Abstract
Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).
Highlights
Landsat data are one of the most important Earth Observation datasets ever acquired due to their long-term availability on regional to global scales [1,2,3], data continuity between sensors [4], high quality ofcalibration (e.g., [5,6]), free and open data access [7], and adequate spatial, temporal, and spectral resolutions to monitor many processes on the landscape level [8]
Necessary processing steps include (1) cloud and cloud shadow detection on a per-pixel basis, including quality flags that indicate only partially or not usable observations from other factors than clouds or cloud shadows like saturation, snow, illumination conditions etc.; (2) radiometric normalization and transformation of top-of-atmosphere reflectance to surface reflectance, which includes at least atmospheric correction (AC), but other corrections like topographic or adjacency effect correction may be required as well; and (3) mapping the data onto a uniform spatial grid, which is highly advantageous for data-intensive applications
The global Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor database was generated with the designated FORCE module WVDB [12], which was implemented for automatic data acquisition and processing of Terra/Aqua MODIS water vapor data; for details about this methodology see Frantz et al (2016) [14]
Summary
Landsat data are one of the most important Earth Observation datasets ever acquired due to their long-term availability on regional to global scales [1,2,3], data continuity between sensors [4], high quality of (inter-)calibration (e.g., [5,6]), free and open data access [7], and adequate spatial, temporal, and spectral resolutions to monitor many processes on the landscape level [8]. Necessary processing steps include (1) cloud and cloud shadow detection on a per-pixel basis, including quality flags that indicate only partially or not usable observations from other factors than clouds or cloud shadows like saturation, snow, illumination conditions etc.; (2) radiometric normalization and transformation of top-of-atmosphere reflectance to surface reflectance, which includes at least atmospheric correction (AC), but other corrections like topographic or adjacency effect correction may be required as well; and (3) mapping the data onto a uniform spatial grid (often referred to as data cubing), which is highly advantageous for data-intensive applications. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring, available from http://force.feut.de) is a toolkit for generating ARD as well as for deriving higher-level products from the generated ARD (such as pixel-based composites, spectral variability metrics, time series derivatives like trend and change indicators, Land Surface Phenology metrics, etc.). The quality of the FORCE AC was recently assessed in the Atmospheric Correction Inter-Comparison Exercise (ACIX) [15]
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