It is possible to infer the genetically modified species by using remotely sensed data. Using ERDAS software the algorithm of BT (Bacillus thuringiensis) Cotton in Punjab, India was developed successfully. GPS enabled space technology has the potential to identify the exact location of Bt Cotton by generating Normalized Difference Vegetation Index (NDVI) for the calculation of total area covered by this species. It was possible to develop a correlation in between genetically modified Cotton crop and NDVI value. In parts of Bhatinda district of Punjab the yield of Bt Cotton and NDVI showing R2 value of more than 4.5 in regression analysis. A correlation matrix was also generated which shows that NDVI values of BT cotton has reasonably acceptable correlation with Total Dissolved Solids (TDS) of soil and water also.

It is possible to infer the genetically modified species by using remotely sensed data. Using ERDAS software the algorithm of BT (Bacillus thuringiensis) Cotton in Punjab, India was developed successfully. GPS enabled space technology has the potential to identify the exact location of Bt Cotton by generating Normalized Difference Vegetation Index (NDVI) for the calculation of total area covered by this species. It was possible to develop a correlation in between genetically modified Cotton crop and NDVI value. In parts of Bhatinda district of Punjab the yield of Bt Cotton and NDVI showing R 2 value of more than 4.5 in regression analysis. A correlation matrix was also generated which shows that NDVI values of BT cotton has reasonably acceptable correlation with Total Dissolved Solids (TDS) of soil and water also. Through remote sensing it is possible to quantify on a global scale the total acreage dedicated to these and other crops at any time. Of greater importance is accurately (best case 90%) estimating the expected yields of each crop locally, regionally or globally. It can be done by first computing the areas dedicated to each crop and then incorporating reliable yield assessment per unit area, which can be measured at representative ground truth sites. Usually, the yield estimates obtained from satellite data are more comprehensive and earlier (often by weeks) than determined conventionally as harvesting approaches 1 . Use of Satellite data for genetically modified crop needs to generate location specific spectral anomalies 2,3 . Use of multispectral satellite data helps to generate NDVI (Normalized Difference Vegetation Index), which can be correlated with the landuse, landcover, soil moisture, soil quality and groundwater quality to estimate the deterministic yield of BT cotton crops 4,5,6 .

The quality of Remote Sensing data is an important parameter that defines the extent of its usability in various applications. The data from Remote Sensing satellites is received as raw data frames at the ground station. This data may be corrupted with data losses due to interferences during data transmission, data acquisition and sensor anomalies. Thus it is important to assess the quality of the raw data before product generation for early anomaly detection, faster corrective actions and product rejection minimization. Manual screening of raw images is a time consuming process and not very accurate. In this paper, an automated process for identification and quantification of losses in raw data like pixel drop out, line loss and data loss due to sensor anomalies is discussed. Quality assessment of raw scenes based on these losses is also explained. This process is introduced in the data pre-processing stage and gives crucial data quality information to users at the time of browsing data for product ordering. It has also improved the product generation workflow by enabling faster and more accurate quality estimation.

The quality of Remote Sensing data is an important parameter that defines the extent of its usability in various applications. The data from Remote Sensing satellites is received as raw data frames at the ground station. This data may be corrupted with data losses due to interferences during data transmission, data acquisition and sensor anomalies. Thus it is important to assess the quality of the raw data before product generation for early anomaly detection, faster corrective actions and product rejection minimization. Manual screening of raw images is a time consuming process and not very accurate. In this paper, an automated process for identification and quantification of losses in raw data like pixel drop out, line loss and data loss due to sensor anomalies is discussed. Quality assessment of raw scenes based on these losses is also explained. This process is introduced in the data pre-processing stage and gives crucial data quality information to users at the time of browsing data for product ordering. It has also improved the product generation workflow by enabling faster and more accurate quality estimation.

It is possible to infer the genetically modified species by using remotely sensed data. Using ERDAS software the algorithm of BT (Bacillus thuringiensis) Cotton in Punjab, India was developed successfully. GPS enabled space technology has the potential to identify the exact location of Bt Cotton by generating Normalized Difference Vegetation Index (NDVI) for the calculation of total area covered by this species. It was possible to develop a correlation in between genetically modified Cotton crop and NDVI value. In parts of Bhatinda district of Punjab the yield of Bt Cotton and NDVI showing R 2 value of more than 4.5 in regression analysis. A correlation matrix was also generated which shows that NDVI values of BT cotton has reasonably acceptable correlation with Total Dissolved Solids (TDS) of soil and water also. Through remote sensing it is possible to quantify on a global scale the total acreage dedicated to these and other crops at any time. Of greater importance is accurately (best case 90%) estimating the expected yields of each crop locally, regionally or globally. It can be done by first computing the areas dedicated to each crop and then incorporating reliable yield assessment per unit area, which can be measured at representative ground truth sites. Usually, the yield estimates obtained from satellite data are more comprehensive and earlier (often by weeks) than determined conventionally as harvesting approaches 1 . Use of Satellite data for genetically modified crop needs to generate location specific spectral anomalies 2,3 . Use of multispectral satellite data helps to generate NDVI (Normalized Difference Vegetation Index), which can be correlated with the landuse, landcover, soil moisture, soil quality and groundwater quality to estimate the deterministic yield of BT cotton crops 4,5,6 .