Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich s...

This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validati...

Knowledge of the spatial distribution of crop types is important for many environmental and human health research studies. These studies may require crop type maps over large geographical regions for multiple years, which can be derived only from satellite images. Interpretation of Landsat images using traditional image classification techniques is not feasible for such applications because of ...

A new tasselled cap transformation based on Landsat 7 at-satellite re ectance was developed. This transformation is most appropriate for regional applications where atmospheric correction is not feasible. The brightness, greenness and wetness of the derived transformation collectively explained over 97% of the spectral variance of the individual scenes used in this study.

The most widely known remote-sensing data are satellite images (typically LANDSAT or other visible imagery), which are used in the geosciences for a multitude of applications, including landuse, urbanization, sea-ice expansion, to name only a few. Commonly, these remote-sensing data are analyzed with the help of commercial software systems (eg. ENVI, ERDAS-IMAGINE). Analysis methods employ the ...

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat...

23 The quantification of uncertainty of global surface albedo data and products is a critical part 24 of producing complete, physically consistent, and decadal land property data records for studying 25 ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the 26 ability to overcome the spatial scaling errors that can contribute on the order of 20% disagr...

This study examines and validates a technique for spatial enhancement of thermal satellite images for urban heat island analysis, using a nighttime ASTER satellite image. The technique, termed Emissivity Modulation, enhances the spatial resolution while simultaneously correcting the image derived temperatures for emissivity differences of earth surface materials. A classified image derived from...

23 The quantification of uncertainty of global surface albedo data and products is a critical part 24 of producing complete, physically consistent, and decadal land property data records for studying 25 ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the 26 ability to overcome the spatial scaling errors that can contribute on the order of 20% disagr...