Significance of Planet SuperDove and refined Sentinel-2 imagery fusion for enhanced soil organic carbon prediction in croplands

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F25%3A43926775" target="_blank" >RIV/62156489:43410/25:43926775 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00027073:_____/25:N0000079

Výsledek na webu

<a href="https://doi.org/10.1016/j.catena.2025.108902" target="_blank" >https://doi.org/10.1016/j.catena.2025.108902</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.catena.2025.108902" target="_blank" >10.1016/j.catena.2025.108902</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Significance of Planet SuperDove and refined Sentinel-2 imagery fusion for enhanced soil organic carbon prediction in croplands

Popis výsledku v původním jazyce

When RS images from multisource specifically at high spatial and spectral resolution, are integrated, the generated imagery is believed to provide higher spatial, spectral, and temporal resolutions. Though image fusion techniques have been employed in many other fields, their applicability in soil science for the estimation of soil properties, including soil organic carbon (SOC), remains limited, especially where digital soil mapping (DSM) models using machine learning algorithms (MLA) are employed. This study explores the viability of enhancing the spectral capability of high spatial resolution imagery acquired from the PlanetScope SuperDove (PSD), which has low spectral capability, by integrating it with high spectral resolution imagery from the Sentinel-2 (S2B) satellite through an image fusion technique. The main aim is to use the fused data and topographic features from the STRM DEM to assess the predictive performance of SOC across large, diverse, and erodible cropland. Prediction models were established using the data sets separately, fused, and with or without the STRM data. Two MLAs were used, including regularised random forest (RRF) and Gaussian process regression (GPR). Correlation and homogeneity tests were conducted between the S2B bands and measured SOC values before their incorporation to obtain refined S2B data for the raster fusion approach. The results show that the optimal SOC content prediction comprised the incorporation of STRM data to the fused data, as input, using the GPR model, where the lowest RMSE of 3.3 gkg-1, the highest coefficient of determination (R2) of 0.83, and the MAE of 3.6 gkg-1 were obtained. In terms of SOC spatial distribution map, the fused datasets supplemented by STRM data employing the GPR model performed better than the other alternatives. In summary, this study highlights the promising potential of image fusion of high spatial and spectral RS images to improve the estimation model of SOC, which has the potential to be widely implemented in erodible cropland area.

Název v anglickém jazyce

Significance of Planet SuperDove and refined Sentinel-2 imagery fusion for enhanced soil organic carbon prediction in croplands

Popis výsledku anglicky

When RS images from multisource specifically at high spatial and spectral resolution, are integrated, the generated imagery is believed to provide higher spatial, spectral, and temporal resolutions. Though image fusion techniques have been employed in many other fields, their applicability in soil science for the estimation of soil properties, including soil organic carbon (SOC), remains limited, especially where digital soil mapping (DSM) models using machine learning algorithms (MLA) are employed. This study explores the viability of enhancing the spectral capability of high spatial resolution imagery acquired from the PlanetScope SuperDove (PSD), which has low spectral capability, by integrating it with high spectral resolution imagery from the Sentinel-2 (S2B) satellite through an image fusion technique. The main aim is to use the fused data and topographic features from the STRM DEM to assess the predictive performance of SOC across large, diverse, and erodible cropland. Prediction models were established using the data sets separately, fused, and with or without the STRM data. Two MLAs were used, including regularised random forest (RRF) and Gaussian process regression (GPR). Correlation and homogeneity tests were conducted between the S2B bands and measured SOC values before their incorporation to obtain refined S2B data for the raster fusion approach. The results show that the optimal SOC content prediction comprised the incorporation of STRM data to the fused data, as input, using the GPR model, where the lowest RMSE of 3.3 gkg-1, the highest coefficient of determination (R2) of 0.83, and the MAE of 3.6 gkg-1 were obtained. In terms of SOC spatial distribution map, the fused datasets supplemented by STRM data employing the GPR model performed better than the other alternatives. In summary, this study highlights the promising potential of image fusion of high spatial and spectral RS images to improve the estimation model of SOC, which has the potential to be widely implemented in erodible cropland area.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

<a href="/cs/project/SS02030018" target="_blank" >SS02030018: Centrum pro krajinu a biodiverzitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Catena

ISSN

0341-8162

e-ISSN

1872-6887

Svazek periodika

254

Číslo periodika v rámci svazku

30 June

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

108902

Kód UT WoS článku

001445522700001

EID výsledku v databázi Scopus

2-s2.0-86000598149