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

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

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 (R 2 = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R 2 = 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.

PlanetScope, Sentinel-2, Sub-Saharan Africa, accuracy assessment, empirical model, field size, harvest index, optimal spatial resolution, temporal aggregation methods
2072-4292
Li, Chengxiu
adaf46fc-1573-4c50-bd7f-b2e7ed048f7e
Chimimba, Ellasy
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Kambombe, Oscar
1bb3e3f9-86df-4b29-bc5f-95c57b198557
Brown, Luke
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Chibarabada, Tendai
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Lu, Yang
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Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698
Ngongondo, Cosmo
745fdb58-cb31-434a-84c1-1a457bd4ed6e
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Li, Chengxiu
adaf46fc-1573-4c50-bd7f-b2e7ed048f7e
Chimimba, Ellasy
7d2af6e9-c10b-4ae3-aebc-a6a136c96bed
Kambombe, Oscar
1bb3e3f9-86df-4b29-bc5f-95c57b198557
Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
Chibarabada, Tendai
b862fa54-c58a-448f-a71e-e7b0fc2114e0
Lu, Yang
6d9d9d4f-3177-4265-b03b-34d7129ec95c
Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698
Ngongondo, Cosmo
745fdb58-cb31-434a-84c1-1a457bd4ed6e
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8

Li, Chengxiu, Chimimba, Ellasy, Kambombe, Oscar, Brown, Luke, Chibarabada, Tendai, Lu, Yang, Anghileri, Daniela, Ngongondo, Cosmo, Sheffield, Justin and Dash, Jadunandan (2022) Maize yield estimation in intercropped smallholder fields using satellite data in southern Malawi. Remote Sensing, 14 (10), [2458]. (doi:10.3390/rs14102458).

Record type: Article

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 (R 2 = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R 2 = 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.

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Accepted/In Press date: 12 May 2022
e-pub ahead of print date: 20 May 2022
Published date: 20 May 2022
Additional Information: Funding Information: This work and APC was funded through the ‘Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa’ (BRECcIA) which is supported by UK Research and Innovation as part of the Global Challenges Research Fund, grant number NE/P021093/1.Acknowledgments: Special thanks to the Ministry of Agriculture, Planning Department of Malawi for providing the agricultural production estimate data used in this study. We are thankful to European Space Agency’s Third Party Missions programme for providing Planetscope data for the project (Project ID: 59585). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: PlanetScope, Sentinel-2, Sub-Saharan Africa, accuracy assessment, empirical model, field size, harvest index, optimal spatial resolution, temporal aggregation methods

Identifiers

Local EPrints ID: 467918
URI: http://eprints.soton.ac.uk/id/eprint/467918
ISSN: 2072-4292
PURE UUID: f00d7473-ef0f-4489-ab52-cf8ab1824c0d
ORCID for Luke Brown: ORCID iD orcid.org/0000-0003-4807-9056
ORCID for Daniela Anghileri: ORCID iD orcid.org/0000-0001-6220-8593
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

Catalogue record

Date deposited: 25 Jul 2022 16:46
Last modified: 06 Nov 2024 02:57

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Contributors

Author: Chengxiu Li
Author: Ellasy Chimimba
Author: Oscar Kambombe
Author: Luke Brown ORCID iD
Author: Tendai Chibarabada
Author: Yang Lu
Author: Cosmo Ngongondo
Author: Jadunandan Dash ORCID iD

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