#TITLE_ALTERNATIVE# - NURTYAWAN (NIM: 35113001), RIAN
Soil moisture is liquid water that occupies the empty spaces between soil particles. The information about the soil moisture conditions and the distribution is essential in order that the government can contrive and manage the production of the crop well. Soil moisture is necessary factor in the of plant growth process. Soil moisture is difficult to measure in a wide coverage by reason of cost and time factors. Traditionally, the model uses average soil moisture values on large areas. Nevertheless, as technology develops, satellite image-based modeling using spatial units (per pixel) can estimate the soil moisture accurately. Monitoring object of agricultural land using remote radar sensing techniques has been widely applied in many countries. Nonetheless, the conditions of cloud cover and rainfall during the rice growing season become one of the obstacles on the optical remote sensing systems. The solution is utilizing remote radar sensing technology that can record without being affected by weather conditions both day and night. Nowadays, the satellite radar system with rapid recording periods for agricultural applications has grown such as satellite ERS, Envisat (Europe) Radarsat 1,2 (Canada) and ALOS PALSAR 1,2 (Japan). Radar images provide the spatial resolution which varies from 10 to 1000 meters with a wide coverage of 50-500 km. The signal radar scatter (backscatter) above the open soil surface (bare soil) is affected by the surface characteristics they are soil moisture, surface roughness and dielectric constant. Soil moisture, surface roughness and characteristics of dielectric are soil parameters that interplay. The most refined surface reflects all of the energy in a specular direction and there is no signal scattering back to the antenna, whereas the very rough surface of the wave diffuses in all directions. Furthemore, the radar signals also depend on radar parameters such as polarization, angle of incidence and wavelength. The values of surface roughness (Hrms) are derived using Campbell and Shepard models modification. The model (MMCS) was developed to produce surface roughness for heterogeneous initial plant stage conditions. The results showed that MMCS model can improve the accuracy of surface roughness model value amount 31.25%. The surface roughness of MMCS results in calibration using a model formed from the correlation between MMCS surface roughness and field measurement results. The value of the dielectric constant (r) is derived using the dPSAR model. The MV value is calculated from the RAdarsat-2 image configuration parameter and the scatter signal coefficient using a model developed based on the correlation between the dielectric constants using the dPSAR model approach and the soil moisture (Mv) field survey. The determination coefficients of R2 and RMSE for June Ã‚Â–August is 0,52 and 9,4 while September - October 2014 is 0.60 and 7.3. The Mv model values for June and October generally accord to Mv conditions in the field, while in August and September underestimate. In June, the area was wet hence the contribution of the scatter signal was reduced due to the attenuation of the water cover in the period before the planting period. In August and September, Mv underestimate by taking into consideration the fact that the influence of mature rice plants or harvest periods. With the paddy canopy, the soil is shielded accordingly the moisture obtained is less than the actual amount. The value of rice plant productivity was extracted using a model built on linear correlation between the values of soil moisture and the value of rice crop productivity with coefficient of determination R2=0,60. The District of Cikedung has the highest value of productivity deviation. This condition is caused by the vulnerability of rice production due to the climate change in North Beach, West Java.