Abstract
Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.
Highlights
Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration [1,2,3].Determining the optimal amount of nitrogen fertilization to match the demands of crop growth is critical for improving grain yield and reducing environmental impacts [4,5]
In this study, we proposed a method for assessing vertical foliage nitrogen distribution in winter wheat by bi-directional reflectance difference function (BRDF) data
According to the correlation analysis results of single VIs with foliage nitrogen density (FND) in Table 2, Normalized Difference Vegetation Index (NDVI) showed the best relationship with foliage total nitrogen density (R2 = 0.63), followed by Nitrogen Reflectance Index (NRI) (R2 = 0.61) and Normalized Pigment Chlorophyll Index (NPCI) (R2 = 0.60)
Summary
Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration [1,2,3]. Confirmed that the Normalized Pigment Chlorophyll Index (NPCI) offered a potential way for measuring nitrogen status of wheat. Most of these studies with those indices focus on assessing canopy. In comparison with a single view from vertical canopy, multi-angular observations can acquire more rich plant information by considering more canopy parameters They have been used to detect foliage disturbances in forest ecosystems and to retrieve chlorophyll vertical distribution in winter wheat [24,25]. In this study, we proposed a method for assessing vertical foliage nitrogen distribution in winter wheat by bi-directional reflectance difference function (BRDF) data. Our objectives for this analysis are to: (1) determine sensitive vegetation indices and viewing angles to estimate foliage nitrogen density for each layer (bottom, middle and top) of winter wheat and (2) select an optical model from three partial least square regression (PLSR) models for assessing foliage nitrogen density at each layer
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