Gradually, slowly, Pianoteq has improved its sound.
#DATA AND MODEL COUPLING PIANOTEQ SOFTWARE#
Whatever comparisons you could make about sound, Pianoteq always felt alive in a way software instruments often didn’t.Īnd now, following an aggressive cadence of releases, Pianoteq 5 makes the software new all over again: You can modify the models to do impossible things.Īnd most importantly, what I’ve always found with Pianoteq was a selection of instruments I really loved to play. And because it uses models, you can also load any number of wild and oddball historical models and other instruments if you like, without having to clear half your drive. It’ll run on Linux – I’ve gotten it running convincingly on a netbook. Whereas others gobble hard drive space, Pianoteq uses sophisticated modeling techniques that skip the samples, meaning it can fit onto a flash drive. You can even have a ridiculously-tall upright. You can have new pianos, old pianos, countless Steinway samples. You can load massive instances of Kontakt with different recorded sounds for every note, every articulation. You can buy entire hard drives just to store the gigabytes of samples. You can have increasingly-good models and samples in hardware, but you can really get a fake piano on your computer. Further studies are needed before being applied on a larger scale.If you want a fake piano, you can have a fake piano. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha −1 before coupling and between 0.04 and 0.42 t ha −1 after integration. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced.
#DATA AND MODEL COUPLING PIANOTEQ FULL#
The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha −1, while the measured yield varied from 3.08 to 5.63 t ha −1 for full irrigation and rainfed treatments.
The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. Notably, FVC and LAI were highly correlated with biomass. In descending order of R 2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. The R 2 coefficient was 0.79 for canopy cover and 0.77 for LAI. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley.
The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management.