Abstract:
Reducing food insecurity in developing countries is one of the crucial targets of the
Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating
food insecurity. However, local planning agencies and governments do not have adequate spatial
information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized
Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and
to estimate maize production area as a parameter for supporting the SDGs. We used Principal
Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each
polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH),
and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM)
and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to
classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient
to map the distribution and estimate production areas for smallholder farms. An overall accuracy
of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference
in production area estimation observed between the two algorithms. This framework can be used
to generate spatial agricultural information in areas where agricultural survey data are limited and
for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal
data in conjunction with machine learning algorithms to map smallholder maize farms to support
the SDGs.
