Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

Abstract

Timely monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data. However, few such studies have been conducted on degraded vegetation. In this study, complete combinations of either original reflectance or first-order derivative spectra have been developed to quantify leaf nitrogen (N), phosphorus (P), and potassium (K) contents of tree, shrub, and grass species using hyperspectral datasets from light, moderate, and severely degraded vegetation sites in Helin County, China. Leaf N, P, and K contents were correlated to identify suitable combinations. The most effective combinations were those of reflectance difference (Dij), normalized differences (ND), first-order derivative (FD), and first-order derivative difference (FD(D)). Linear regression analysis was used to further optimize sensitive band-based combinations, which were compared with 43 frequently used empirical spectral indices. The proposed hyperspectral indices were shown to effectively quantify leaf N, P, and K content (R2 > 0.5, p < 0.05), confirming that hyperspectral data can be potentially used for fine scale monitoring of degraded vegetation.employed in biomes such as grasslands and savannas 1,18-20 , crops 21 , and trees 22 . A study of maize leaf P content found that 540, 720, 740, and 850 nm are the most sensitive bands for detection of P in both the vegetation production stage and the flowering stage 21 .The spectral region which most closely relates to leaf P content has been found to overlay the spectral region which demonstrates water absorbing traits (1000-2500 nm) 19 . Bands which are indicative of leaf P also lie in the region of 580-710 nm, although this varies among different case studies. The confusion between water absorption and sensitivity to sampling sites makes it difficult to identify the most suitable bands for leaf P estimation. In this study, we aimed to select several sensitive bands from the 500 available and develop a complete combination of reflectance and its first-order derivative (FD) from tree, shrub, and grass species in various degraded vegetation sites, with the objective of developing more general hyperspectral indices for the estimation of leaf P content.Finally, potassium (K) is also a key plant requirement, present mostly as K + ions in vacuoles. K provides regulatory control over processes such as transpiration, starch synthesis, sucrose translocation, respiration, and lipid synthesis 23 . Plants deficient in K exhibit limited growth, metabolism, and stress defense 24 , leading to lower overall biomass and coverage and changes to leaf color. If a K deficiency occurs at the vegetation level, this can accelerate the degradation process. Soils in many broad-acre semiarid areas have become deficient in K, resulting in a decrease of K in the canopy and stem 25,26 .Remote sensing of leaf N, P, or K contents is a challenging task due to the lack of direct absorption features that can be observed in t...

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

“…A poor prediction accuracy was found by [93] exploiting the Inverted red-edge chlorophyll index (R 2 = 0.66), relative normalized difference index (R 2 = 0.48), red-edge chlorophyll index (R 2 = 0.28), and normalized difference infrared index ranged R 2 = 0.28−0.67 for the coffee canopy N using satellite data. Thus, our results on the poor performance of spectral or vegetation indices are consistent with those reported by [11,93], among others. Prediction accuracy of R 2 = 0.16−0.48 was obtained by [94] to predict the N:P ratio of the grass vegetation using previously published vegetation indices computed from the satellite data however the performance was improved to R 2 = 0.59−0.72 with optimized vegetation indices.…”

“…Normalized difference spectral indices were effectively used by [91] and [92] to estimate leaf N, P, or K content in different plant species. In [11], a poor prediction of already published 43 empirical spectral indices for the N, P, and K content of the shrub and grass vegetation in China was recorded. Furthermore, to overcome this, the linear regression analysis to optimize the band-band combination was performed and effectively retrieved the leaf N, P, and K content (R 2 > 0.5, p < 0.05).…”

“…Over the last two decades, advancements in remote sensing technologies such as the use of reflectance spectroscopy, airborne and satellite technology, and statistical analysis approaches thereof have made it easy to understand several key processes and components of plants such as plant population [1][2][3], grain yield and biomass [4][5][6][7][8], pigment or chlorophyll [9][10][11], water stress response [12][13][14][15], nutritional status [16][17][18][19][20][21] or pest and disease identification [22][23][24][25]. Yet, in-field proximal sensing to estimate the nutritional status of the crops is an economical and technical challenge [26].…”

The distribution of climbing chalk on climbed boulders and its impact on rock‐dwelling fern and moss species

“…Furthermore, it is worthwhile to continue with an experimental approach by applying climbing chalk on unclimbed rock and study its in situ impact on rock‐dwelling species (besides bryophytes and ferns, also including lichens and flowering plants) on different rock types. Finally, recent advances in drone and imaging technology may allow for mapping and analyzing visible climbing chalk traces and vegetation health along climbing routes with hyperspectral imaging by means of unmanned aerial vehicles (Peng et al., 2020; Strumia, Buonanno, Aronne, Santo, & Santangelo, 2020; Zhang, Zhang, Wei, Wang, & Huang, 2020).…”

Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region

“…The major determining components of vegetation include biochemical constituents that are central to their physiological form and function, along with water, chlorophyll, and accessory pigments, nitrogen, cellulose, starch, sugars, lignin, and protein. These are the mandatory parameters for describing the nutritional status of any tree of a particular ecosystem [2,3], while the secondary metabolites such as terpenes, sesquiterpenes, phytosterols, etc., are more useful to humans [4], which makes the plant economically valuable.…”