Abstract
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.
Highlights
Earth’s biosphere is exposed to increasing pressure from environmental changes, many of which are of anthropogenic cause
Further accuracy-limiting factors are of a technological nature, concerning the sensor’s spatial, radiometric, spectral, and temporal resolution [61], which can be summarized as sampling errors
As a consequence of the larger spatial resolution of satellite sensors, representative areas of ground observations have to be upscaled to approximate the target cell size. This principle is frequently applied to higher resolution satellite images to represent an estimation of the in situ state on a spatial grid and as an intermediate step for comparisons to data that has an even coarser resolution [65,70,71]. This two-stage process is subject to uncertainty itself, as the accuracy of ground-based reference maps depends on errors in field measurements and on uncertainties of fine resolution satellite data, and sampling and spatial scaling errors [72]
Summary
Earth’s biosphere is exposed to increasing pressure from environmental changes, many of which are of anthropogenic cause (e.g., global change [1]). A different approach to gain consistent time-series presents value compositing (e.g., maximum value), where multiple images are processed for a preset period of time to create representative, cloud-free datasets with the least atmospheric attenuation and viewing geometry effects [59]. This can reduce the impact of missing data and unexpected day-to-day variations. Further accuracy-limiting factors are of a technological nature, concerning the sensor’s spatial (geometric), radiometric, spectral, and temporal resolution [61], which can be summarized as sampling errors These errors become effective if information is assessed at a level of detail that cannot be properly sustained by the capabilities of the data acquisition mechanism. Time-series need to show temporal stability in their accuracy, since changing data quality over time would justifiably concern users that temporal trends observed in the data are ambiguous [63]
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