85 research outputs found
AMSR-2 and COSMO-SkyMed data integration for estimating snow depth at high resolution in Italian Alps
No full textIntegration of Active and Passive Multifrequency Data from AMSR-2 and Cosmo SkyMed for Snow Depth Monitoring at High Resolution in Alpine Environments
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Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas
No full textIn this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors
High-Resolution Mapping of Soil Moisture by AMSR2 Data Disaggregation Based on Sentinel-1 and Machine Learning
No full textThanks to the frequent revisiting, satellite microwave radiometers have great potential for surface soil moisture (SM) monitoring. However, their spatial resolution is not sufficient for hydrological studies in small catchments as well as applications to precision farming. In this study, a disaggregation technique based on machine learning is proposed: the technique combines Sentinel-1 (S-1) SAR data with SM generated from advanced microwave scanning radiometer 2 by the IFACs HydroAlgo algorithm, with the aim of enhancing the SM spatial resolution from the original 10 km to about 30 m. To this scope, two machine-learning techniques have been considered for the implementation, namely artificial neural networks (ANNs) and random forests (RF). Training is carried out by aggregating and coregistering S-1 data with the HydroAlgo SM at 10-km resolution. After training, the ANN and RF algorithms are applied pixel-by-pixel to the S-1 images at full resolution for generating the enhanced SM maps. The method has been implemented and validated in two agricultural areas located in Central Italy, where a series of experiments has been carried out between 2019 and 2020 for collecting the main soil and vegetation parameters at the same time of satellite overpasses. To assess the actual resolution of the output SM, the validation against in situ measurements has been carried out by aggregating data at 10, 30, 50, and 70 m. The results confirmed the effectiveness of the proposed method: validation carried out at 30 m obtained R≃0.82 and RMSE≃0.05 m3/m3 that represent a noticeable improvement with respect to the results obtained by HydroAlgo at 10 km (R≃0.56 and RMSE >> 0.1 m3/m3). Validation results also pointed out the superior performances of the ANN based with respect to the RF-based disaggregation
Soil and Vegetation Water Status Monitoring by Integrating Optical and Microwave Satellite Data
No full textA reappraisal of the Strong Fluctuation Theory in combination with rough soil models to improve the simulation accuracy in alpine snowpacks at C- and X-band
No full textOn the Relationship Between Stickiness in DMRT Theory and Physical Parameters of Snowpack: Theoretical Formulation and Experimental Validation With SNOWPACK Snow Model and X-Band SAR Data
No full textHigh Resolution Mapping of Crop Biomass by Combining Sentinel-1 and Cosmo Skymed Through Machine Learning
No full textSnow cover area identification by using a change detection method applied to COSMO-SkyMed images
No full textThe information theoretic snow detection algorithm, a method that employs a change detection approach derived by Shannon's information theory based on the conditional probability of the local means between two images taken at different times, is applied to multitemporal COSMO-SkyMed (R) data. The ultimate purpose of the method is the identification of snow cover areas in the case of extensive surface changes between summer and winter seasons. Both Himage and Ping Pong data in Stripmap acquisition mode from the COSMO-SkyMed constellation are processed. Results are compared to the available ground snow information gathered at the meteorological station present in the area. Quantitative assessments are obtained for Himage by considering a Landsat image as ground-truth. Receiver operating characteristic curves are used to deliver numerical comparisons between ground-truth and classified image, which is then compared to the well-known log-ratio approach. The proposed information theoretical approach to change detection provides very promising results in the case of large snow covering on multitemporal single-look synthetic aperture radar images at very high spatial resolution, due to its intrinsic low sensibility to speckle noise
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