{"title":"Evaluation of average leaf inclination angle quantified by indirect optical instruments in crop fields","authors":"","doi":"10.1016/j.jag.2024.104206","DOIUrl":null,"url":null,"abstract":"<div><div>Average leaf inclination angle (<span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span>) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct <span><math><mrow><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover></mrow></math></span> measurements (R<sup>2</sup> = 0.54, RMSE = 7.37°; R<sup>2</sup> = 0.58, RMSE = 8.08°) than DHP (R<sup>2</sup> = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> values, and smaller gap fraction gradients led to smaller <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span> than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating <span><math><mrow><msub><mover><mrow><mi>θ</mi></mrow><mrow><mo>¯</mo></mrow></mover><mtext>L</mtext></msub></mrow></math></span>. We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843224005624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
Average leaf inclination angle () is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct measurements (R2 = 0.54, RMSE = 7.37°; R2 = 0.58, RMSE = 8.08°) than DHP (R2 = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger values, and smaller gap fraction gradients led to smaller values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating . We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.