Remote Sensing · 2020 · 55 citations · 73 references
AgricultureAgronomyCrop SciencesCrop EcologyCrop SensingPlant EcophysiologyCrop GrowthCrop Stress DetectionPlant Stress DetectionVegetation Change DetectionVegetation IndicesVegetation MonitoringCanopy Spectral ReflectanceCrop Condition MonitoringCrop Growth DynamicsLeaf Area Index AssessmentLeaf Area Index MeasurementNormalized Difference Vegetation Index MappingVegetation Health AssessmentVegetation Stress Detection
Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify the crop response against elevated air temperatures, heat stress, and herbicide damage. The spectral reflectance, yield components, and growth parameters such as plant height, leaf area index (LAI), and above-ground dry matter of paddy rice, which was cultivated in a temperature gradient field chamber to simulate global warming conditions, were observed from 2016 to 2018. The relationships between the vegetation indices and the crop parameters were assessed considering stress conditions. The normalized difference vegetation index (NDVI) represented the changes in plant height (R-square = 0.93) and the LAI (R-square = 0.901) before the heading stage. Furthermore, the NDVI and the cumulative growing degree days had a Sigmoid curve and an R-square value of 0.937 under the normal growth case, but it decreased significantly in the herbicide damage case. This characteristic was useful for detecting the damaged crop growth condition. Additionally, to estimate the grain yield of paddy rice, the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index was better: R-square = 0.912; root mean square error = 95.69 g/m2. Photochemical reflectance index was sensitive to physiological stress caused by the heatwave, and it decreased in response to extremely high air temperatures. These results will contribute towards determining vegetation indices under stress conditions and how to effectively utilize them.
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Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Alfredo Huete, Kamel Didan, Tomoaki Miura et al. · Remote Sensing of Environment · 2002 · 9.6K citations
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A soil-adjusted vegetation index (SAVI)
Alfredo Huete · Remote Sensing of Environment · 1988 · 7.7K citations
Derivation of Leaf‐Area Index from Quality of Light on the Forest Floor
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Agriculture, Ecology, Plant Sciences +13