Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm−2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m−2 and 76.2 µmol m−2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2/m2 and 0.19 m2/m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.
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Language: | English |
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Elsevier B.V.
2023
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Small-Scale Croplands, Random Forest, HAIL DAMAGE, REMOTE SENSING, UNMANNED AERIAL VEHICLES, MAIZE, CROPS, SMALLHOLDERS, Sustainable Agrifood Systems, |
Online Access: | https://hdl.handle.net/10883/22746 |
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dig-cimmyt-10883-227462023-11-09T10:00:30Z Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system Sibanda, M. Ndlovu, H.S. Brewer, K. Buthelezi, S. Trylee N. Matongera Mutanga, O. Odidndi, J. Clulow, A.D. Chimonyo, V.G.P. Mabhaudhi, T. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm−2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m−2 and 76.2 µmol m−2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2/m2 and 0.19 m2/m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change. 2023-11-08T01:30:17Z 2023-11-08T01:30:17Z 2023 Article Published Version https://hdl.handle.net/10883/22746 10.1016/j.atech.2023.100325 English CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Netherlands Elsevier B.V. art. 100325 6 2772-3755 Smart Agricultural Technology |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems Sibanda, M. Ndlovu, H.S. Brewer, K. Buthelezi, S. Trylee N. Matongera Mutanga, O. Odidndi, J. Clulow, A.D. Chimonyo, V.G.P. Mabhaudhi, T. Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
description |
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm−2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m−2 and 76.2 µmol m−2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2/m2 and 0.19 m2/m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Small-Scale Croplands Random Forest HAIL DAMAGE REMOTE SENSING UNMANNED AERIAL VEHICLES MAIZE CROPS SMALLHOLDERS Sustainable Agrifood Systems |
author |
Sibanda, M. Ndlovu, H.S. Brewer, K. Buthelezi, S. Trylee N. Matongera Mutanga, O. Odidndi, J. Clulow, A.D. Chimonyo, V.G.P. Mabhaudhi, T. |
author_facet |
Sibanda, M. Ndlovu, H.S. Brewer, K. Buthelezi, S. Trylee N. Matongera Mutanga, O. Odidndi, J. Clulow, A.D. Chimonyo, V.G.P. Mabhaudhi, T. |
author_sort |
Sibanda, M. |
title |
Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
title_short |
Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
title_full |
Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
title_fullStr |
Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
title_full_unstemmed |
Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
title_sort |
remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system |
publisher |
Elsevier B.V. |
publishDate |
2023 |
url |
https://hdl.handle.net/10883/22746 |
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