Matches in SemOpenAlex for { <https://semopenalex.org/work/W3101411094> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3101411094 abstract "Soil moisture plays a vital role in water resources management related applications. Nevertheless, the coarse resolution of satellite-based surface soil moisture products has limited applications at field-scale, for example, precision agriculture. The current Sentinel-1 satellite mission provides soil moisture products at 1km resolution, which is still not matching the need at field scales. Therefore, the spatial downscaling approach was applied to downscale coarse resolution (1km) satellite surface soil moisture (SSM) products to high resolution (15 cm) utilising UAS measurements using the random forest (RF) machine learning-based model. In this study, the RF model was trained using various configurations of input data prepared with remotely sensed SSM and ancillary land surface parameters of LST, DEM and NDVI. The performance of different trained RF models was evaluated to find out which RF model could represent the best relationship of SSM and surface parameters with the best capability for the prediction of SSM. The results indicated that all trained RF models have good performances. However, the trained RF model using 2018 - 2019 dataset on 78 km by 78km spatial extent outperformed the others with the highest correlation coefficient (R ) of 0.83 and RMSE of 12.13 %. Therefore, this trained RF model was considered for further process and was applied with the land surface features derived from UAS imageries to predict the SSM at 15cm resolution at noon and sunrise time. The trained RF model can also identify the relative importance of land surface parameters /features in predicting SSM. It was found that the LST has a higher impact than other features, while DEM being the least influential. The downscaled SSM can capture the spatial pattern of SSM at noon and sunrise time, when compared with the in situ measurements from the study area in Monte Cilento Sub catchment in Alento Catchment, Italy. The averaged ubRMSE, RMSE and R are reported 0.07 cm3/cm3, 0.21 cm3/cm3, and 0.60 respectively. Notably, all statistical metrics showed acceptable results even though the average of ubRMSE does not reach the SMAP and Global Climate Observing System (GCOS) mission accuracy target of 0.04 cm3/cm3 for soil moisture due to the downscaled SSM products were generated at 5 cm while the in situ measurements were taken at 15 cm. In summary, this study successfully generates high spatial resolution SSM data from coarse-scale satellite products by integrating UAS measurements and RF model as a downscaling approach. The generated soil moisture products could provide useful information for better agricultural management in Monteforte Cilento sub-catchment in Alento River catchment." @default.
- W3101411094 created "2020-11-23" @default.
- W3101411094 creator A5007539455 @default.
- W3101411094 date "2020-01-01" @default.
- W3101411094 modified "2023-09-27" @default.
- W3101411094 title "Spatial Downscaling of Satellite Soil Moisture Utilising High-Resolution UAS Data over Alento Catchment in Italy" @default.
- W3101411094 hasPublicationYear "2020" @default.
- W3101411094 type Work @default.
- W3101411094 sameAs 3101411094 @default.
- W3101411094 citedByCount "0" @default.
- W3101411094 crossrefType "dissertation" @default.
- W3101411094 hasAuthorship W3101411094A5007539455 @default.
- W3101411094 hasConcept C107054158 @default.
- W3101411094 hasConcept C119857082 @default.
- W3101411094 hasConcept C127313418 @default.
- W3101411094 hasConcept C127413603 @default.
- W3101411094 hasConcept C146978453 @default.
- W3101411094 hasConcept C153294291 @default.
- W3101411094 hasConcept C154945302 @default.
- W3101411094 hasConcept C169258074 @default.
- W3101411094 hasConcept C187320778 @default.
- W3101411094 hasConcept C19269812 @default.
- W3101411094 hasConcept C205372480 @default.
- W3101411094 hasConcept C205649164 @default.
- W3101411094 hasConcept C24939127 @default.
- W3101411094 hasConcept C2778102629 @default.
- W3101411094 hasConcept C2778755073 @default.
- W3101411094 hasConcept C2780092901 @default.
- W3101411094 hasConcept C39432304 @default.
- W3101411094 hasConcept C41008148 @default.
- W3101411094 hasConcept C41156917 @default.
- W3101411094 hasConcept C58640448 @default.
- W3101411094 hasConcept C62649853 @default.
- W3101411094 hasConceptScore W3101411094C107054158 @default.
- W3101411094 hasConceptScore W3101411094C119857082 @default.
- W3101411094 hasConceptScore W3101411094C127313418 @default.
- W3101411094 hasConceptScore W3101411094C127413603 @default.
- W3101411094 hasConceptScore W3101411094C146978453 @default.
- W3101411094 hasConceptScore W3101411094C153294291 @default.
- W3101411094 hasConceptScore W3101411094C154945302 @default.
- W3101411094 hasConceptScore W3101411094C169258074 @default.
- W3101411094 hasConceptScore W3101411094C187320778 @default.
- W3101411094 hasConceptScore W3101411094C19269812 @default.
- W3101411094 hasConceptScore W3101411094C205372480 @default.
- W3101411094 hasConceptScore W3101411094C205649164 @default.
- W3101411094 hasConceptScore W3101411094C24939127 @default.
- W3101411094 hasConceptScore W3101411094C2778102629 @default.
- W3101411094 hasConceptScore W3101411094C2778755073 @default.
- W3101411094 hasConceptScore W3101411094C2780092901 @default.
- W3101411094 hasConceptScore W3101411094C39432304 @default.
- W3101411094 hasConceptScore W3101411094C41008148 @default.
- W3101411094 hasConceptScore W3101411094C41156917 @default.
- W3101411094 hasConceptScore W3101411094C58640448 @default.
- W3101411094 hasConceptScore W3101411094C62649853 @default.
- W3101411094 hasLocation W31014110941 @default.
- W3101411094 hasOpenAccess W3101411094 @default.
- W3101411094 hasPrimaryLocation W31014110941 @default.
- W3101411094 hasRelatedWork W2027554150 @default.
- W3101411094 hasRelatedWork W2033517242 @default.
- W3101411094 hasRelatedWork W2187365236 @default.
- W3101411094 hasRelatedWork W2363950207 @default.
- W3101411094 hasRelatedWork W2514170613 @default.
- W3101411094 hasRelatedWork W2529998007 @default.
- W3101411094 hasRelatedWork W2741949247 @default.
- W3101411094 hasRelatedWork W2746963570 @default.
- W3101411094 hasRelatedWork W2788391142 @default.
- W3101411094 hasRelatedWork W3033219169 @default.
- W3101411094 hasRelatedWork W3042880838 @default.
- W3101411094 hasRelatedWork W3082777587 @default.
- W3101411094 hasRelatedWork W3118293400 @default.
- W3101411094 hasRelatedWork W3118411654 @default.
- W3101411094 hasRelatedWork W3126157226 @default.
- W3101411094 hasRelatedWork W3127840256 @default.
- W3101411094 hasRelatedWork W3170311800 @default.
- W3101411094 hasRelatedWork W3201498974 @default.
- W3101411094 hasRelatedWork W3205481294 @default.
- W3101411094 hasRelatedWork W3206266431 @default.
- W3101411094 isParatext "false" @default.
- W3101411094 isRetracted "false" @default.
- W3101411094 magId "3101411094" @default.
- W3101411094 workType "dissertation" @default.