The Landsat-7 Enhanced Thematic Mapper Plus (ETM+) is the sensor payload on the Landsat-7 satellite imager
(launched on April 15th, 1999) that is a derivative of the Landsat-4 and 5 Thematic Mapper (TM) land imager sensors.
Scan Line Corrector (SLC) malfunctioning appeared onboard on May 31, 2003. The SLC-Off problem was caused by
failure of the SLC which compensates for the forward motion of the satellite . As ETM+ is still capable of acquiring
images with the SLC-Off mode, the need of applying new techniques and using other data sources to reconstruct the
missed data is a challenging for scientists and final users of remotely sensed images. One of the predicted future roles of
the Advanced Land Imager (ALI) onboard the Earth Observer One (EO-1) is its ability to offer a potential technological
direction for Landsat data continuity missions . In this regard more than the purposes of the work as fabricating the
gapped area in the ETM+ the attempt to evaluate the ALI imagery ability is another noticeable point in this work. In the
literature there are several techniques and algorithms for gap filling. For instance local linear histogram matching ,
ordinary kriging, and standardized ordinary cokriging . Here we used the Regression Based Data Combination
(RBDC) in which it is generally supposed that two data sets (i.e. Landsat/ETM+ and EO-1/ALI) in the same spectral
ranges (for instance band 3 ETM+ and band 4 ALI in 0.63 - 0.69 μm) will have meaningful and useable statistical
characteristics. Using this relationship the gap area in ETM+ can be filled using EO-1/ALI data. Therefore the process is
based on the knowledge of statistical structures of the images which is used to reconstruct the gapped areas. This paper
presents and compares four regression based techniques. First two ordinary methods with no improvement in the
statistical parameters were undertaken as Scene Based (SB) and Cluster Based (CB) followed by two statistically
developed algorithms including Buffer Based (BB) and Weighted Buffer Based (WBB) techniques. All techniques are
executed and evaluated over a study area in Sulawesi, Indonesia. The results indicate that the WBB and CB approaches
have superiority over the SB and BB methods.
The use of remote sensing data in site specific crop management aims at the prediction of soil and crop factors that have an impact on yield formation processes in agriculture. Numerous methods demonstrate the potential of spectral reflectance data for the detection of qualitative and quantitative crop features but there is, however, no established methodology for the implementation of these data in operational crop production processes. The paper describes the main aspects of remote sensing based site characterization, considering major site variables (yield, soil) and plant parameters (nitrogen uptake) as key features for the description of the site specific variability in crops. Spectral reflectance data of the VIS/NIR region are transformed into different spectral indices for statistical analysis. Analyzing these indices it is found that the determination of a prediction model depends on the relevance of the suggested data fitting method (causality) as well as on the statistical significance of the interrelationship. Results point out that remote sensing data are suitable predictors for crop vitality and site characterization. Hence, the application of these data in agricultural work routines is limited by their quality and availability as well as by the influence of environmental factors on yield formation processes.