Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Remote Sensing of Environment, Год журнала: 2024, Номер 304, С. 114048 - 114048

Опубликована: Фев. 16, 2024

Язык: Английский

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Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Coasts, Год журнала: 2024, Номер 4(1), С. 127 - 149

Опубликована: Фев. 26, 2024

Mapping coastal regions is important for environmental assessment and monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) present more advantageous solutions pattern-finding tasks such as the automated detection of landscape patches heterogeneous landscapes. This study aimed to discriminate patterns along eastern coasts Mozambique ML modules Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm module ‘r.learn.train’ was used map landscapes shoreline Bight Sofala, remote sensing (RS) data at multiple temporal scales. dataset included Landsat 8-9 OLI/TIRS imagery collected dry period during 2015, 2018, 2023, which enabled evaluation dynamics. supervised classification RS rasters supported by Scikit-Learn package Python embedded GRASS Sofala characterized diverse marine ecosystems dominated swamp wetlands mangrove forests located mixed saline–fresh waters coast Mozambique. paper demonstrates advantages areas. integration Earth Observation data, processed decision tree classifier land cover characteristics recent changes ecosystem Mozambique, East Africa.

Язык: Английский

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Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery

Remote Sensing of Environment, Год журнала: 2024, Номер 309, С. 114224 - 114224

Опубликована: Май 28, 2024

High-spatiotemporal-resolution leaf area index (LAI) data are essential for sustainable agro-ecosystem management and precise disturbance detection. Previous LAI products were primarily derived from satellite with limited spatiotemporal or spectral resolutions, which could be overcome the use of Sentinel-2. While hybrid methods that integrate PROSAIL simulations machine learning offer advantages in extracting high-spatiotemporal-resolution Sentinel-2, they still face challenges due to confounding factors related canopy structure, biochemistry, soil background. To reduce impacts these confounders, we developed an efficient method Sentinel-2-based retrieval. Our approach consists random forest models trained on simulated datasets generated by PROSAIL-5B two refinements: variable fraction fully senescent leaves (FS) bidirectional reflectance factor (BRF) Brightness-Shape-Moisture (BSM) model. We corrected BRF using near-infrared vegetation (NIRV) cover within mixed pixels (VC). For validation, used ground measurements across different types Copernicus Ground Based Observations Validation (GBOV) Korea flux (KoFlux) sites during 2019–2023. results showed coupling BSM FS improved estimates, reducing RMSE 10.8%–73.8%. Utilizing NIRV VC correct better quantified most types, reduced 15.3%–64.8%. robust agreement validation GBOV (R2 = 0.88, 0.71) KoFlux 0.80, 0.75). Overall, our 0.58–0.93, 0.04–0.83) outperformed both benchmark Sentinel Application Platform 0.11–0.85, 0.28–1.67) data-driven 0.09–0.85, 0.29–0.93) algorithms producing seasonal at finer resolutions. findings underscore potential proposed retrieval diverse ecosystems.

Язык: Английский

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Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges

Science of Remote Sensing, Год журнала: 2024, Номер 10, С. 100152 - 100152

Опубликована: Июль 27, 2024

For many applications, raw satellite observations need to be converted high-level products of various essential environmental variables. While numerous are available at kilometer spatial resolutions, there few global high resolutions (10–30 m), which also referred fine or medium in the literature. To facilitate development more resolution products, this paper systematically reviews state-of-the-art progress on inversion algorithms and publicly regional products. We begin with an inventory high-resolution data, then present different for determining cloud masks, estimating aerosol optical depth, performing atmospheric correction topographic land surface reflectance retrieval. The majority existing 18 variables four major categories: 1) Land radiation, including broadband albedo, temperature, all-wave net radiation; 2) Terrestrial ecosystem variables, leaf area index, fraction absorbed photosynthetically active fractional vegetation cover, forest tree height, above-ground biomass gross primary production, agricultural crop yield; 3) Water cycle cryosphere, soil moisture, evapotranspiration, snow cover; 4) types, such as impervious surface, inland water, type, fire. Since over large regions usually spatially discontinuous due contamination, data fusion assimilation some producing seamless temporally continuous presented. In end, we discuss a variety challenges generating

Язык: Английский

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Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Remote Sensing, Год журнала: 2024, Номер 16(15), С. 2760 - 2760

Опубликована: Июль 28, 2024

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity coastal wetland ecosystems. Conducting extensive field surveys in wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) satellite remote sensing have been widely utilized to estimate regional AGB. However, mixed pixel effects hinder precise estimation AGB, while high-spatial resolution UAVs face challenges estimating large-scale To fill this gap, study proposed an integrated approach for AGB using sampling, UAV, Sentinel-2 data. Firstly, based on multispectral data from vegetation indices were computed matched with sampling develop Field–UAV model, yielding results at UAV scale (1 m). Subsequently, these upscaled (10 Vegetation calculated establish UAV–Satellite enabling over large areas. Our findings revealed model achieved R2 value 0.58 0.74 scale, significantly outperforming direct modeling (R2 = −0.04). The densities Xieqian Bay, Meishan Hangzhou Zhejiang Province, 1440.27 g/m2, 1508.65 1545.11 respectively. total quantities estimated be 30,526.08 t, 34,219.97 296,382.91 This underscores potential integrating accurately assessing regions, providing valuable support conservation management

Язык: Английский

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Developing aboveground biomass yield curves for dominant boreal tree species from time series remote sensing data

Forest Ecology and Management, Год журнала: 2024, Номер 561, С. 121894 - 121894

Опубликована: Апрель 25, 2024

Forest aboveground biomass (AGB) is an important attribute informing on carbon storage, forest function, and habitat condition. Accurate knowledge of current AGB its dynamics essential for sustainable management monitoring. Common methods estimating AGB, such as permanent sample plots, yield curves, or simulations, often fail to adequately capture the spatial distribution structural complexity attributes. To address these limitations, we present integrated model-driven, data-informed approach developing curves exclusively from remotely sensed data, including annual time series data Landsat informed values, tree species composition, age. We applied this a 76.5 million-hectare study area, encompassing diverse conditions, species, ages, partitioned into 34 150 × 150-km analysis tiles account local variation. The 37-year (1984–2021) were filtered create representative noise-reduced set remote sensing-derived (RSYC). Using nonlinear mixed-effects modeling framework, generated 127 RSYC models eight across area. Developed offered insights different types conditions. performance was evaluated using three independent datasets: existing established growth simulator. Assessment showed influence geographic position representation in reference data. In general, tended underestimate increments, with relative RMSE ranging between 22.66% 70.30% plots. discuss challenges associated model validation, filtering processes, advantages utilizing wall-to-wall sensing. Our findings confirm feasibility covering wide range stand conditions representing large extent.

Язык: Английский

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Multi-resolution monitoring of the 2023 maui wildfires, implications and needs for satellite-based wildfire disaster monitoring

Science of Remote Sensing, Год журнала: 2024, Номер 10, С. 100142 - 100142

Опубликована: Июнь 6, 2024

The August 2023 wildfires over the island of Maui, Hawaii were one deadliest U.S. wildfire incidents on record with 100 deaths and an estimated $5.5 billion cost. This study documents incidence, extent, characteristics Maui using multi-resolution global satellite fire products, in so doing demonstrates their utility limitations for detailed monitoring, highlights outstanding observation needs monitoring. NASA 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product is compared PlanetScope 3 areas that mapped a published deep learning algorithm. In addition, all active detections provided by MODIS Terra Aqua satellites Visible Infrared Radiometer Suite (VIIRS) S-NPP NOAA-20 are used to investigate geographic temporal occurrence fires incidence relative areas. diurnal variation radiative power (FRP), available detections, presented examine how energetically burning. analysis undertaken town Lahaina was major population center burned. Satellite first detected 8th early morning (1:45 onwards) western slopes Mt. Haleakalā last 10th (at 2:46) Haleakalā. FRP VIIRS indicate less intensely from beginning end this three day period, nighttime generally more than daytime fires, most burning occurred likely due high fuel load buildings vegetation elsewhere. too coarse map 18 unambiguously at resolution covered 29.60 km2, equivalent about 1.6% Maui. systematically derived products assessment before, during after disaster events such as those experienced future monitoring events, recommendation constellation, discussed.

Язык: Английский

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High-resolution sensors and deep learning models for tree resource monitoring

Nature Reviews Electrical Engineering, Год журнала: 2024, Номер 2(1), С. 13 - 26

Опубликована: Ноя. 15, 2024

Язык: Английский

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Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model

Science of Remote Sensing, Год журнала: 2025, Номер unknown, С. 100188 - 100188

Опубликована: Янв. 1, 2025

Язык: Английский

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Distribution and Structure of China–ASEAN’s Intertidal Ecosystems: Insights from High-Precision, Satellite-Based Mapping

Remote Sensing, Год журнала: 2025, Номер 17(1), С. 155 - 155

Опубликована: Янв. 5, 2025

The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, anthropogenic impacts. Accurately mapping ecosystems differentiating mangroves, salt marshes, tidal flats remains challenge due to inconsistencies in classification frameworks. Here, we present high-precision approach for using multi-source satellite data, including Sentinel-1, Sentinel-2, Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, enable detailed of zones across China–ASEAN. Our findings indicate total area 73,461 km2 China–ASEAN, an average width 1.16 km. Analyses patch area, abundance, perimeter relationships reveal power-law distribution scaling exponent 1.52, suggesting self-organizing characteristics shaped both natural human pressures. offer foundational data guide conservation management strategies region’s novel perspective propel research on global coastal ecosystems.

Язык: Английский

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