Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion
Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied...
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/810 |
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| author | Nitzan Malachy Imri Zadak Offer Rozenstein |
| author_facet | Nitzan Malachy Imri Zadak Offer Rozenstein |
| author_sort | Nitzan Malachy |
| collection | DOAJ |
| description | Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (K<sub>c</sub>). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The K<sub>c</sub> time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between K<sub>c</sub> and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and K<sub>c</sub>. Height was best predicted using the Mean and the Sample methods for all three crops (R<sup>2</sup> = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of K<sub>c</sub> (R<sup>2</sup> = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications. |
| first_indexed | 2024-03-09T21:09:14Z |
| format | Article |
| id | doaj.art-74e62118304b4c3593b9f508a518fc8e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:09:14Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-74e62118304b4c3593b9f508a518fc8e2023-11-23T21:52:33ZengMDPI AGRemote Sensing2072-42922022-02-0114481010.3390/rs14040810Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from MotionNitzan Malachy0Imri Zadak1Offer Rozenstein2Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, Rishon LeZion 75359, IsraelInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, Rishon LeZion 75359, IsraelInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, Rishon LeZion 75359, IsraelAlthough it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (K<sub>c</sub>). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The K<sub>c</sub> time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between K<sub>c</sub> and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and K<sub>c</sub>. Height was best predicted using the Mean and the Sample methods for all three crops (R<sup>2</sup> = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of K<sub>c</sub> (R<sup>2</sup> = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications.https://www.mdpi.com/2072-4292/14/4/810crop coefficientstructure from motionunmanned aerial vehiclecrop height model |
| spellingShingle | Nitzan Malachy Imri Zadak Offer Rozenstein Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion Remote Sensing crop coefficient structure from motion unmanned aerial vehicle crop height model |
| title | Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion |
| title_full | Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion |
| title_fullStr | Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion |
| title_full_unstemmed | Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion |
| title_short | Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion |
| title_sort | comparing methods to extract crop height and estimate crop coefficient from uav imagery using structure from motion |
| topic | crop coefficient structure from motion unmanned aerial vehicle crop height model |
| url | https://www.mdpi.com/2072-4292/14/4/810 |
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