Mapping Maize Phenological Stages Using Multi-Temporal Sentinel-1 SAR and Sentinel-2 Imagery in Zambia

Chenje Prassat Mtonga; Garikai Martin Membele

doi:10.53974/unza.jonas.7.1.1679

Chenje Prassat Mtonga University of Zambia
Garikai Martin Membele University of Zambia

DOI: https://doi.org/10.53974/unza.jonas.7.1.1679

Keywords: Remote Sensing, Sentinel-1 SAR, Sentinel-2, Maize Mapping, Random Forest Classification

Abstract

Traditional field-based techniques are primarily used to monitor and assess the potential maize harvest. This study aimed to integrate Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery to map maize fields and monitor their growth stages in the Kasisi area of Chongwe District, Zambia. Multi-temporal satellite data (biweekly from November 2019 to April 2020) were analysed to capture phenological changes of maize. Dual-polarised Sentinel-1 SAR (VV and VH) backscatter was combined with vegetation indices (Normalised Difference Vegetation Index – NDVI, and Normalised Difference Water Index – NDWI) from Sentinel-2. These features were used to train a Random Forest classifier to delineate maize fields, using field-collected training data. The results show that the classification had a very high overall accuracy of 96.7% (Kappa = 0.95), successfully distinguishing maize from other land covers. Maize fields were mapped, covering about 6721 ha (about 55% of the study area). Temporal analyses of SAR and NDVI identified four critical growth phases: sowing (early November 2019), emergence (mid-December 2019), maturity (mid-January to mid-February 2020), and harvest (March–April 2020). SAR backscatter increased during vegetative growth, stagnated at maturity, and declined after harvest. At the same time, NDVI trends peaked in late March and dropped by the end of April, confirming crop senescence in Zambia. The integration of radar and optical data proved effective for agricultural monitoring in a cloud-prone region, offering a scalable and timely approach to crop mapping. These results demonstrate that remote sensing can provide near-real-time information to support yield prediction, thereby supporting planning, decision-making, and policy interventions in the agriculture sector.

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