No abstract

Landsat-8 data (level 1T) received by users are still in digital number and can be used directly for mapping land use / land cover. However, the data still has low radiometric accuracy when it is used to derive information such as vegetation index, biomass, land use / land cover classification, etc. so that so that it requires radiometric / atmospheric correction. In this study, we use atmospheric correction method of the second simulation of satellite in the solar spectrum (6S) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH) to eliminate atmospheric influences and compare the results with field measurements. The atmospheric parameters used were aerosol optical depth (AOD), water vapour column and ozone thickness from MODIS data with the date and time of acquisition approaching with Landsat-8 data. From the analysis conducted on the spectral response of atmospheric corrected image shows the 6S model has better accuracy for the spectral response from the rice growth phase compared to the FLAASH model. The analysis of the values of vegetation indices (NDVI, EVI, SAVI and MSAVI) shows that the 6S model has better accuracy for NDVI while for EVI, SAVI and MSAVI the FLAASH model has slightly better accuracy than 6S.

Atmospheric correction is essential in satellite data processing to reduce atmospheric and lighting effects by studying the physical parameter of the earth’s surface. In this study, ATCOR and 6S algorithms were evaluated for Sentinel-2 over paddy field area. In this evaluation, level-1C Top of Atmosphere (TOA) Sentinel-2 image was used as an input data. The spectral pattern analysis of the results was used to assess the reliability of the methods. As a result, the both methods produced the correct spectral patterns. Moreover, the results showed that the longer the wavelength the less the improvement values. It starts from the blue band, 43,2% for the fallow phase and 60% for the vegetative phase of BOA corrected image. In the other hand, the NDVI values of fallow and vegetative phases that derived from the two methods are not greatly different.

Along with the times and technology development, the number of satellite images produced is increasing and diverse, both in terms of spatial and temporal resolution. In addition, in the last few years, LAPAN has done a mosaic of Landsat data for 16 days. The availability of extensive satellite data makes users desire data ready to use without initial processing to save time and costs. Furthermore, the medium resolution image has a comprehensive enough coverage for applications in the forestry, agriculture, plantation, and marine sectors. Therefore, the need for medium resolution Analysis Ready Data (ARD) increases. The available ARD data has been through the initial processing, including ToA and BRDF corrections. The existing Landsat-8 ARD does not cover Indonesian territory. The Landsat-8 ARD for the Indonesian region was first produced and introduced in this paper. Moreover, the development of ARD data quality is carried out by adding a topographic correction process, and image cropping become tiles with a size of 1-degree x 1-degree, which can be run automatically. This processing system produces ARD Landsat-8 to meet user requirements effectively and efficiently. These advantages are obtained because the pre-processing data load for the user has been reduced, and the user can immediately use the data according to the required scope. The software for making ARD Landsat-8 has been recognized as Intellectual Property Rights. Moreover, ARD Landsat-8 for Indonesian territory has been continuously utilized by the Indonesian Ministry of Environment and Forestry and the Indonesian Ministry of Agriculture.