Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities

Abstract

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level.

Remote Estimation of Crop Chlorophyll Content by Means of High‐Spectral‐Resolution Reflectance Techniques

“…The overall good correlation and relatively low RMSE obtained by the NAOC supported confidence in the utility of the proposed technique across a broad range of agricultural areas. This same impression also arose in a review of upscaling remote sensing methods from leaf to canopy level (He and Mui, 2010), where it was remarked that the NAOC would benefit from testing this promising technique over other vegetation types such as forested areas, grasslands, and natural vegetation.…”

senSCOPE: Modeling radiative transfer and biochemical processes in mixed canopies combining green and senescent leaves with SCOPE

“…The presence of non-photosynthetic elements in the canopy has 100 been already addressed in turbid medium RTM (Bach et al 2001;Braswell et al 1996;Wenhan 1993) 101 and used to improve the estimation of biophysical parameters such as leaf area index (LAI) or 102 chlorophyll concentration (C ab ) or the fraction of absorbed photosynthetically active radiation (Houborg Therefore, in multi-species grasslands senescence and degradation can take place at different rates and 130 periods, increasing the variability of surface biophysical and optical properties as well as the complexity 131 of modeling and characterization. In fact, senescent material and litter are nowadays considered a 132 challenge for the estimation of biophysical properties in semi-arid grasslands (He and Mui 2010). 133…”

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

“…In contrast, hyperspectral sensors have 100s to 1000s of narrow wavebands providing continuous coverage across the spectral profile. Such hyperspectral data have been used for many applications, including invasive species control [9][10][11], biodiversity estimation [12], vegetation/land cover/crop residue classification [13][14][15][16][17][18][19][20], biochemical characteristics modeling [21], pollution assessment [22][23][24], and various agricultural applications [25][26][27].…”