Evaluation of limiting factors for SAR backscatter based cut detection of alpine grasslands

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 9, P. 100117 - 100117

Published: Jan. 5, 2024

Several studies utilized C-band Synthetic Aperture Radar (SAR) backscatter time series to detect cut events of grasslands. They identified several potential factors hindering the detection: Vegetation characteristics, precipitation, and timing salvage harvested grass. This study uses a comprehensive in situ database assess impact those on detection rate by performing based Sentinel-1 relating accuracy potentially limiting factors. The results can be summarized following key findings: (i) decreases significantly with grass heights below 35 cm biomass less than 2100 kg/ha. As first growth is typically characterized greater height higher biomass, cuts achieved 85% compared re-growth 65%. (ii) False positive were related precipitation amounts, but adding data model led only slight increase cuts, decrease overall accuracy. (iii) No relation was found between behaviour. These insights contribute better utilization for vegetation analysis agricultural applications, including detection. Further research dense measurements, Water Content (VWC) required fully understand behaviour over managed

Language: Английский

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Towards data-driven discovery of governing equations in geosciences

Communications Earth & Environment, Journal Year: 2024, Volume and Issue: 5(1)

Published: Oct. 14, 2024

Governing equations are foundations for modelling, predicting, and understanding the Earth system. The system is undergoing rapid change, conventional approaches establishing governing equations, such as empirical generalisations, becoming increasingly challenging to deal with complexity diversity of geoscience processes we study today. In this Perspective, explore data-driven equation discovery, a novel scientific artificial intelligence pathway, advancing geosciences. Data-driven discovery identifies hidden patterns from data transforms them into interpretable representations, automating accelerating processes. It provides practical approach geoscientists model understand complex based on big data. final vision uncover new clear, describable, quantifiable in various disciplines. We summarize opportunities highlight that challenges field should be addressed by interdisciplinary collaborations. can identify transform disciplines, according review advantages potential geoscience.

Language: Английский

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A temporal-spectral value and shape change detection method integrating thematic index information and spectral band information

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(16), P. 47408 - 47421

Published: Feb. 4, 2023

Language: Английский

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Temporal decorrelation of C-band radar data over wheat in a semi-arid area using sub-daily tower-based observations

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114059 - 114059

Published: Feb. 22, 2024

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

Language: Английский

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Quantitative assessment of various proxies for downscaling coarse-resolution VOD products over the contiguous United States

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103910 - 103910

Published: May 16, 2024

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating canopy, plays crucial role in global-scale biomass monitoring and climate change research. However, spatial gridding existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at regional scale. High-resolution active-microwave proxies optical indices can potentially be used to disaggregate coarse-resolution VOD, but it unclear which proxy optimal. In this paper, Normalized Difference Index (NDVI) (VH, VV, cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed various frequencies (L-/C-/X-VOD) across contiguous United States (U.S.). The results showed VH (R = 0.80) NDVI 0.77) exhibit high correlation L-VOD products. For temporal correlation, had highest overall performances all products, good correlations also achieved CR and, lesser extent, VH. Further comparisons performance between Brightness Temperature (TB) revealed while TB displayed strong proxies, its such low. contrast, both temporally spatially (e.g., VH). These evidences suggested downscaling using combination other could an alternative promising method estimate high-resolution VOD.

Language: Английский

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Emerging Methods to Validate Remotely Sensed Vegetation Water Content

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(14)

Published: July 16, 2024

Abstract Satellite‐retrieved vegetation optical depth (VOD) has provided extensive insights into global plant function (such as, carbon stocks, water stress, crop yields) because of VOD's ability to monitor stress and biomass at near daily temporal frequency under all‐weather conditions. However, arguably, the greatest challenge with broadly applying VOD is its lack validation partly simultaneous sensitivity status changes, as well intensive methods required measure these properties in‐situ. Here, inspired by recent Yao et al. (2024), https://doi.org/10.1029/2023GL107121 article, I argue that estimated from navigation satellite systems (GNSS) land surface models hydraulic schemes are two emerging show promise for more widely validating satellite‐based VOD. encourage wider adoption approaches validate further advance research.

Language: Английский

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Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 279, P. 113116 - 113116

Published: June 30, 2022

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ40o), slope (σ′) and curvature (σ″) over France. The Interactions between Soil, Biosphere Atmosphere (ISBA) land surface model (LSM) produce variables (LSVs) that are input DNN. DNN trained simulate σ40o, σ′ σ″ from 2007 2016. predictive skill of evaluated during an independent validation period 2017 2019. Normalized sensitivity coefficients (NSCs) computed study ASCAT observables changes in LSVs as a function time space. Model performance yields near-zeros bias σ40o σ′. domain-averaged values ρ 0.84 0.85 for σ′, compared 0.58 σ″. unbiased RMSE 8.6% dynamic range 13% with cover having some impact on performance. NSC results show DNN-based could reproduce physical response LSVs. Results indicated sensitive soil moisture LAI these sensitivities vary time, highly dependent type. was shown be LAI, but also root zone due dependence vegetation water content moisture. potentially serve observation operator data assimilation constrain dynamics LSMs.

Language: Английский

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Forest foliage fuel load estimation from multi-sensor spatiotemporal features

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 115, P. 103101 - 103101

Published: Nov. 10, 2022

Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage load (FFL) are important for fire managers to assess risk. Here, we integrated optical data from Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) Sentinel-1 estimate FFL. We first reconstructed seamless time series and imagery by accounting unequal intervals between image observations outliers. then extracted temporal features that proxies intra- inter-annual these series. In addition, derived spatial quantify context therefore used varying window sizes. The random forest regression was implemented importance spatiotemporal features, reduce errors, derive robust FFL estimates. satellite estimates were validated against 96 field measurements Pinus yunnanensis forests Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both SAR importantly contributed estimation. When only used, model achieved a R2 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when 0.76 (rRMSE 25.6 %). However, combined, increased 0.81 23.2 also found more predictors than captured context. demonstrated our mapping method case study Chinese relation occurrence fire. Our needs additional validation over different tree species types, yet has potential loads

Language: Английский

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Dew/hoar frost on the canopies and underlying surfaces of two typical desert shrubs in Northwest China and their relevance to drought

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127880 - 127880

Published: April 26, 2022

Language: Английский

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A Coupled Temporal–Spectral–Spatial Multidimensional Information Change Detection Framework Method: A Case of the 1990–2020 Tianjin, China

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2023, Volume and Issue: 16, P. 5741 - 5758

Published: Jan. 1, 2023

Satellite image time series change detection methods have become an effective means of obtaining information on land cover change. However, the temporal, spectral and spatial features their derived objects are great importance for detection. Existing studies made insufficient use these features, which may affect results In order to fully integrate above portray represent information, this study proposes a coupled temporal-spectral-spatial multidimensional framework (TSSF) method. Firstly, index calculated construct intra-annual temporal-spectral reduce underutilization features. Secondly, temporal is extended spatio-temporal domain by simple non-iterative clustering (SNIC) method SG filtering increase exploitation Then, value shape based dynamic warping vector analysis in posterior probability space (CVAPS) employed obtain from spectral, index, class perspectives. Finally, type region obtained magnitude according Bayesian criterion. Tianjin City was used as area explore 1990 2020. The show that TSSF feasible expressing compared with existing methods, conducive efficient acquisition identification areas types.

Language: Английский

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