumination effects on the EVI before retrieving phenological metrics over the Amazon.

Evolution of Increased Volcanic Activity in Arjuno-Welirang Based on LST Analysis of Landsat 8 Satellite Imagery Using GEE Cloud Computing

Typically, monitoring the volcanic activity of a volcano is carried out using volcanic seismic methods. However, this method is technically less flexible. Volcano seismic data is not freely available. Access to these data centers must be authorized by the data authority. Therefore, it is necessary to use other methods as an alternative. The alternative method used in this study is remote sensing using the Landsat 8 satellite sensors. Landsat 8 satellite imagery data can be freely accessed and easily downloaded. Landsat 8 image analysis is implemented with Google Earth Engine (GEE). GEE is a remote sensing image analysis programming tool with a cloud computing platform. The GEE programming implementation is open source. With GEE, the evolution of Arjuno-Welirang volcanic activity can be monitored accurately. The use of GEE with a cloud computing platform also makes it easier to process large remote sensing data because the downloaded file's size is unlimited. GEE has successfully conducted an LST analysis on Landsat 8 satellite imagery of the Arjuno-Welirang complex area in the 2016-2021 range. The LST calculation is performed by adding the surface emissivity correction obtained based on the NDVI value. According to the results of the LST calculations that have been obtained, the surface temperature in the Arjuna Welirang Crater area experienced the highest increase in 2018, reaching 33.94 oC, with a larger contour size of thermal distribution image than the others. This increase in thermal based volcanic activity is in accordance with the increase in seismic activity monitored by the VSI (Volcanological Survey of Indonesia).

Water availability and atmospheric dryness controls on spaceborne sun-induced chlorophyll fluorescence yield

Climate change is amplifying the duration, frequency, and intensity of droughts, harming global ecosystems. During droughts, plants can close their stomata to save water, at the expense of a reduced carboxylation rate. When in a carboxylation-limited regime, plants benefit from an increase in water availability, as it increases their photosynthetic rate. The sun-induced chlorophyll fluorescence (SIF) signal, measurable from satellites, is mechanistically linked to this rate. Like canopy photosynthesis, SIF carries an imprint from the available irradiation (PAR) as well as the canopy structure and the efficiency of the photosynthesis at the photosystem level. Normalizing the global TROPOMI SIF observations with TROPOMI reflectance and MODIS Normalised Difference Vegetation Index (NDVI) data, we extracted the fluorescence quantum yield (ϕF), which lab-scale experiments have found to be linked to the photosynthetic electron transport. Plant physiologists have long proved the photosynthetic electron transport to be sensitive to plant water status. Here, the plant water status is controlled by the soil moisture (SM) and the vapour pressure deficit (VPD). Combining data from the TROPOMI, AIRS and SMAP satellite sensors, this study describes how SM and VPD control the ϕF at the global scale. We identify a VPD range (VPD<1.5 kPa) in which the ϕF is mainly controlled by VPD, and another (VPD>1.5 kPa) in which the ϕF is co-regulated by SM and VPD. The precise values of this range, as well as the magnitude of ϕF values, are modulated by the plant isohydricity. To gain a deeper understanding of the link between ϕF and photosynthetic efficiency at large scale, we used the link between ϕF and data on the canopy conductance (Gs), which were calculated using remote sensing data-driven models. A comparison found that the ϕF-Gs relationship at large scale is in line with the ϕF-Gs relationship described in plant-level studies.

Sensor Data Fusion using Kalman Filter for LAPAN-A2 Satellite Attitude Estimation

LAPAN A2 is a Low Earth Orbit (LEO) satellite with the primary mission to monitor the area. In order to fulfil the mission’s objectives, the Satellite attitude must be controlled properly. One of the control processes under consideration is the output attitude estimation. Attitude estimation processes in satellites mostly use satellite sensor output transformation. The estimation process, however, cannot be carried out with only one sensor where the generated transformation does not represent the satellite actual attitude. Thus, a fusion of two or more attitude sensors is needed. However, measurement noise, such as the effect of the Earth's albedo on the sun sensor and the interference of the Earth's magnetic field on the magnetometer, is incorporated into the estimation process. Thus, a filtering process is needed. Hence, this study aims to investigate the utilization of the sensor output raw data fusion combined with the Kalman Filter algorithm to estimate the LAPAN-A2 satellite attitude in the form of Euler angle.

Remote sensing and analysis of tropical cyclones: Current and emerging satellite sensors

This article describes recent advances in the capability of new satellite sensors for observing Tropical Cyclones (TC) fine structure, wind field, and temporal evolution. The article is based on a World Meteorological Organization (WMO) report prepared for the 10th International Workshop on Tropical Cyclones (IWTC), held in Bali in December 2022, and its objective is to present updates in TC research and operation every four years. Here we focus on updates regarding the most recent space-based TC observations, and we cover new methodologies and techniques using polar orbiting sensors, such as C-band synthetic aperture radars (SARs), L-band and combined C/X-band radiometers, scatterometers, and microwave imagers/sounders. We additionally address progress made with the new generation of geostationary and small satellites, and discuss future sensors planned to be launched in the next years. We then briefly describe some examples on how the newest sensors are used in operations and data assimilation for TC forecasting and research, and conclude the article with a discussion on the remaining challenges of TC space-based observations and possible ways to address them in the near future.

2.5D Time Series of Postseismic Deformation Following the 2018 Mw 7.5 Palu Earthquake Using Sentinel-1 InSAR

The observed Displacement of Line of Sight (LOS) InSAR can only measure the displacement towards and away from the satellite sensor, making it difficult to provide an intuitive depiction of the deformation source. In the previous studies, research on the characteristics of postseismic deformation mechanisms was conduct by InSAR but only in the vertical component. Therefore, our study attempts to complement this deformation picture by considering both the horizontal and vertical components. It is hoped that this will provide a more comprehensive understanding of the postseismic deformation mechanism of the 2018 Palu earthquake. In this study, we attempted to estimate the 2.5-D surface deformation resulting from the 2018 Palu Earthquake using Sentinel-1 data. The data was then modeled using exponential and logarithmic functions to understand the characteristics of the postseismic deformation mechanism. The 2.5-D surface deformation exhibited variations in horizontal and vertical motions. The East-West (EW) displacement values showed a maximum value of -66.19 mm in the east direction, while the maximum value in the west direction is 75.23 mm. On the other hand, for the Up-Down (UD) displacement, there was a maximum subsidence of -73.40 mm and a maximum uplift of 67.28 mm. To study the characteristics of the transient postseismic deformation, observations were made at 14 points. Postseismic deformation was observed at all locations in the study area. During the period 2018-2021, the time series of the north and east components of the postseismic transients were analyzed using logarithmic and exponential functions. The modeling results showed that the Root Mean Square Error (RMSE) value for the exponential model is 81.80 mm, while for the logarithmic model is 81.38 mm. Therefore, the logarithmic model demonstrated a better fit, indicating that the postseismic deformation mechanism is influenced by afterslip.

Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity

Satellite-derived Ecosystem Functional Types (EFTs) are increasingly used in ecology and conservation to characterize ecosystem heterogeneity. The diversity of EFTs, also known as Ecosystem Functional Diversity (EFD), has been suggested both as a potential metric of ecosystem-level biodiversity and as a predictor for ecosystem functioning, ecosystem services, and resilience. However, the impact of key methodological choices on patterns of EFTs and EFD have not been formally assessed. Using Costa Rica as a study system, we compared EFTs and EFD, derived from MODIS and Landsat data using different methodological assumptions, at both national and local extents. Our results showed that the regional spatial patterns of EFTs and EFD derived from 250 m MODIS and 30 m Landsat are notably different. The selection of sensors for deriving EFTs and EFD is dependent on the study area, data quality, and the research objective. Given its finer spatial resolution, Landsat has greater capacity to differentiate more EFTs than MODIS, though MODIS could be a better choice in frequently cloudy areas due to its shorter revisiting time. We also found that the selection of spatial extent used to derive EFD is critical, as smaller extents (e.g., at a local rather than a national scale) can show much higher diversity. However, diversity levels derived at smaller extents appear to be nested within the diversity levels derived at larger extents. As EFTs and EFD continue to develop as a tool for ecosystem ecology, we highlight the important methodological choices to ensure that these metrics best fit research objectives.

Co-seismic Ground Deformation Monitoring on Brebes-Segment of Baribis-Kendeng Fault Zone Based on Sentinel-1 Data

Co-seismic deformation is a sudden change in the earth’s surface in a fault zone. Therefore, with modern satellite imagery, it is possible to investigate. Synthetic Aperture Radar (SAR) data from Mission Sentinel-1 is one example of satellite imagery that can be used to identify deformation. We employ the interferometry method by utilizing the Interferometric synthetic aperture radar Scientific Computing Environment (ISCE) and another InSAR software to monitor the co-seismic ground deformation in the form of line-of-sight (LOS) displacement. This research focused on the Brebes Segment of the Baribis-Kendeng Fault Zone and applied the interferometry method for the March 22, 2015, Balapulang earthquake (M4.7) and December 11, 2020, Brebes earthquake (M4.2). The earthquake event choice is based on the shallow depth of the hypocenter, which represents the earthquake originating from the fault in the crustal, not the megathrust subduction. The results show that despite the earthquake occurring under the thick quaternary sediment of the north shore of Java Island, the deformation caused by the co-seismic is still emerging to the surface. The LOS displacement was detected within the range of 6 cm from the ascending and descending track of the satellite sensors. Furthermore, this result may provide new information on the Baribis-Kendeng Fault zone, specifically the Brebes Segment.

Estimation of national sources and sinks of greenhouse gases based on satellite observations

Human-driven Greenhouse gases (GHGs) are the most significant contributor to climate change. World countries and Egypt are moving towards achieving sustainable development goals (SDGs) 2030, and 2050, to reach Net-Zero emissions. Based on satellite observations, this research assesses and monitors the GHG emissions induced by human activities in Egypt. Different satellite sensors were utilized in this study to obtain Methane (CH4), Carbon Dioxide (CO2) amounts during 2015–2022. To get a deeper insight into the effects of anthropogenic activities on CO2 and CH4 amounts, they were correlated with land use and land cover, fire incidents, and industrial activities in Egypt. Results revealed a noticeable increase in CH4 and CO2 emissions over the country with a maximum level in 2022. CO2 has a seasonal variation mode, with the highest amounts in spring reaching 0.000409 CO2/mol dry-air. As well, the high CH4 concentration fluctuates all the year-round, with a peak around 1890 ppbv in August. The high levels of GHGs mostly concentrated in the Nile Delta and Nile Valley, where most of the anthropogenic activities are existing. Fire incidents, industries, and land cover change maps showed a spatial matching with the high emission zones. However, the emissions are increasing in Egypt it does not exceed the global average. In conclusion, unmanaged human activities in Egypt increased GHGs release and affected environmental sustainability. This study attempts to better understand the ambient environment in Egypt and support the decision-makers with full insight into the GHG emission hotspots in the country to mitigate their release into the atmosphere and achieve Net-Zero emissions.

Anthropogenic landscape change and amphibian diversity in tropical montane biodiversity hotspots: insights from satellite remote sensing in the Madagascar highlands

AbstractThe magnitude of anthropogenic landscape change in tropical montane biodiversity hotspots and its relationship with biodiversity is a global issue that remains ‘locked-in’ in the broad narrative of tropical change in Africa. Over a montane biodiversity hotspot of Madagascar highlands (Ankaratra Massif), we conducted analysis on land cover change with Landsat satellite sensor data to identify the magnitude of change (1995–2016) and on the habitat change–amphibian diversity relationship to understand links with biodiversity. The results evidenced that 17.8% of the biodiversity hotspot experienced change in land cover in only 20 years. That pressured the already threatened forests, particularly since 2005. Of the total forest area in 1995 (2062.7 ha), 21.5% was cleared by 2016 (1618.3 ha). Changes in forest cover followed a bidirectional pattern. While in the period 1995–2005, forests expanded at a rate of 2.0% year−1 (from 2062.7 to 2524.8 ha), the area declined between 2005 and 2016 at a rate of − 4.1% year−1, fourfold the rate reported nationally for Madagascar (− 1.1% year−1). Forest-to-shrubland transitions emerged as being of increasing concern to forest integrity. We identified a significant link between habitat change and amphibian diversity, but only for species richness. Counter to expectations, no significant relationship was found between species richness and deforestation rates, and between microendemism rates and any of the habitat change variables. Species richness responded to the spatiotemporal variability in vegetation dynamics represented by the standard deviation of the Normalized Differenced Vegetation Index (NDVI_std). Species richness was strongly negatively related to NDVI_std in the short-term (R2 = 0.91, p = 0.003) and long-term (R2 = 0.69, p = 0.03), increasing where the spatiotemporal variability in NDVI was lower. The magnitude of changes in this biodiversity hotspot suggests that region-specific assessments are necessary in the context of the tropical change narrative in Africa and should consider conservation policies tailored for local conditions. Reducing deforestation and land conversion rates through a management plan codesigned with local communities is urgent. Habitat change appears to impact on amphibian diversity by altering the functional attributes of the habitat and not just by reducing habitat extent. NDVI_std seems a relevant indirect metric for monitoring such change although other biophysical attributes obtained from satellite sensor data should be integrated and explored.

Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy.

Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5)

Abstract. Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks

Satellite sensors like Landsat 8 OLI (L8) and Sentinel-2 MSI (S2) provide valuable multispectral Earth observations that differ in spatial resolution and spectral bands, limiting synergistic use. L8 has a 30 m resolution and a lower revisit frequency, while S2 offers up to a 10 m resolution and more spectral bands, such as red edge bands. Translating observations from L8 to S2 can increase data availability by combining their images to leverage the unique strengths of each product. In this study, a conditional generative adversarial network (CGAN) is developed to perform sensor-specific domain translation focused on green, near-infrared (NIR), and red edge bands. The models were trained on the pairs of co-located L8-S2 imagery from multiple locations. The CGAN aims to downscale 30 m L8 bands to 10 m S2-like green and 20 m S2-like NIR and red edge bands. Two translation methodologies are employed—direct single-step translation from L8 to S2 and indirect multistep translation. The direct approach involves predicting the S2-like bands in a single step from L8 bands. The multistep approach uses two steps—the initial model predicts the corresponding S2-like band that is available in L8, and then the final model predicts the unavailable S2-like red edge bands from the S2-like band predicted in the first step. Quantitative evaluation reveals that both approaches result in lower spectral distortion and higher spatial correlation compared to native L8 bands. Qualitative analysis supports the superior fidelity and robustness achieved through multistep translation. By translating L8 bands to higher spatial and spectral S2-like imagery, this work increases data availability for improved earth monitoring. The results validate CGANs for cross-sensor domain adaptation and provide a reusable computational framework for satellite image translation.

Implementing a Dual-Spectrometer Approach for Improved Surface Reflectance Estimation

Surface reflectance measurement is an integral part of the vicarious calibration of satellite sensors and the validation of satellite-derived top-of-atmosphere (TOA) and surface reflectance products. A well-known practice for estimating surface reflectance is to conduct a field campaign with a spectrometer and a calibration panel, which is labor-intensive and expensive. To address this issue, the Radiometric Calibration Network, RadCalNet, has been developed, which automatically collects surface reflectance over several selected sites. Neither of these approaches can continuously track the atmosphere, which limits their ability to compensate for atmospheric transmittance change during target measurement. This paper presents the dual-spectrometer approach that uses a stationary spectrometer dedicated to continuously tracking changes in atmospheric transmittance by staring at a calibrated reference panel while the mobile spectrometer measures the target. Simultaneous measurement of the reflectance panel and target help to transfer calibration from the stationary spectrometer to the mobile spectrometer and synchronize the measurements. In this manner, atmospheric transmittance changes during target measurement can be tracked and used to reduce the variability of the target surface reflectance. This paper uses field measurement data from combined field campaigns between different calibration groups at Brookings, South Dakota, and Landsat 8 and Landsat 9 underfly efforts over Coconino National Forest, Arizona, and Guymon, Oklahoma. Preliminary results show that even in a clear sky condition, where atmospheric transmittance changes are minimal, the precision of target surface reflectance estimated using the dual-spectrometer approach is 2–6% better than the single-spectrometer approach. The dual-spectrometer approach shows the potential for a substantial improvement in the precision of the target spectral profile when the atmospheric transmittance is changing rapidly during field measurement. Results show that during non-optimal atmospheric conditions, the dual-spectrometer approach improved the precision of the surface reflectance by 50–60% compared to the single-spectrometer approach across most spectral regions. The ability to estimate surface reflectance more precisely using the dual-spectrometer approach in different atmospheric conditions improves the vicarious calibration of optical satellite sensors and the validation of both TOA and surface reflectance products.

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.