Dwi Rustam Kendarto
Abstract: Soil moisture is important information in management efficiency water irrigation plantation with technical irrigation systems. One method of determining soil moisture, the use of remote sensing data is very beneficial because it can record soil moisture with fast time duration. But needs to be considered is how far the ability of remote sensing data to describe the distribution of soil moisture content. This study aims to determine the moisture content of soil using remote sensing data and imagery of the Land Satellite Thematic Mapper imagery (Landsat TM7) and MODIS (Moderate Resolution Imaging Spectroradiometer) and integrated model with wavelet transform. Determination of moisture content of soil used a triangle model of Carlsson and field data. The data field was done by measuring soil moisture content daily with a depth range of 10 cm to 100 cm depth. Wavelet transformation was used to integrated soil water content form landsat TM7 imagery analysis and MODIS image analysis to improved spatial resolution and temporal resolution. Location of the research done on pineapple plantations in Terbanggi Besar Lampung Tengah. The result showed that the remote sensing data was able to identify the moisture content of the soil especially for depth less than 40 cm. Absolute error for a depth of less than 40 cm are 7, 24 for Landsat and 5.84 for MODIS. Ability of soil moisture content determination from Landsat and MODIS imagery to increase the depth of the lower soil solum. The ability to image Landsat soil moisture content idenfikasi better at a depth of 10 cm to 40 cm instead of using MODIS. Root mean square error values for a depth of 10 cm was 2.2. For the Landsat TM7 and 2.44 for MODIS, the relative error of 13.15 for the Landsat and to 15.98 MODIS. Average absolute error Landsat images to a depth of 10 cm was 4.83 for Landsat and 5.95 for MODIS. Based analysis absolute error, root mean square and the relative error can be stated that the Landsat better in determining soil moisture compared to the MODIS imagery. Wavelet transform can improve spatial resolution and temporal resolution to follow the characteristics of temporal resolution and best spatial resolution of the input data.
Keywords: Soil moisture content, remote sensing data, wavelets transform