An optimal composite interval index to produce remote-sensing time-series data

Complete remote-sensing time series with consistent length are important for obtaining reliable analytical results in regional applications. Temporal composite reconciles incomplete raw remote-sensing time series into a time series with fixed and equidistant time intervals, forming the basis for applications like crop classification. To our knowledge, no algorithms or indicators exist for determining the optimal composite interval while quantitatively considering cloud conditions and image acquisition capabilities across different latitudes. As a pioneering effort, we propose an optimal composite interval index (OCII) for producing remote-sensing composite time series. This index first calculates the proportion of valid (cloud-free) composite observations and the information loss from compositing, then describes the trade-off between these two aspects with a simple normalized difference form. We tested OCII using crop classification as an example, accessing classification accuracy with composite time-series data of varying intervals. Experimental results from three regions with different geographic conditions show that OCII suggested 16-day, 25-day and 30-day intervals for the sites with low cloud cover (37.56%), medium cloud cover (56.37%), and high cloud cover (82.11%), respectively. Classification accuracy was poor with either too short or too large composite intervals, and the optimal composite interval derived from OCII achieved a relative accuracy increase of 2.8-10.2%. This underscores the effectiveness of OCII considering differences in data availability at clear and cloudy sites. Calculating OCII requires only the data quality layer and can be easily implemented in various areas using the Google Earth Engine platform. We believe that OCII has great potential for crop classifications and other applications of remote-sensing time-series data.