TY - JOUR
T1 - Mapping tobacco planting areas in smallholder farmlands using phenological-spatial-temporal LSTM from time-series Sentinel-1 SAR images
AU - Li, Mengmeng
AU - Feng, Xiaomin
AU - Belgiu, Mariana
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/5
Y1 - 2024/5
N2 - Phenological information on crop growth aids in identifying crop types from remote sensing images, but its incorporation into classification models is insufficiently exploited, especially in deep learning frameworks. This study presents a new model, Phenological-Temporal-Spatial LSTM (PST-LSTM), for mapping tobacco planting areas in smallholder farmlands using time-series Sentinel-1 Synthetic Aperture Radar (SAR) images. The PST-LSTM model is built on a multi-modal learning framework that fuses phenological information with deep spatial–temporal features. We applied the model to extract tobacco planting areas in Ninghua, Pucheng, and Shanghang Counties, in Fujian Province, and Luoping County in Yunnan Province, China. We compared PST-LSTM with existing methods based on phenological rules and Dynamic Time Warping (DTW) methods, and analyzed its strength in feature fusion. Results showed that our model outperformed these methods, achieving an overall accuracy (OA) of 93.16% compared to 86.69% and 85.93% for the phenological rules and DTW methods, respectively, in the Ninghua area. PST-LSTM effectively integrated time-series data with phenological information derived from different strategies at the feature level and performed better than existing feature fusion methods (based upon fuzzy sets) at the decision level. It also demonstrated a better spatial transferability than other methods when applied to different study areas, achieving an OA of 90.95%, 91.41%, and 80.75% for the Pucheng, Shanghang, and Luoping areas, respectively, using training samples from Ninghua. We conclude that PST-LSTM can effectively extract tobacco planting areas in smallholder farming from time-series SAR images and has the potential for mapping other crop types.
AB - Phenological information on crop growth aids in identifying crop types from remote sensing images, but its incorporation into classification models is insufficiently exploited, especially in deep learning frameworks. This study presents a new model, Phenological-Temporal-Spatial LSTM (PST-LSTM), for mapping tobacco planting areas in smallholder farmlands using time-series Sentinel-1 Synthetic Aperture Radar (SAR) images. The PST-LSTM model is built on a multi-modal learning framework that fuses phenological information with deep spatial–temporal features. We applied the model to extract tobacco planting areas in Ninghua, Pucheng, and Shanghang Counties, in Fujian Province, and Luoping County in Yunnan Province, China. We compared PST-LSTM with existing methods based on phenological rules and Dynamic Time Warping (DTW) methods, and analyzed its strength in feature fusion. Results showed that our model outperformed these methods, achieving an overall accuracy (OA) of 93.16% compared to 86.69% and 85.93% for the phenological rules and DTW methods, respectively, in the Ninghua area. PST-LSTM effectively integrated time-series data with phenological information derived from different strategies at the feature level and performed better than existing feature fusion methods (based upon fuzzy sets) at the decision level. It also demonstrated a better spatial transferability than other methods when applied to different study areas, achieving an OA of 90.95%, 91.41%, and 80.75% for the Pucheng, Shanghang, and Luoping areas, respectively, using training samples from Ninghua. We conclude that PST-LSTM can effectively extract tobacco planting areas in smallholder farming from time-series SAR images and has the potential for mapping other crop types.
KW - Phenological knowledge
KW - Phenological-Temporal-Spatial LSTM (PST-LSTM)
KW - Smallholder farming
KW - Time-series SAR images
KW - Tobacco mapping
KW - ITC-GOLD
KW - ITC-ISI-JOURNAL-ARTICLE
U2 - 10.1016/j.jag.2024.103826
DO - 10.1016/j.jag.2024.103826
M3 - Article
AN - SCOPUS:85190148424
SN - 1569-8432
VL - 129
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
M1 - 103826
ER -