Frontiers in Remote Sensing

A novel building metric for PlanetScope© data optimization

2026-04-01

Mona Morsy, Silas Michaelides, Peter Dietrich

PlanetScope© data are being widely used by the scientific community in addressing important environmental issues, including cropland and tree cover loss, burned area mapping post-wildfire, imagery for river water masking, and snow-covered area mapping. However, building indices’ applications on PlanetScope data for use in separating blocks and to identify the difference between altered/unaltered constructed areas are not available, primarily due to the absence of the Short-Wave Infrared (SWIR) band. In this research, three types of remote sensing data series are employed, namely, PlanetScope images, Sentinel-2A data, and Google Earth Pro data, to separate the blocks and to capture the differences between damaged and undamaged blocks in the Khan Younis town of the Gaza Strip, utilizing a novel building metric purposely developed to be used with PlanetScope data. Additionally, three change detection methodologies are utilized to assess the efficacy of the new metric, namely, image differencing, Principal Component Analysis (PCA), and metrics, including the one proposed in this paper. The proposed metric exhibited high performance with the PlanetScope data compared to Sentinel-2A data. The results of the three methodologies indicate a strong correlation, with an R-value of 0.793,263 and a P-value of 0.0002456. The average sizes of the affected areas, derived from PlanetScope data from October 2023 to April 2024, are 0, 0.05, 2.4, 2.9, 5.2, 12.8, and 13.1 km2. The Sentinel data from October to January shows that the average sizes of the devastated regions are 0, 0.9, 2.1, and 7 km2, while the rest of the data series was concealed and unavailable by Google Earth Engine (GEE). The accuracy assessment test conducted with the Google Earth Pro scenes to evaluate the strength of the metric showed a considerably strong percentage of compatibility with PlanetScope data. The new proposed building metric yields results that are very close to the other techniques adopted, with slight variations but clearer visual outputs.

Estimating flavonoids using radiative transfer model inversion on imaging spectroscopy data

2026-03-20

Bongokuhle S’phesihle Sibiya, Moses Azong Cho, Onisimo Mutanga, John Odindi, Cecilia Masemola, Johannes Van Staden

Assessing flavonoid content is crucial for understanding plant responses to biotic and abiotic stresses. However, pigments such as chlorophylls, carotenoids, and anthocyanins, absorb light between 410 and 430 nm, thus makes it difficult to quantify flavonoid content in this region. To improve the sensitivity of flavonoids have been developed, but they are largely empirical and lack transferability across environments. Therefore, this study compares the effectiveness of the PROSAIL-D radiative transfer model (RTM) and an empirical approach in mapping flavonoids using AVIRIS data in the Fynbos biome through indices. Results show that PROSAIL-D consistently outperformed the empirical method, with the spectral index FI417,693 achieving an R2 of 0.63 and RMSE of 0.24 (mg catechin (CAE)/g). The empirical approach with the same index yielded an R2 of 0.46 and RMSE of 5.11 mg CAE/g. Overall, the RTM model demonstrated superior accuracy, with RMSE values ranging from 0.24 to 1.70, compared to 5.11 to 6.28 for empirical methods. These findings highlight the potential of integrating AVIRIS data with PROSAIL for non-destructive, remote sensing-based assessment of flavonoids, offering a scalable approach for monitoring plant health and stress responses at the canopy level.

Study of the changes in the Curonian Lagoon shoreline strip (1995–2024), Lithuania

2026-03-19

Sérgio Lousada, Dainora Jankauskienė, Giedrė Ivavičiūtė, Lina Kuklienė, Indrius Kuklys, Birutė Ruzgienė, Vivita Pukite

Coastal lagoons are among the most vulnerable aquatic environments to climate change and human pressures; therefore, studying the Curonian Lagoon is crucial to support evidence-based management and improve understanding of shoreline responses to hydro-meteorological forcing and local land-use pressures. This research investigates the evolution of the Curonian Lagoon shoreline near Preila (Neringa municipality) from 1995 to 2024 using a multi-temporal orthophotographic series (ORT10LT) complemented by a very high-resolution UAV orthomosaic produced in 2024. Shoreline position was consistently delineated and compared across eight observation periods to quantify section-based displacement and hotspot area changes. The analysis reveals a spatially organized pattern, with a persistent accumulation-prone stretch in the mid-profile (250–350 m) and a persistent erosion hotspot toward the latter shoreline (400–566 m). The maximum shoreline retreat reached 32.80 m (425 m section, 2024 relative to 1995–1999), while the maximum shoreline advance reached 18.22 m (275 m section, 2021–2023). Area-based hotspot metrics indicate erosion losses up to 654 m2 (2018–2020) and accumulation gains up to 395 m2 (2009–2010) relative to the baseline. These results provide a reproducible, decision-oriented shoreline-change characterization that supports targeted monitoring and management of this culturally and environmentally significant lagoon margin.

Case 2 Regional Coast Colour: a neural network-based framework for atmospheric correction and in-water retrievals across multiple ocean colour satellite sensors

2026-03-18

Dagmar Müller, Martin Hieronymi, Ana B. Ruescas, Marco Peters, Rüdiger Röttgers, Marcel König, Carole Lebreton, Kerstin Stelzer, Carsten Brockmann, Roland Doerffer

Since the 1990s, Doerffer and Schiller have been developing physics-based neural network algorithms for analyzing ocean colour in satellite imagery of optically complex coastal waters. At its core, the approach uses neural networks to solve the inversions in various aspects of solar radiative transfer in both the atmosphere and water, including atmospheric correction, towards the estimation of inherent optical properties (IOPs) of the water constituents. Empirical bio-optical models are then applied to derive constituent concentrations from these IOPs. Over the years, this algorithm has evolved significantly and is now widely recognized as Case-2 Regional CoastColour (C2RCC), a trusted tool within the ocean colour research community. Originally designed for the MERIS sensor aboard ENVISAT, C2RCC is now the operational ground segment processor for generating Case-2 (complex) water products from Sentinel-3 OLCI data and from Sentinel-2 MSI data in the Copernicus Marine High Resolution Ocean Colour Service. Adaptations of the algorithm have also been developed for other satellite missions, including SeaWiFS, MODIS, VIIRS, Landsat OLI, and Sentinel-2 MSI. The C2RCC processor is freely accessible through the Sentinel Application Platform (SNAP). This article provides an overview of the background and evolution of the C2RCC algorithm, presenting validation results at coastal sites and in land waters alongside user performance evaluations analyzing the influence of system vicarious calibration gains. It highlights cases where the algorithm delivers reliable results as well as its limitations and areas for future improvement. In its current iteration for Sentinel-3 OLCI, C2RCC performs effectively, particularly in moderately absorbing or scattering Case-2 waters.

Spectral assessment of nutrient limitation in the savanna landscape: selection of spectral indices towards Sentinel-2 upscaling

2026-03-17

Nasiphi Ngcoliso, Abel Ramoelo, Philemon Tsele, Mcebisi Qabaqaba, Siyamthanda Gxokwe

Nutrient limitations can significantly impact the ecosystem services provided by the savanna biome, potentially leading to degradation and reduced grazing capacity if not detected in time. A key indicator of growth-limiting nutrients is the Nitrogen to Phosphorus (N:P) ratio. However, grass foliar phosphorus content has rarely been studied in African savannas, especially using remote sensing approaches. As a result, there is limited information on the spatial distribution of nutrient limitations in these ecosystems. This study aimed to develop a Sentinel-2-based machine learning regression model to predict and map the distribution of the N:P ratio in the northern region of Kruger National Park (KNP), South Africa, which is dominated by the savanna rangeland biome. Fieldwork was conducted between 15 March and 30 April 2008 to collect grass samples and spectral data using an Analytical Spectral Device (ASD). The hyperspectral field data were then resampled to match the multispectral configuration of Sentinel-2 imagery. A Random Forest Regression (RFR) technique was applied to the simulated Sentinel-2 datasets to develop predictive models of the N:P ratio. Model accuracy was evaluated using the Root Mean Square Error (RMSE) Relative Root Mean Square Error (RRMSE), Percent Bias (PBIAS), and the coefficient of determination (R2). The results showed that vegetation indices (VIs), particularly the Normalized Difference Red Edge (NDRE) derived from Sentinel-2 bands B8 and B5, was optimal for estimating N:P ratio. This index explained over 80% of the N:P variability, with the lowest PBIAS of 0.02%. The best-performing model was used to map nutrient limitations across the study area using Sentinel-2 imagery. The spatial analysis indicated consistent nitrogen limitation and co-limitation across the investigated regions, with no evidence of phosphorus limitation. The high-accuracy models demonstrate the effectiveness of Sentinel-2 imagery for estimating nutrient limitations in heterogeneous savanna landscapes. This study offers a cost-effective, scalable tool for decision-makers involved in the management, sustainability, and restoration of the savanna biome. Future research should consider incorporating textural and environmental variables to enhance model performance and understanding of nutrient dynamics.

Mapping small-sized logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset

2026-03-11

Audrey Mercier, Julie Betbeder, Frédéric Mortier, Nicolas Barbier, Pierre Ploton, Guillaume Cornu, Pierre Couteron

Deforestation and forest degradation are the main threats to biodiversity and carbon stocks in tropical forests. Advances in optical and SAR satellite sensors have enabled the development of real-time monitoring of deforestation on a global scale. SAR is particularly appealing in tropical areas due to its insensitivity to cloud cover. However, the automatic detection of small disturbed areas (such as individual tree felling gaps) remains a major challenge. Thanks to a unique dataset consisting of 23,759 locations of individual tree felling gaps and multi-date drone lidar acquisitions, we evaluated the potential of Sentinel-1 dense time series for monitoring small-sized forest disturbances substantially smaller than 0.1 ha on both FSC-certified and artisanal logging sites in the Congo Basin. We designed a new method for forest monitoring using the fused-lasso technique optimized to detect abrupt changes of at least 0.02 ha in Sentinel-1 time series using the fused-lasso technique (Fused-Lasso Change Detection, FLCD). We assessed our new method along with the Cumulative Sum (CuSum) that also proved promising for detecting small impacts, referring for the first time to precise disturbance dates over large areas. Both approaches reached similar rates of confirmed felling gaps that were similarly increasing with gap size, and similar rates of unconfirmed detected gaps. The FLCD method estimates the dates of tree felling more accurately in FSC-certified areas (−2 days difference for FLCD and −19 for CuSum on average). The effective resolution of the S-1 images limits detection for the smallest gaps, yet the approach can help detect and monitor degradation fronts. Fused lasso regression is relevant for modeling the temporal trajectories of the radar signal, which will allow taking advantage of both the increasing availability of UAV-borne data and the lengthening of the S-1 image series.

Editorial: Detection and characterization of unidentified underwater biological sounds, their spatiotemporal patterns, and possible sources

2026-03-03

Lucia Di Iorio, Audrey Looby, Francis Juanes, Tzu-Hao Lin, Zhongchang Song, Jenni Stanley, Miles J. G. Parsons

Graphical AbstractInfographic illustrating research themes in unidentified underwater biological sounds, featuring animal silhouettes surrounded by arrows pointing to images representing identifying, finding, understanding, and using sounds, along with maps, graphs, and labeled research categories and references.

Editorial: Earth observations from the deep space: 10 years of the DSCOVR mission

2026-03-02

A. Lyapustin, A. Marshak, A. Szabo

Crop type mapping in the pre-Sentinel era using variable-length Landsat time-series and self-supervised learning

2026-02-20

Jayan Wijesingha, Ilze Beila

Crop type mapping is crucial for agricultural land cover monitoring and decision-making. State-of-the-art methods developed using recent Sentinel satellite data have already demonstrated their ability to accurately map crop types. However, crop type mapping for the pre-Sentinel era remains challenging due to the limited availability of higher spatial- and temporal-resolution data. This study addresses this knowledge gap by leveraging variable-length Landsat satellite time-series (L-SITS) data in combination with a self-supervised learning model, SITS-BERT, for crop type mapping. This case study, conducted in two German districts, demonstrates the potential of mapping two different crop type levels (CTL1 - 5 and CTL2 - 9 classes) in the pre-Sentinel era. The SITS-BERT model, pre-trained on unlabelled L-SITS data, was fine-tuned on single-year and 3-year datasets and evaluated using past and future years’ data, compared with the model’s training data. The SITS-BERT model achieved overall accuracies of 0.78–0.83 and 0.64–0.76 for CTL1 and CTL2, respectively, with fine-tuning on single-year data. The model fine-tuned with 3 years achieved higher accuracies (0.81–0.85 and 0.72–0.78). The results showed that the SITS-BERT model finetuned with single-year data outperforms the baseline random forest model trained on single-year fixed-length L-SITS data. The study highlighted that, with this approach, limited number of available SITS observations can still be useful. The findings of this study demonstrated the potential of the SITS-BERT model with L-SITS data for crop-type mapping in the pre-Sentinel era, contributing to a more comprehensive understanding of agricultural land cover dynamics and to the evaluation of agricultural policy impacts.

NISTAR measurements confirm basic aspects of EPIC-derived global-scale dayurnal variability in Earth’s reflected radiation

2026-02-11

Andrew A. Lacis, Gary L. Russell, Barbara E. Carlson, Wenying Su, Yinan Yu

A unique model/data comparison capability is made possible by the unique viewing geometry from NASA’s DSCOVR Mission Lissajous orbital location around the Lagrangian L1 point. The key point of this unique location is the large orbital inclination relative to the perpendicular of the Sun-Earth line-of-sight. This circumstance enables periodic Sun-Earth-Satellite phase angle shifts ranging from 2-degrees to 12-degrees with repeating ∼3-month periodicity. At such extreme phase angles, backscattered radiation for spherical cloud-top particles is strongly phase angle dependent, but not for irregular-shaped ice particles. Also key, are the near-hourly high-resolution EPIC images that have been converted to radiative solar fluxes by extensive use of ancillary satellite data and CERES-based ADMs. These EPIC-derived SW fluxes, integrated over the Earth’s sunlit hemisphere, constitute the EPIC Composite dataset of 1-day resolution global-scale reflected SW fluxes, which have been shown to agree well with CERES reflected SW fluxes. Using the EPIC data as a template, the DSCOVR satellite ephemeris enables aggregation of climate GCM run-time output over the sunlit hemisphere with the same viewing geometry as EPIC. Generating the GCM-equivalent global-scale SW flux dataset, together with the EPIC data, forms the basis for a new paradigm in model/data comparisons. The key advantages of this DSCOVR-style approach are the (1) identical space-time sampling with identical viewing geometry and complex, but identical averaging over the diurnal cycle between observations and climate GCM output data, (2) preservation of short-period variability at 1-day resolution due to the Earth’s rotation, and (3) self-consistent weather noise suppression by identical averaging over the sunlit hemisphere. Early examples of the EPIC data variability drew concerns from colleagues worried that the variability in the EPIC data might be modeling noise. There is no other way to resolve this concern but to find another data source that shows the same degree of variability. Definitive comparisons to NISTAR measurements presented in this study unequivocally confirm that the global EPIC-derived variability is indeed real, and not a data artifact.

SuperDove radiometric data assessment in coastal and inland waters

2026-02-02

Ilaria Cazzaniga, Ana I. Dogliotti, Susanne Kratzer, Frédéric Mélin

The use of high-resolution data in aquatic applications increased significantly in the last decade with the launch of decametre-scale optical sensors. More recently, commercial very-high resolution (VHR) sensors, offering finer spatial and temporal resolutions, have shown the potential of complementing data from high-resolution missions. Planet SuperDove (SD), with a band-setting similar to the Copernicus Sentinel-2 MultiSpectral Instrument (S2-MSI), a 3-m spatial resolution and quasi-daily revisiting time, show the potential for widening water monitoring applications to smaller water basins, and finer-scale phenomena. However, the uncertainties in SD products need to be quantified, to assess their fitness-for-purpose for these applications. This work aims to provide uncertainty estimates for SD-derived aquatic remote sensing reflectance (RRS) in different water types, benefitting from the radiometric measurements of the AERONET-OC network. RRS was derived from both Surface Reflectance (SR) products, distributed by Planet, or from data processed with ACOLITE. The comparability between SD and S2-MSI products was also assessed comparing RRS and Rayleigh-corrected reflectance (RRC) from S2-MSI and SD. The results indicate generally low performance across all bands for both SD RRS products, except in the most turbid waters, and highlight the lack of a publicly available robust atmospheric correction processor for SD data for most optical water types. The comparison to S2-MSI shows promising results only when comparing RRC values, but differences still suggest issues associated with calibration and radiometry of the SD sensors. The results also highlight the need for a harmonization strategy to ensure consistent integration of these datasets within multi-source monitoring systems.