Additional work on improving cloud screening algorithms may also be fruitful if it decreases missingness by omitting fewer retrievals not contaminated by clouds or omits retrievals currently suffering from contamination.
However, these data require substantial processing for applications in spatial analyses and are sensitive to cloud contamination [ 37]; these data may not be useful for regions with cloud forest and rainforest.
However, noise induced by cloud contamination and atmospheric variability impedes the analysis and application of NDVI data.
Frequent data gaps, e.g. due to cloud contamination, impose a serious challenge for such time series analyses.
Using UAVs avoids many of the limitations associated with satellite data (e.g., long repeat times, cloud contamination, low spatial resolution).
Ground-truthed fire sizes and shapes were correlated with the AVHRR/NDVI-mapped areas; however, light cloud contamination increased false detection of fires due to NDVI suppression.
However, affected by temporal revisit rates and cloud contamination, Landsat time series can only capture very limited temporal segments of vegetation phenology.
While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination.
The ability of estimating wavenumber offset a priori is most stable for 0.27 convolutionution kernel, which can accurately find the reference line from GOSAT 1 September 2013 global measurements with SNR larger than 100 and without cloud contamination.
However, due to frequent cloud contamination and trade-offs in satellite sensor design, the FVC estimates from remote sensing data are not continuous, either spatially or temporally, and cannot simultaneously depict details in spatio-temporal variation.
