TY - JOUR
T1 - TRANCO
T2 - Thermo radiometric normalization of crop observations
AU - Cintas, Juanma
AU - Franch, Belen
AU - Van-Tricht, Kristof
AU - Boogaard, Hendrik
AU - Degerickx, Jeroen
AU - Becker-Reshef, Inbal
AU - Moletto-Lobos, Italo
AU - Mollà-Bononad, Bertran
AU - Sobrino, Jose A.
AU - Gilliams, Sven
AU - Szantoi, Zoltan
PY - 2023/4
Y1 - 2023/4
N2 - Crop type maps are essential for a wide range of applications such as crop monitoring, and yield estimation. In addition, Earth Observation (EO) systems allow robust and timely mapping of the earth's surface, usually based on time-series. Yet, existing crop type maps are either global at coarse spatial resolution, or have a local or regional scope. The reasons for this gap can be linked to the scarcity of global crop type datasets at field level to train the models, their bias towards the Northern Hemisphere, as well as the limited transferability of existing crop type models across different regions. One of the main limitations on the transferability is driven by the phenological shift of the crops’ radiometric time series detected by Earth Observation (EO) systems, which is mainly induced by the different climates across regions. In this study, we explore the normalization of EO-based wheat time series with the accumulation of Growing Degree Days (GDD): the Thermo-RAdiometric Normalization of Crop Observations (TRANCO) system. The TRANCO system is based on the assumption that crop phenology evolution is mainly driven by temperature accumulation, represented by the accumulated GDD from Start of Season (SOS) to End of Season (EOS) dates, derived from a crop calendar. We tested the proposed method to normalize wheat on a database of globally distributed samples, whose results show a great improvement of the GDD F1 score (0.90) compared to a simpler normalization approach based on Time windows defined from SOS calendars (0.87) and a baseline without normalization (0.83).
AB - Crop type maps are essential for a wide range of applications such as crop monitoring, and yield estimation. In addition, Earth Observation (EO) systems allow robust and timely mapping of the earth's surface, usually based on time-series. Yet, existing crop type maps are either global at coarse spatial resolution, or have a local or regional scope. The reasons for this gap can be linked to the scarcity of global crop type datasets at field level to train the models, their bias towards the Northern Hemisphere, as well as the limited transferability of existing crop type models across different regions. One of the main limitations on the transferability is driven by the phenological shift of the crops’ radiometric time series detected by Earth Observation (EO) systems, which is mainly induced by the different climates across regions. In this study, we explore the normalization of EO-based wheat time series with the accumulation of Growing Degree Days (GDD): the Thermo-RAdiometric Normalization of Crop Observations (TRANCO) system. The TRANCO system is based on the assumption that crop phenology evolution is mainly driven by temperature accumulation, represented by the accumulated GDD from Start of Season (SOS) to End of Season (EOS) dates, derived from a crop calendar. We tested the proposed method to normalize wheat on a database of globally distributed samples, whose results show a great improvement of the GDD F1 score (0.90) compared to a simpler normalization approach based on Time windows defined from SOS calendars (0.87) and a baseline without normalization (0.83).
KW - Crop type classification
KW - GDD
KW - MSMD
KW - Remote Sensing
KW - Spatial cross-validation
U2 - 10.1016/j.jag.2023.103283
DO - 10.1016/j.jag.2023.103283
M3 - Article
AN - SCOPUS:85151613153
SN - 1569-8432
VL - 118
JO - International Journal of applied Earth Observation and Geoinformation
JF - International Journal of applied Earth Observation and Geoinformation
M1 - 103283
ER -