The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing
Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological...
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| author | Bin Yang Wanxue Zhu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Junqiang Zhang |
| author_facet | Bin Yang Wanxue Zhu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Junqiang Zhang |
| author_sort | Bin Yang |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (<i>R</i><sup>2</sup> increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (<i>R</i><sup>2</sup> = 0.93, RMSE = 0.77 t·ha<sup>−1</sup>). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction. |
| first_indexed | 2024-03-09T11:28:26Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:28:26Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5ca1396836b94c2db592d62b9527f4272023-11-30T23:55:44ZengMDPI AGRemote Sensing2072-42922022-03-01147155910.3390/rs14071559The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote SensingBin Yang0Wanxue Zhu1Ehsan Eyshi Rezaei2Jing Li3Zhigang Sun4Junqiang Zhang5CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaLeibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, GermanyKey Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaYusense Information Technology and Equipment (Qingdao) Inc., Qingdao 266000, ChinaUnmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (<i>R</i><sup>2</sup> increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (<i>R</i><sup>2</sup> = 0.93, RMSE = 0.77 t·ha<sup>−1</sup>). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.https://www.mdpi.com/2072-4292/14/7/1559unmanned aerial vehicleremote sensingmaizeyieldmultispectral |
| spellingShingle | Bin Yang Wanxue Zhu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Junqiang Zhang The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing Remote Sensing unmanned aerial vehicle remote sensing maize yield multispectral |
| title | The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing |
| title_full | The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing |
| title_fullStr | The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing |
| title_full_unstemmed | The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing |
| title_short | The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing |
| title_sort | optimal phenological phase of maize for yield prediction with high frequency uav remote sensing |
| topic | unmanned aerial vehicle remote sensing maize yield multispectral |
| url | https://www.mdpi.com/2072-4292/14/7/1559 |
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