Mporal SAR data: (1) it is actually very hard to construct rice samples applying only SAR time series information without rice prior distribution info; (2) the rice planting cycleAgriculture 2021, 11,4 ofin tropical or subtropical places is complicated, along with the existing rice extraction methods do not make complete use of the temporal characteristics of rice, as well as the classification accuracy must be improved; (3) additionally, smaller rice plots are frequently impacted by smaller roads and shadows. You will find some false alarms inside the extraction results, so the classification benefits need to be optimized.Table 1. SAR information list table.Orbit Number–Frame Number: 157-63 No. 1 2 3 4 5 six Acquisition Time 2019/4/5 2019/4/17 2019/5/11 2019/5/12 2019/6/4 2019/6/16 No. 7 8 9 ten 11 12 Acquisition Time 2019/6/28 2019/7/10 2019/7/22 2019/8/3 2019/8/4 2019/8/27 No. 13 14 15 16 17 18 Acquisition Time 2019/9/8 2019/9/20 2019/10/2 2019/10/14 2019/10/26 2019/11/7 No. 19 20 21 22 Acquisition Time 2019/11/19 2019/12/1 2019/12/13 2019/12/Orbit Number–Frame Quantity: 157-66 No. 1 two three 4 5 six Acquisition Time 2019/3/30 2019/4/11 2019/5/5 2019/5/17 2019/5/29 2019/6/10 No. 7 eight 9 ten 11 12 Acquisition Time 2019/6/22 2019/7/04 2019/7/16 2019/7/28 2019/8/9 2019/8/21 No. 13 14 15 16 17 18 Acquisition Time 2019/9/2 2019/9/14 2019/9/26 2019/10/8 2019/10/20 2019/11/1 No. 19 20 21 22 Acquisition Time 2019/11/13 2019/11/25 2019/12/19 2019/12/Orbit Number–Frame Number: 84-65 No. 1 2 three four 5 six Acquisition Time 2019/3/31 2019/4/12 2019/5/6 2019/5/18 2019/5/30 2019/6/11 No. 7 eight 9 ten 11 12 Acquisition Time 2019/6/23 2019/7/5 2019/7/17 2019/7/29 2019/8/10 2019/8/22 No. 13 14 15 16 17 18 Acquisition Time 2019/9/3 2019/9/15 2019/9/27 2019/10/9 2019/10/21 2019/11/2 No. 19 20 21 22 Acquisition Time 2019/11/14 2019/11/26 2019/12/8 2019/12/Therefore, this paper proposes a rice extraction and mapping system making use of multitemporal SAR information, as shown in Figure two. This analysis was performed in the following parts: (1) pixel-level rice sample production based on temporal statistical qualities; (2) the BiLSTM-Attention network model constructed by combining BiLSTM model and attention mechanism for rice region, and (three) the optimization of classification final results based on FROM-GLC10 information. 2.2.1. Preprocessing Mainly because VH polarization is superior to VV polarization in DTSSP Crosslinker MedChemExpress monitoring rice phenology, specifically throughout the rice flooding period [52,53], the VH polarization was selected. Numerous preprocessing actions have been carried out. 1st, the S1A level-1 GRD data format had been imported to generate the VH intensity images. Second, the multitemporal intensity image L-Norvaline Protocol within the very same coverage area had been registered applying ENVI computer software. Then, the De Grandi Spatio-temporal Filter was used to filter the intensity image within the time-space mixture domain. Lastly, Shuttle Radar Topography Mission (SRTM)-90 m DEM was utilised to calibrate and geocode the intensity map, as well as the intensity information value was converted in to the backscattering coefficient around the logarithmic dB scale. The pixel size with the orthophoto is ten m, which can be reprojected to the UTM region 49 N inside the WGS-84 geographic coordinate technique.Agriculture 2021, 11,five ofFigure 2. Flow chart on the proposed framework.2.two.2. Time Series Curves of Diverse Landcovers To know the time series characteristics of rice and non-rice in the study area, standard rice, buildings, water, and vegetation samples within the study region had been chosen for time series curve analysis. The sample places of 4.