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Indramayu merupakan salah satu lumbung padi Indonesia yang ada di wilayah Jawa Barat dimana Badan Pusat Statistik mencatat pada tahun 2014, Indramayu menghasilkan padi sebesar 1.361.374 ton. Untuk memantau produksi padi, sangat diperlukan pemantauan fase pertumbuhan tanaman padi, salah satu metodenya dengan teknologi penginderaan jauh sistem RADAR menggunakan citra RADARSAT-2 quad polarimetrik. Penelitian ini bertujuan untuk mengklasifikasi daerah fase pertumbuhan tanaman padi menggunakan metode Cloude Pottier H/A/α (entropi/anisotropi/sudut alfa) dan mengevaluasi metode tersebut dalam klasifikasi fase pertumbuhan tanaman padi. Hasil dari penelitian ini yaitu peta klasifikasi fase pertumbuhan tanaman padi dimana dari keseluruhan akuisisi citra, luas lahan tertinggi adalah fase germination/laut yang berjumlah 2.368.242 m2 (22 September 2014). Hasil klasifikasi ini disesuaikan dengan bidang H- α classification plane untuk mengetahui pada zona man a yang memiliki hamburan paling dominan. Hasil pada 18 Juni 2014 dan 5 Agustus 2014 menunjukkan zona 7 (fase panicle initiation/inisiasi malai), zona 8 (fase milk stage/gabah matang susu), dan zona 9 (fase germination/perkecambahan benih atau fase seeding/pertunasan) menjadi zona yang dominan dimana ketiga mekanisme memiliki arti double-bounce scattering (Z7), volume scattering (Z8), dan surface scattering (Z9) sedangkan pada 22 September 2014 dan 16 Oktober 2014 hamburan yang paling dominan terdapat pada Z8 (fase milk stage/gabah matang susu) dengan mekanisme volume scattering dan Z9 (fase germination/perkecambahan benih atau fase seeding/pertunasan) dengan mekanisme surface scattering. Kata kunci : Pertumbuhan Padi, Klasifikasi, RADARSAT-2, H/A/ α ABSTRACT Indramayu is one of Indonesia's granary in West Java where Statistic Data Center noted that in 2014 Indramayu produced 1.361.374 tons of rice. It’s necessary to monitor growth phase of rice plant for monitoring rice production, one of the method is remote sensing technology is the RADAR system with RADARSAT-2 image quad-polarimetric. This study aims to classify the phase of growth of rice plants using the Cloude Pottier H / A / α method (entropy / anisotropy / alpha angle) and evaluate these methods in classification of rice plant growth phases. The results of this study are the classification map of the rice plant phase where from the overall image acquisition, the highest land area is the germination / sea phase, which amounts to 2,368,242 m 2 (22 September 2014). The classification results are adjusted with the H- α classification plane to find out which zone has the most dominant scattering. The result on 18 June 2014 and 5 August 2014 showed zone 7 (panicle initiation phase), zone 8 (milk stage phase), and zone 9 (germination/seeding) to be the dominant zone where the three mechanisms mean double-bounce scattering (Z7), volume scattering (Z8), and surface scattering (Z9) while on 22 September 2014 and 16 October 2014 the most dominant scattering is in Z8 (milk stage phase) with volume scattering mechanism and Z9 (germination/seeding phase) with surface scattering mechanism Keywords : Rice Growth , Classification, RADARSAT-2, H/A/α.

Indonesia mempunyai 127 gunung api aktif yang tersebar dari Sabang sampai Merauke. Oleh karena itu, perlu adanya pemantauan aktivitas gunung api yang dapat digunakan untuk acuan mitigasi bencana. Pada penelitian ini menggunakan metode deformasi, metode deformasi merupakan perubahan bentuk, posisi, dan dimensi dari suatu benda. Tujuan dari pemantauan deformasi ini untuk mengetahui perubahan gunung api yang disebabkan oleh aktivitas gunung api. Pemantauan aktivitas gunung api metode deformasi dilakukan dengan menggunakan citra Sentinel-1A yang diolah dengan teknologi Differential Interferometry SAR (DInSAR). Dalam penelitian ini dilakukan pengolahan dengan teknologi DInSAR metode two-pass dari empat buah citra satelit sentinel-1A 10 Januari 2018, 27 Februari 2018, 10 Mei 2018 dan 22 Januari 2019 serta data Digital Elevation Model (DEM) SRTM dengan ketelitian 30 meter .Hasil dari penelitian ini yaitu peta deformasi pra 1 erupsi yang diolah dari pasangan citra 10 Januari 2018 dengan citra 27 Februari 2018 yang menghasilkan deflasi sebesar -0,12 meter, dan peta deformasi pra 2 erupsi yang diolah dari pasangan citra 27 Februari 2018 dan 10 Mei 2018 menghasilkan deflasi sebesar -0,27 meter serta peta pasca erupsi yang diolah dari pasangan citra 10 Mei 3018 dan 22 Januari 2019 menghasilkan deflasi sebesar -0,194 meter. Kata kunci : Deformasi, Gunung Merapi, Sentinel-1A, DInSAR. ABSTRACT Indonesia has 127 active volcanoes spread over from Sabang to Merauke. Therefore, it is necessary to monitor volcanic activity that can be used as a reference for disaster mitigation. In this study, deformation method was used to reflect a change in the shape, position, and dimensions of an object. The purpose of monitoring this deformation is to find out volcanic changes caused by volcanic activity. Monitoring the volcanic activity of the deformation method is carried out using Sentinel-1A images processed with Differential Interferometry SAR (DInSAR) technology. In this research, two-pass method of DInSAR technology was processed using four sentinel-1A satellite images on January 10, 2018, February 27, 2018, May 10, 2018 and January 22, 2019 and SRTM Digital Elevation Model (DEM) data with 30 meters accuracy. This research processed pre-eruption deformation map from the 10 January 2018 imagery pair with the 27 February 2018 image which resulted in a deflation of 0.12 meters. Pre- eruption 2 deformation map was processed from the 27 February 2018 and 10 May 2018 image pairs and resulted in a deflation of 0.27 meters while post-eruption map processed from the 10 May 3018 and 22 January 2019 image pairs resulted in deflation of 0.194 meters. Keywords : Deformation, Merapi Mountain, Sentinel-1A, DinSAR.

Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.

On July 29th, 2018 there was an earthquake of 6.4 magnitude occurred in Lombok Island, West Nusa Tenggara. The repercussions of the earthquake cause the area to deform. Deformation refers to any changes in the shape and size of an object. Hence the purpose of this research is to measure the deformation cause by the aforementioned earthquake in Lombok. The deformation calculation is conduct by using the SAR data from Sentinel-1A Satellite Imagery (Interferometric Wide Swath (IW)) using DInSAR method. The effect of deformation cause by an earthquake in Lombok based on DInSAR method is -0.001 m up to -0.134 m with the average -0.026 m and the image coherence is 0.32, with the range for coherence is 0 to 1. The small value of coherence cause by the vegetation density within the research area. The vegetation density will affect the imagery coherence value due to its movement and development thus effecting the backscatter. Hence, the deformation data situate within the vegetation density area will result less accurate data.

This research aims to model surface roughness for agricultural land using RADARSAT-2 satellite imagery. To obtain the model of surface roughness, the digital number of radar image are converted into backscattering coefficient and used as basis of surface roughness modeling. Furthermore, using the relationship between the backscattering coefficient, local incidence angle, and the wavelength was made initial models for surface roughness. The new equation that can model surface roughness on agricultural land is obtained by performing calibration between the value of the surface roughness from initial models and combinations of initials models with the value of surface roughness from field measurement that the equation resulted y=-591987x5+(9.106)x4-(6.107)x3+(2.108)x2-(3.108)x+(2.108) with RMSE value = 0.31 cm, where y is the value of surface roughness modeled (cm) and x is the surface roughness value of the combination of initial models used during the calibration process (cm). The combination of HV, VH polaristation and local incident angle (h0HV+h0VH)x cos ϴ produces a correlation value (R2) = 0.804.

Monitoring the growth of rice in Indonesia is very important because rice is a staple food of the Indonesian people. The biggest rice producer in West Java is Indramayu. Soil moisture is one of factor in monitoring the growth of paddy. The Monitoring is carried out using SAR image, namely Radarsat-2 Quad-Polarimetric imagery. In estimating the value of soil moisture, required a dielectric constant which is obtained from the value of the Digital Number (DN) that is converted into the backscattering value (σo). The approach that used in calculating the dielectric constant is a semi-empirical approach using the Dubois model (1995). The purpose of this study is to estimate the value of soil moisture in order that it can be used in monitoring the growth phase of paddy. The rice growth phase is distinguished in the form of a graph subsequently matched with the results of field photo validation. The results of this research show a positive linear correlation between the Dubois model (1995) and field soil moisture with a level of accuracy of ± 7.202% volumetric.

Microwave backscattering from the earth's surface depends on several parameters such as surface roughness and dielectric constant of surface materials. The two parameters related to water content and porosity are crucial for estimating soil moisture. The soil moisture is an important parameter for ecological study and also a factor to maintain energy balance of land surface and atmosphere. Direct roughness measurements to a large area require extra time and cost. Heterogeneity roughness scale for some applications such as hydrology, climate, and ecology is a problem which could lead to inaccuracies of modeling. In this study, we modeled surface roughness using Radasat-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) data. The statistical approaches to field roughness measurements were used to generate an appropriate roughness model. This modeling uses a physical SAR approach to predicts radar backscattering coefficient in the parameter of radar configuration (wavelength, polarization, and incidence angle) and soil parameters (surface roughness and dielectric constant). Surface roughness value is calculated using a modified Campbell and Shepard model in 1996. The modification was applied by incorporating the backscattering coefficient (σ°) of quad polarization HH, HV and VV. To obtain empirical surface roughness model from SAR backscattering intensity, we used forty-five sample points from field roughness measurements. We selected paddy field in Indramayu district, West Java, Indonesia as the study area. This area was selected due to intensive decreasing of rice productivity in the Northern Coast region of West Java. Third degree polynomial is the most suitable data fitting with coefficient of determination R2 and RMSE are about 0.82 and 1.18 cm, respectively. Therefore, this model is used as basis to generate the map of surface roughness.