Estimation of vertical distribution of chlorophyll concentration by bi-directional canopy reflectance spectra in winter wheat

Abstract

An effective technique to measure foliage chlorophyll concentration (Chl) at a large scale and within a short time could be a powerful tool to determine fertilization amount for crop management. The objective of this study was to investigate the inversion of foliage Chl vertical-layer distribution by bi-directional reflectance difference function (BRDF) data, so as to provide a theoretical basis for monitoring the growth and development of winter wheat and for providing guidance on the application of fertilizer. Remote sensing could provide a powerful tool for large-area estimation of Chl. Because of the vertical distribution of leaves in a wheat stem, Chl vertical distribution characteristics show an obvious decreasing trend from the top of the canopy to the ground surface. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized soil adjusted Wenjiang Huang is an associate professor of application of quantitative remote sensing in agriculture. vegetation index (OSAVI) was called the canopy chlorophyll inversion index (CCII) in this study. The value of CCII at nadir, ±20 and ±30°, at nadir, ±30 and ±40°, and at nadir, ±50 and ±60°view angles were selected and assembled as bottom-layer Chl inversion index (BLCI), middle-layer Chl inversion index (MLCI), and upper-layer Chl inversion index (ULCI), respectively, for the inversion of Chl at the vertical bottom layer, middle layer, and upper layer. The root mean squared error (RMSE) between BLCI-, MLCI-, and ULCI-derived and laboratory-measured Chl were 0.7841, 0.9426, and 1.7398, respectively. The vertical foliage Chl inversion could be used to monitor the crop growth status and to guide fertilizer and irrigation management. The results suggested that vegetation indices derived from bi-directional reflectance spectra (e.g., BLCI, ULCI, and MLCI) were satisfactory for inversion of the Chl vertical distribution.

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

“…For more advanced analysis of reflectance anisotropy, there still may be benefits in collecting full hyperspectral data. An interesting approach to reflectance anisotropy is to consider that from different view angles the observer detects different portions of the top-most and the deeper layers of the canopy [54]. In traditional remote sensing, it is possible to estimate leaf pigment concentrations in the canopy using hyperspectral data.…”

Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics

“…Various studies reported that the vertical leaf N distribution of a plant canopy is non-uniform; i.e., the shaded lower leaves generally have a lower N content than the upper illuminated leaves [17][18][19][20][21]. It has also been shown that the lower leaves are more sensitive to N deficiency than the upper leaves [22].…”

Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat

“…Surface reflectance and radiation characteristics will change as the observation angle varies (Asner et al 1998;Despan et al 1999). In this case, the observed vertical inversion model was insufficient to accurately reflect the structure, and physiological and biochemical characteristics of the crop canopy, which would affect the models application in large-scale satellite monitoring (Boyd et al 2002;Huang et al 2011). Compared with vertical observations, multi-angle remote sensing can monitor the target in many directions and obtain more abundant, detailed, and reliable information regarding the targeted object, which would provide a new method of quantitative remote sensing (Gao et al 2003;Wu et al 2010).…”