Abstract

Highlights What are the main findings? The TRANCO approach is able to normalize time-series through time. The addition of TRANCO into a classifier improves its performance. What are the implications of the main findings? The tests performed in this research show that the TRANCO approach is able to generalize information outside its time component, allowing normalized time-series outside its year of origin to be used by classifiers. Such normalization of time-series could allow improving deeper classifiers, even empower the classification of past years' crops, in which ground truth data could be scarce, by training in recent years.Highlights What are the main findings? The TRANCO approach is able to normalize time-series through time. The addition of TRANCO into a classifier improves its performance. What are the implications of the main findings? The tests performed in this research show that the TRANCO approach is able to generalize information outside its time component, allowing normalized time-series outside its year of origin to be used by classifiers. Such normalization of time-series could allow improving deeper classifiers, even empower the classification of past years' crops, in which ground truth data could be scarce, by training in recent years.Abstract Crop type maps are essential for food security. However, there is a gap in information for worldwide maps that, at the same time, cover a wide period of time. The inability of classification algorithms to generalize information across years is one of the main reasons for this lack of information. This study aims to advance this direction by normalizing annual time series of wheat crops using the accumulation of Growing Degree Days (GDDs). Based on the Crop Data Layer (CDL) crop-type maps and Landsat 5, 7, and 8 imagery, we built yearly time series for the period 2008-2020. Then, we tested the performance of two normalization approaches: TRANCO, which uses Growing Degree Days (GDDs) and Crop Calendars to normalize time-series data; and Time Windows, which uses Crop Calendars to define wheat's biofix dates and normalize time-series data. Furthermore, we compared them with a Baseline, meaning a time series without further processing. Such performance was tested in two main ways: By computing the Jeffries-Matusita (JM) distances between time series and their average behavior, and by training random forest classifiers. For the latter, we defined a training period (2017-2020) during which we trained the models, and a validation period (2008-2016), during which we validated them on years the models were not trained on. We found that TRANCO was the best normalization approach for bringing the time-series closer to a common behavior (JM = 0.3), compared to Time windows (JM = 0.4) or the Baseline. Also, it achieved the best classification results (F1 = 0.779), compared to Time windows (F1 = 0.71) or the Baseline (F1 = 0.73), and, in addition, TRANCO's classifier was the most stable throughout the validation period, empowering past crop type classifications with classifiers trained in recent years.