# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Higher Level: Highly Analysis Ready Data
#### 3.3.4 Time Series Analysis/Level 4 Highly Analysis Ready Data+  
FORCE time series analysis (FORCE TSA) provides time series preparation and analysis functionality (Figure 10), i.e., extraction of quality-controlled time series with a number of aggregation and interpolation techniques, deriving land surface phenology metrics, and computing change and  
Figure 9: Best-available-pixel composite (near-infrared, shortwave infrared, red in RGB) for Angola, Zambia, Zimbabwe, Botswana, and Namibia. The 250, 25, and 2.5 km subsets provide different zoom levels of the composited data. The composite is temporally centered at the end of season land surface phenology metric for 2018. The land surface phenology was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and its spatial resolution was enhanced with the FORCE ImproPhe code (see Section 3.3.5).  
trend metrics. Complex processing workflows (example: Figure 11) can be executed in a single process. Many outputs of FORCE TSA are referred to as highly analysis ready data plus (hARD+), meaning that generated products can be directly ingested, analyzed, and interpreted in a geographic information system (GIS) to fuel research questions without any further processing.  
Figure 10: Processing workflow of FORCE time series analysis (TSA). All products indicated by a USB plug can be output; all products indicated by * can be centered/standardized before output.  
Processing is based on a spectral band (Table 1), spectral index (e.g. NDVI, for a full list see [42]), or fractional cover (using linear spectral mixture analysis [48] with custom endmembers). The full time series (limited by temporal filters, see Section 3.3.1) is generated, quality-controlled, and potentially output. The time series may be centered and/or standardized each pixel's mean and/or standard deviation before output as indication for vegetation under-/over-performance. A basic summary of the full time series can be generated, which includes per-pixel mean, standard deviation, minimum, and maximum.  
The time series may be interpolated/smoothed at equidistant time steps using linear interpolation, moving average filter, and radial basis function (RBF) filter ensembles. The RBF kernel strengths are adapted by weighting with actual data availability in each kernel [49].  
The full time series may be folded (aggregated) by year, month, week, or day--using mean, minimum, or maximum statistics. Folding by year is most common and generates annual time series (e.g., as employed by [50]). If folded by month, week, or day, the observations are pooled into a single virtual year, which gives up to 12, 52, or 365 values per pixel, and can, for e.g., be used to derive the long-term mean seasonality [51].  
The interpolated time series may be folded by year with the land surface phenology method, i.e., annual phenometrics are extracted using the Spline Analysis of Time Series (SPLITS) API [52], Twenty six metrics are available, which describe the timing and value of specific temporal points of interest, amplitudes, integrals, and durations.  
A time series analysis can be performed on any of the folded time series. In the case of land surface phenology, the analysis is performed for each photometric. Currently implemented analyses are linear trend analysis to derive long-term changes [53; 54] and an extended change, aftereffect, trend (CAT) transform [55] with full trend parameters for the three parts of the time series (example: Figure 11).