Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China

Abstract

Although herbage biomass and nutrient status are widely assessed from hyperspectral measurements, certain difficulties are encountered in semiarid and arid regions with low canopy cover. This study investigated the potential of band depth approaches using partial least squares (PLS) regression to estimate herbage biomass and the concentrations of nitrogen (N) and phosphorus (P) in the Inner Mongolia grassland. Field hyperspectral measurements and plant sampling were conducted in desert and typical steppes with different fertilizer levels. The PLS analyses of typical steppe, desert steppe and combined datasets were based on canopy reflectance and first derivative reflectance (FDR) at wavelengths of 400-1000 nm, with consideration of six band depth features extracted from the red absorption region (580-740 nm). The predictive accuracy of the standard full-spectrum PLS (FS-PLS) was compared with that of the iterative stepwise elimination PLS (ISE-PLS) via the cross-validated coefficient of determination (R 2 cv ) and the ratio of prediction to standard deviation (RPD). In most of the datasets, the ISE-PLS provided better predictive results than the FS-PLS. The final models used band depth features to estimate herbage biomass (R 2 cv = 0.624-0.952, RPD = 1.506-4.539) and pasture N (R 2 cv = 0.437-0.888, RPD = 1.331-2.869) and reflectance and FDR to estimate pasture P (R 2 cv = 0.686-0.815, RPD = 1.754-2.267). The models could accurately estimate most of the grass parameters (RPD >1.5), with the exception of pasture N concentrations in the desert steppe dataset due to a range of variation that was too small. The band depth approach with ISE-PLS improved the predictive ability of the method for estimating herbage biomass and the nutrient contents of grasses in sparse grasslands.

Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

“…The PLSR and MLR are popular regression techniques for developing spectral prediction in agricultural land use [29,58]. They are relatively easy to understand and implement compared to machine learning techniques and are practical chemometric analyses for spectral estimation of plant attributes [32,33]. The PLSR models were developed using a cross-validated approach, with 10 latent factors, on the calibration sets.…”

“…Band 11 and band 12 of Sentinel-2 images (Short wave infrared bands) were also found important for MLR prediction of BM and CP (Tables 7 and 8). Gong et al [32] found that short-wave infrared (1300-2500 nm) regions could be more efficient than visible regions to predict forage CP and nitrogen concentration. The absorbance spectra at 2060 nm, 2130 nm, 2180 nm, and 2240 nm are associated with N-H and C-H bonds of protein [79,80].…”

“…Multivariate analyses have been reported as practical statistical approaches for spectral modelling of plant biomass [30,31]. The partial least squares regression (PLSR) and stepwise multiple linear regression (MLR) models are the most common regression techniques which have been reported as practical approaches for spectral analyses [32,33]. The capability of these two techniques for predicting fresh GQ indicators in the temperate maritime climate in Ireland was assessed in this study.…”

Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau

“…As the plant undergoes senescence, chlorophyll decomposes gradually and the coverage and canopy density decrease rapidly; accordingly, variables that were previously sensitive to N and chlorophyll previously may become insensitive. However, some spectral variables, such as SAVI, OSAVI, BDR, and NBDI, may contribute significantly to the detection of N during this period because SAVI and OSAVI have the potential to effectively eliminate the soil background effect [47,78]; BDR and NBDI are calculated from the continuum-removed spectrum in which the absorption features of forage nutrients are strengthened [8,26]. These results support the findings of this study that the NBDI, OSAVI, and BDR are three of the first five most important variables for N estimation in GP170927, as shown in Table 3.…”

“…Most of the aforementioned hyperspectral parameters (i.e., VIs and absorption features) were proposed based on a specific growth period, especially the vigorous growing stage when the effect of biomass on N can be minimized [8,9]. Moreover, most studies have focused on the detection of spatial differences in forage N during a particular period without considering changes in the temporal dimension (different growth periods) [25,26]. Although these spectral variables have shown great potential in estimating N, whether their ability to estimate N differs during different growth periods should be investigated.…”