Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV
Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny ve...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/9/1691 |
| _version_ | 1797536105958998016 |
|---|---|
| author | Romina de Souza Claudia Buchhart Kurt Heil Jürgen Plass Francisco M. Padilla Urs Schmidhalter |
| author_facet | Romina de Souza Claudia Buchhart Kurt Heil Jürgen Plass Francisco M. Padilla Urs Schmidhalter |
| author_sort | Romina de Souza |
| collection | DOAJ |
| description | Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices. |
| first_indexed | 2024-03-10T11:54:55Z |
| format | Article |
| id | doaj.art-4cc984d171dd4fb3823b78da981b167d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:54:55Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4cc984d171dd4fb3823b78da981b167d2023-11-21T17:24:49ZengMDPI AGRemote Sensing2072-42922021-04-01139169110.3390/rs13091691Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAVRomina de Souza0Claudia Buchhart1Kurt Heil2Jürgen Plass3Francisco M. Padilla4Urs Schmidhalter5Plant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, GermanyPlant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, GermanyPlant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, GermanyPlant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, GermanyDepartment of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, SpainPlant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, GermanyOptical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices.https://www.mdpi.com/2072-4292/13/9/1691aerial sensingambient conditionsdronehigh-throughputprecision farmingphenotyping |
| spellingShingle | Romina de Souza Claudia Buchhart Kurt Heil Jürgen Plass Francisco M. Padilla Urs Schmidhalter Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV Remote Sensing aerial sensing ambient conditions drone high-throughput precision farming phenotyping |
| title | Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV |
| title_full | Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV |
| title_fullStr | Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV |
| title_full_unstemmed | Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV |
| title_short | Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV |
| title_sort | effect of time of day and sky conditions on different vegetation indices calculated from active and passive sensors and images taken from uav |
| topic | aerial sensing ambient conditions drone high-throughput precision farming phenotyping |
| url | https://www.mdpi.com/2072-4292/13/9/1691 |
| work_keys_str_mv | AT rominadesouza effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav AT claudiabuchhart effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav AT kurtheil effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav AT jurgenplass effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav AT franciscompadilla effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav AT ursschmidhalter effectoftimeofdayandskyconditionsondifferentvegetationindicescalculatedfromactiveandpassivesensorsandimagestakenfromuav |