Several modelling techniques are currently available to analyse the efficiency of inter-digital transducers (IDTs) fabricated on piezoelectric substrates for producing surface acoustic wave (SAW) devices. Impulse response method, equivalent circuit method, coupling of modes, transmission matrix method, and numerical techniques are some of the popular ones for this. Numerical techniques permit modelling to be carried out with any number of finger electrode pairs with required boundary conditions on any material of interest. In this work, we describe numerical modelling of SAW devices using ANSYS to analyse the effect of mass loading, a major secondary effect of IDTs on the performance of SAW devices. The electrode thickness of the IDT influences the resonance frequency of the SAW delay line. The analysis has been carried out for different electrode materials, aluminium, copper, and gold, for different substrate materials, barium titanate (BaTiO3), X-Y lithium niobate (LiNbO3), lithium tantalate (LiTaO3), and the naturally available quartz. The results are presented and discussed.

Dry marginal agricultural land (DryMAL) potentially use as an alternative resource for crop production. DryMAL de-fined as land having low natural fertility due to its intrinsic properties and forming environmental factors. This study uses Sentinel-2A imagery to map the spatial extent, compare the result of the classification, and identify the change in DryMAL occupation. The area of study (461.9 km2) is part of Situbondo Regency and is located at the eastern part of East Java, In-donesia. Sentinel-2A image captured in dry-season of 2018 use for this study. Then, supervised image classification using a maximum likelihood algorithm use for image treatment and processing. Furthermore, 450 ground control points for train-ing areas collected during the field surveys. Five bands use in the classification process. The maps produced from the clas-sification process were then compared to the land-use map from the year 2000. The change in DryMAL occupation from 2000 to 2018 was calculated by comparing the classified and land-use map. Supervised classification yielded an overall accuracy of 95.8% and a kappa accuracy of 93.2%. The classification produced six (6) classes of land use: (1) forest, (2) pavement or built-up area, (3) irrigated paddy field, (4) non-irrigated rural area, (5) dry marginal land and (6) water body. Globally, during the last two decades, regional development led by the Regency occupied more DryMAL area for develop-ing plantation. The effort reduces the amount of non-irrigated and converting to the plantation, pavement areas, and irrigat-ed paddy-field.