Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals

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.

RETRACTED: Monitoring system of agricultural performance based on multisensor data and cross‐validation technique in merging temporal images

“…Another among those issues was often lacking adequate remote sensing information during agricultural control because of meteorological circumstances like fog. There have been numerous efforts that overcome issues, including an a technique described by Mashaba-Munghemezulu et al (2021), which substituted the absence of available material with knowledge of a combination of greater temporal data and poor remote sensing pictures. Two primary challenges which prohibit policymaking from using remote sensing information to track plants and predict agricultural output are environmental circumstances and inadequate chronological precision (Nguyen et al, 2021).…”

Vertical farm: prospects for achieving sustainable development goals

“…We found two articles that emphasized the importance of engaging small farmers in food production and fighting hunger, thus being able to collaborate in SDGs, but not with emphasis on vertical farms (Mashaba-Munghemezulu, et al, 2021, Vamuloh, et al 2019.…”

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

“…In addition, we also identified that land cover from Sentinel-1 and Sentinel-2 was also used in several works. Mainly, Sentinel-1 helped report indicator 6.6.1 [43], and it was also related to SDG 2 [57]. Meanwhile, Sentinel-2 was used to report indicators 15.1.1 and 15.1.2 [52], 11.1.1 and 11.3.1 [58], as well as new definitions for indicators 11.1.1, 11.2.1, 11.3.1, and 11.6.2 [59].…”