Maize yield estimation in intercropped smallholder fields using satellite data in southern Malawi

Li, C; Chimimba, EG; Kambombe, O; Brown, LA; Chibarabada, TP; Lu, Y; Anghileri, D; Ngongondo, C; Sheffield, J; Dash, J

Abstract

Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA because of small field sizes, widespread intercropping practices, and inadequate field observations. This study aimed to firstly evaluate the potential of satellite data in estimating maize yield in intercropped smallholder fields and secondly assess how factors such as satellite data spatial and temporal resolution, within-field variability, field size, harvest index and intercropping practices affect model performance. Having collected in situ data (field size, yield, intercrops occurrence, harvest index, and leaf area index), statistical models were developed to predict yield from multisource satellite data (i.e., Sentinel-2 and PlanetScope). Model accuracy and residuals were assessed against the above factors. Among 150 investigated fields, our study found that nearly half were intercropped with legumes, with an average plot size of 0.17 ha. Despite mixed pixels resulting from intercrops, the model based on the Sentinel-2 red-edge vegetation index (VI) could estimate maize yield with moderate accuracy (R2 = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R2 = 0.52, nRMSE = 19.95%). Seasonal peak VI values provided better accuracy than seasonal mean/median VI, suggesting peak VI values may capture the signal of the dominant upper maize foliage layer and may be less impacted by understory intercrop effects. Still, intercropping practice reduces model accuracy, as the model residuals are lower in fields with pure maize (1 t/ha) compared to intercropped fields (1.3 t/ha). This study provides a reference for operational maize yield estimation in intercropped smallholder fields, using free satellite data in Southern Malawi. It also highlights the difficulties of estimating yield in intercropped fields using satellite imagery, and stresses the importance of sufficient satellite observations for monitoring intercropping practices in SSA.

Citation

Li, C., Chimimba, E., Kambombe, O., Brown, L., Chibarabada, T., Lu, Y., …Dash, J. (2022). Maize yield estimation in intercropped smallholder fields using satellite data in southern Malawi. Remote Sensing, 14(10), https://doi.org/10.3390/rs14102458

• Journal Article Type: Article
• Publication Date: May 20, 2022
• Deposit Date: Oct 28, 2022
• Publicly Available Date: Oct 28, 2022
• Journal: Remote Sensing
• Publisher: MDPI
• Volume: 14
• Issue: 10
• DOI: https://doi.org/10.3390/rs14102458
• Publisher URL: https://doi.org/10.3390/rs14102458