The sea state bias (SSB) is a large source of uncertainty in the estimation of sea level from satellite altimetry. It is still unclear to what extent it depends on errors in parameter estimations (numerical source) or to the wave physics (physical source). By improving the application of this correction we compute 20-Hz sea level anomalies that are about 30% more precise (i.e. less noisy) than the current standards. The improvement is twofold: first we prove that the SSB correction should be applied directly to the 20-Hz data (12 to 19% noise decrease); secondly, we show that by recomputing a regional SSB model (based on the 20-Hz estimations) even a simple parametric relation is sufficient to further improve the correction (further 15 to 19% noise decrease). We test our methodology using range, wave height and wind speed estimated with two retrackers applied to Jason-1 waveform data: the MLE4 retracked-data available in the Sensor Geophysical Data Records of the mission and the ALES retracked-data available in the OpenADB repository (https://openadb.dgfi.tum.de/). The regional SSB models are computed parametrically by means of a crossover analysis in the Mediterranean Sea and North Sea. Correcting the high-rate data for the SSB reduces the correlation between retracked parameters. Regional variations in the proposed models might be due to differences in wave climate and remaining sea-state dependent residual errors. The variations in the empirical model with respect to the retracker used recall the need for a specific SSB correction for any retracker. This study, while providing a significantly more precise solution to exploit high-rate * This is the accepted version of the manuscript identified as

Agriculture plays an essential role in national economic development. This fact made agricultural land one of the main unique production factors irreplaceable due to its importance in the agricultural business processes. However, a persistent problem of arable land conversion and land degradation have become more massive throughout the years. Meanwhile, the continuation of existing agricultural land and transformation into new agricultural land is inherently small. This research aimed to map agricultural land in sustainable agricultural development. Several transformative vegetation indices: NDVI, SAVI, and TSAVI, applied SPOT 6/7 satellite imagery in Lampung Province. Results show that the TSAVI value is the highest, with a 1.80 value, which indicates that this index value is very dense vegetation. Meanwhile, the NDVI index, which has a minimum value of -1.02, suggests that this index value is a non-vegetation object. However, high or low value does not indicate the rigorousness and Accuracy of an index. All three indices’ results are then overlaid with the satellite imagery classification process result. The accuracy result shows that the agricultural land has a maximum of 100% producer accuracy while the user accuracy value is 87.87%. Overall, for NDVI, the Accuracy was valued at 90.25%, which could be classified as a reasonable classification result. SAVI has a PA value of 97.85%, UA 85.20% and OA 86.63%, while the TSAVI Index has a PA value of 98.23%, UA 86.16% and OA 87.63%. This accuracy value indicates that the map has good results but judging from the magnitude of the highest accuracy value obtained from NDVI, it can be concluded that NDVI is the best index to determine paddy fieldsKeywords: Agricultural Land, SPOT 6/7, NDVI, SAVI, TSAVI.

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