Modified Vegetation Detection Index Using Different-Spectral Signature

The Normalization Difference Vegetation Index (NDVI), for many years, was widely used in remote sensing for the detection of vegetation land cover. This index uses red channel radiances (i.e., 0.66 μm reflectance) and near-IR channel (i.e., 0.86 μm reflectance). In the heavy chlorophyll absorption area, the red channel is located, while in the high reflectance plateau of vegetation canopies, the Near-IR channel is situated. Senses of channels (Red & Near- IR) read variance depths over vegetation canopies. In the present study, a further index for vegetation identification is proposed. The normalized difference vegetation shortwave index (NDVSI) is defined as the difference between the cubic bands of Near- IR and Shortwave infrared radiation (SWIR) divided by their sums. The radiances or reflectances are included in this index from the Near-IR channel and WSIR2 channel (2.1 μm). The NDVSI is less sensitivite to atmospheric effects as compared to NDVI. By comparing the one NDVSI index with the two indexes (NDVI, SAVI) of vegetation cover, good correlations were found between NDVI and NDVSI (R2=0.917) and between SAVI and NDVSI (R2=0.809. Accordingly, the proposed index can be taken into consideration as an independent vegetation index

Effects of Plant Crown Shape on Microwave Backscattering Coefficients of Vegetation Canopy

A microwave scattering model is a powerful tool for determining relationships between vegetation parameters and backscattering characteristics. The crown shape of the vegetation canopy is an important parameter in forestry and affects the microwave scattering modeling results. However, there are few numerical models or methods to describe the relationships between crown shapes and backscattering features. Using the Modified Tor Vergata Model (MTVM), a microwave scattering model based on the Matrix Doubling Algorithm (MDA), we quantitatively characterized the effects of crown shape on the microwave backscattering coefficients of the vegetation canopy. FEKO was also used as a computational electromagnetic method to make a complement and comparison with MTVM. In a preliminary experiment, the backscattering coefficients of two ideal vegetation canopies with four representative crown shapes (cylinder, cone, inverted cone and ellipsoid) were simulated: MTVM simulations were performed for the L (1.2 GHz), C (5.3 GHz) and X (9.6 GHz) bands in fully polarimetric mode, and FEKO simulations were carried out for the C (5.3 GHz) band at VV and VH polarization. The simulation results show that, for specific input parameters, the mean relative differences in backscattering coefficients due to variations in crown shape are as high as 127%, which demonstrates that the crown shape has a non-negligible influence on microwave backscattering coefficients of the vegetation canopy. In turn, this also suggests that investigation on effects of plant crown shape on microwave backscattering coefficients may have the potential to improve the accuracy of vegetation microwave scattering models, especially in canopies where volume scattering is the predominant mechanism.

Vegetation Angular Signatures of Equatorial Forests From DSCOVR EPIC and Terra MISR Observations

In vegetation canopies cross-shading between finite dimensional leaves leads to a peak in reflectance in the retro-illumination direction. This effect is called the hot spot in optical remote sensing. The hotspot region in reflectance of vegetated surfaces represents the most information-rich directions in the angular distribution of canopy reflected radiation. This paper presents a new approach for generating hot spot signatures of equatorial forests from synergistic analyses of multiangle observations from the Multiangle Imaging SpectroRadiometer (MISR) on Terra platform and near backscattering reflectance data from the Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR). A canopy radiation model parameterized in terms of canopy spectral invariants underlies the theoretical basis for joining Terra MISR and DSCOVR EPIC data. The proposed model can accurately reproduce both MISR angular signatures acquired at 10:30 local solar time and diurnal courses of EPIC reflectance (NRMSE < 9%, R2 > 0.8). Analyses of time series of the hot spot signature suggest its ability to unambiguously detect seasonal changes of equatorial forests.

SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals

The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared, and thermal domains with land surface processes in a physically based manner, and quantifying the microclimate in vegetation canopies. It simulates radiative transfer in the soil, leaves, and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 12 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary production (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) it enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understorey or with a vertical gradient in leaf chlorophyll concentration; (2) it enables the simulation of soil reflectance; (3) it includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) the computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.

Inversion of Solar-Induced Chlorophyll Fluorescence Using Polarization Measurements of Vegetation

In vegetation remote sensing, the apparent radiation of the vegetation canopy is often combined with three components derived from different parts of vegetation that have different production mechanisms and optical properties: volume scattering Lvol, polarized light Lpol, and chlorophyll fluorescence ChlF. The chlorophyll fluorescence plays a very important role in vegetation remote sensing, and the polarization information in vegetation remote sensing has become an effective way to characterize the physical characteristics of vegetation. This study analyzes the difference between these three types of radiation flux and utilizes polarization radiation to separate them from the apparent radiation of the vegetation canopy. Specifically, solar-induced chlorophyll fluorescence is extracted from vegetation canopy radiation data using standard Fraunhofer-line discrimination. The results show that polarization measurements can quantitatively separate Lvol, Lpol, and ChlF and extract the solar-induced chlorophyll fluorescence. This study improves our understanding of the light-scattering properties of vegetation canopies and provides insights for developing building models and research algorithms.

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

NONHYDROSTATIC MODELING OF FLOW INTERACTIONS WITH HIGHLY FLEXIBLE VEGETATION

Improving our understanding of the interactions between gravity waves, currents, and coastal vegetation, which are nonlinear in nature, enables coastal engineers and managers to better estimate hydrodynamic forces on coastal infrastructure and utilize natural elements to mitigate their impacts. Aquatic vegetation is ubiquitous in coastal waters and it is well-known that flow loses energy over vegetation. Computational modeling of wave-vegetation interaction has been the subject of numerous recent studies and many improvements have been achieved in reducing limitations applied on wave and vegetation behavior in these models. Mechanisms for highly flexible vegetation have been incorporated in a Boussinesq-type model and Reynolds-Averaged Navier-Stokes (RANS) models. Flow dynamics over flexible vegetation and vegetation dynamics in response to hydrodynamic forcing are important for predicting wave and surge dissipation by vegetation, storm impacts on vegetation canopies, ecological processes, and sediment transport in estuaries, and require further investigation. In this study, we implement a numerical model for highly flexible vegetation in an open-source RANS model NHWAVE to address some of these questions.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oAwb8nu4RSs