Mporal SAR data: (1) it can be pretty tough to construct rice samples employing only SAR time series data with out rice prior distribution data; (2) the rice planting cycleAgriculture 2021, 11,4 ofin tropical or subtropical places is complicated, and the existing rice extraction methods usually do not make full use with the temporal qualities of rice, and also the classification accuracy must be improved; (3) additionally, modest rice plots are often affected by little roads and shadows. You can find some false alarms within the extraction benefits, so the classification outcomes must be optimized.Table 1. SAR information list table.Orbit Number–Frame Quantity: 157-63 No. 1 two 3 4 five six Acquisition Time 2019/4/5 2019/4/17 2019/5/11 2019/5/12 2019/6/4 2019/6/16 No. 7 eight 9 10 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 Number: 157-66 No. 1 two three 4 five six Acquisition Time 2019/3/30 2019/4/11 2019/5/5 2019/5/17 2019/5/29 2019/6/10 No. 7 8 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 two three four five 6 Acquisition Time 2019/3/31 2019/4/12 2019/5/6 2019/5/18 2019/5/30 2019/6/11 No. 7 8 9 10 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 process utilizing multitemporal SAR data, as shown in Figure two. This analysis was performed within the following components: (1) pixel-level rice sample production based on temporal statistical characteristics; (two) the BiLSTM-Attention network model constructed by combining BiLSTM model and interest N-(3-Azidopropyl)biotinamide Epigenetic Reader Domain mechanism for rice area, and (three) the optimization of classification outcomes primarily based on FROM-GLC10 information. two.2.1. Preprocessing Mainly because VH polarization is superior to VV polarization in monitoring rice phenology, especially during the rice flooding period [52,53], the VH polarization was selected. Quite a few preprocessing steps had been carried out. First, the S1A level-1 GRD information format were imported to produce the VH intensity photos. Second, the multitemporal intensity image within the very same coverage region have been registered employing ENVI software. Then, the De Grandi Spatio-temporal Filter was applied to filter the intensity image inside the time-space combination domain. Ultimately, Shuttle Radar Topography Mission (SRTM)-90 m DEM was used to calibrate and geocode the intensity map, as well as the intensity information worth was converted in to the backscattering coefficient on the logarithmic dB scale. The pixel size on the orthophoto is 10 m, which is reprojected to the UTM area 49 N inside the WGS-84 geographic coordinate system.Agriculture 2021, 11,5 ofFigure 2. Flow chart with the proposed framework.two.two.two. Time Series Curves of Unique MX1013 Data Sheet Landcovers To understand the time series traits of rice and non-rice inside the study area, common rice, buildings, water, and vegetation samples within the study location had been chosen for time series curve evaluation. The sample areas of 4.