VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

Download Full-text

Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland

Remote Sensing ◽

10.3390/rs12203391 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3391

Author(s):

Jiabin Pu ◽

Kai Yan ◽

Guohuan Zhou ◽

Yongqiao Lei ◽

Yingxin Zhu ◽

...

Keyword(s):

Model Uncertainty ◽

Large Scale ◽

Near Infrared ◽

Evaluation Method ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Scale Dependency ◽

Area Index ◽

Input Uncertainty

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.

Download Full-text

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Remote Sensing ◽

10.3390/rs11212547 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2547 ◽

Cited By ~ 1

Author(s):

Siheng Wang ◽

Dong Yang ◽

Zhen Li ◽

Liangyun Liu ◽

Changping Huang ◽

...

Keyword(s):

Sensitivity Analysis ◽

Random Forest ◽

Global Sensitivity Analysis ◽

Vegetation Indices ◽

Broad Band ◽

Influential Factor ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Global Sensitivity

Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.

Download Full-text

First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices

Remote Sensing ◽

10.3390/rs12172724 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2724

Author(s):

Víctor Cicuéndez ◽

Manuel Rodríguez-Rastrero ◽

Laura Recuero ◽

Margarita Huesca ◽

Thomas Schmid ◽

...

Keyword(s):

Soil Respiration ◽

Carbon Cycle ◽

Near Infrared ◽

Spatial Scales ◽

Coefficient Of Determination ◽

Growth Stages ◽

Spectral Indices ◽

Crop Phenology ◽

Area Index ◽

Temporal And Spatial

Rainfed agriculture occupies the majority of the world’s agricultural surface and is expected to increase in the near future causing serious effects on carbon cycle dynamics in the context of climate change. Carbon cycle across several temporal and spatial scales could be studied through spectral indices because they are related to vegetation structure and functioning and hence with carbon fluxes, among them soil respiration (Rs). The aim of this work was to assess Rs linked to crop phenology of a rainfed barley crop throughout two seasons based on spectral indices calculated from field spectroscopy data. The relationships between Rs, Leaf Area Index (LAI) and spectral indices were assessed by linear regression models with the adjusted coefficient of determination (Radj2). Results showed that most of the spectral indices provided better information than LAI throughout the studied period and that soil moisture and temperature were relevant variables in specific periods. During vegetative stages, indices based on the visible (VIS) region showed the best relationship with Rs. On the other hand, during reproductive stages indices containing the near infrared-shortwave infrared (NIR-SWIR) spectral region and those related to water content showed the highest relationship. The inter-annual variability found in Mediterranean regions was also observed in the estimated ratio of carbon emission to carbon fixation between years. Our results show the potential capability of spectral information to assess soil respiration linked to crop phenology across several temporal and spatial scales. These results can be used as a basis for the utilization of other remote information derived from satellites or airborne sensors to monitor crop carbon balances.

Download Full-text

Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

Remote Sensing ◽

10.3390/rs13163175 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3175

Author(s):

Naichen Xing ◽

Wenjiang Huang ◽

Huichun Ye ◽

Yu Ren ◽

Qiaoyun Xie

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Transfer Model ◽

Growth Stages ◽

Growth Monitoring ◽

Area Index

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively.

Download Full-text

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Remote Sensing ◽

10.3390/rs13020233 ◽

2021 ◽

Vol 13 (2) ◽

pp. 233

Author(s):

Ilja Vuorinne ◽

Janne Heiskanen ◽

Petri K. E. Pellikka

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Fibre Production ◽

Additive Models ◽

Leaf Biomass ◽

Ground Vegetation ◽

Perennial Crop ◽

Red Edge ◽

Agave Sisalana ◽

Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

Download Full-text

Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽

10.3390/land10050505 ◽

2021 ◽

Vol 10 (5) ◽

pp. 505

Author(s):

Gregoriy Kaplan ◽

Offer Rozenstein

Keyword(s):

Water Vapor ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Near Infrared ◽

Linear Regression Models ◽

Area Index ◽

Estimation Performance ◽

Red Edge ◽

Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

Download Full-text

Mineralogical and chemical properties inversed from 21-lunar-day VNIS observations taken during the Chang’E-4 mission

Scientific Reports ◽

10.1038/s41598-021-93694-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Qinghong Zeng ◽

Shengbo Chen ◽

Yuanzhi Zhang ◽

Yongling Mu ◽

Rui Dai ◽

...

Keyword(s):

Impact Crater ◽

Near Infrared ◽

Infrared Imaging ◽

Chemical Properties ◽

Landing Site ◽

Radiative Transfer Model ◽

Transfer Model ◽

Chemical Compositions ◽

Impact Melt ◽

And Chemical Properties

AbstractWe report on the mineralogical and chemical properties of materials investigated by the lunar rover Yutu-2, which landed on the Von Kármán crater in the pre-Nectarian South Pole–Aitken (SPA) basin. Yutu-2 carried several scientific payloads, including the Visible and Near-infrared Imaging Spectrometer (VNIS), which is used for mineral identification, offering insights into lunar evolution. We used 86 valid VNIS data for 21 lunar days, with mineral abundance obtained using the Hapke radiative transfer model and sparse unmixing algorithm and chemical compositions empirically estimated. The mineralogical properties of the materials at the Chang’E-4 (CE-4) site referred to as norite/gabbro, based on findings of mineral abundance, indicate that they may be SPA impact melt components excavated by a surrounding impact crater. We find that CE-4 materials are dominated by plagioclase and pyroxene and feature little olivine, with 50 of 86 observations showing higher LCP than HCP in pyroxene. In view of the effects of space weathering, olivine content may be underestimated, with FeO and TiO2 content estimated using the maturity-corrected method. Estimates of chemical content are 7.42–18.82 wt% FeO and 1.48–2.1 wt% TiO2, with a low-medium Mg number (Mg # ~ 55). Olivine-rich materials are not present at the CE-4 landing site, based on the low-medium Mg #. Multi-origin materials at the CE-4 landing site were analyzed with regard to concentrations of FeO and TiO2 content, supporting our conclusion that the materials at CE-4 do not have a single source but rather are likely a mixture of SPA impact melt components excavated by surrounding impact crater and volcanic product ejecta.

Download Full-text

Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model

Remote Sensing of Environment ◽

10.1016/s0034-4257(03)00005-1 ◽

2003 ◽

Vol 85 (3) ◽

pp. 257-270 ◽

Cited By ~ 144

Author(s):

H Fang

Keyword(s):

Genetic Algorithm ◽

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index

Download Full-text

Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data

10.5194/egusphere-egu21-510 ◽

2021 ◽

Author(s):

Richard Fernandes ◽

Fred Baret ◽

Luke Brown ◽

Francis Canisius ◽

Jadu Dash ◽

...

Keyword(s):

North America ◽

Google Earth ◽

Model Complexity ◽

Radiative Transfer Model ◽

Transfer Model ◽

Wide Field ◽

Area Index ◽

Product Generation ◽

Level 2 ◽

Sentinel 2

The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.&#160; The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.&#160; Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.&#160; Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?&#160;&#160; To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).&#160; This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.&#160; SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.&#160; Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.&#160; Results for other sites will also be presented.&#160; To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.&#160; The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.

Download Full-text