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Vegetation Optical Depth: Remote Sensing Retrievals and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 20815

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Special Issue Editors

Dr. Arnaud Mialon

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Guest Editor

CESBIO, CNES/CNRS/IRD/UPS, UMR 5126, 31401 Toulouse, CEDEX 9, France
Interests: surface soil moisture; SMOS; L-VOD; passive microwave
Special Issues, Collections and Topics in MDPI journals

Dr. Nemesio Rodriguez-Fernandez

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Guest Editor

CESBIO, CNES/CNRS/IRD/UPS, UMR 5126, 31401 Toulouse, CEDEX 9, France
Interests: microwave remote sensing; soil moisture; vegetation optical depth; biomass; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Mariette Vreugdenhil

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Guest Editor

Department of Geodesy and Geoinformation, TU Wien, 1020 Vienna, Austria
Interests: active microwave remote sensing; vegetation dynamics; soil moisture

Dr. Tianjie Zhao

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: soil moisture; passive microwave; hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Roberto Fernández Morán

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Guest Editor

Image Processing Lab, University of Valencia, 46100 Valencia, Spain
Interests: L-band microwaves; machine learning; SMOS; SMAP; Sentinel-3

Special Issue Information

Vegetation optical depth (VOD) accounts for the attenuation of microwaves through vegetation and is a function of vegetation water content and its structure. VOD estimated at different frequencies provides complementary information on these vegetation properties. It has been shown that VOD can be used as a proxy of other vegetation properties, such as tree height, sap flow, leaf fall and above ground biomass.

Unlike traditional optically-based technologies, microwave-frequency sensors can observe through clouds. Furthermore, one of the strengths of particularly low frequency VOD is its sensitivity to vegetation changes in dense forests, where optical indices tend to saturate. The relationship between VOD and AGC (above ground carbon) was recently discovered. The estimation of AGC allows scientists to assess interannual variations in carbon stocks, a main actor in climate change mitigation, on Earth.

The availability of data from ASCAT, Sentinel-1, SMOS, SMAP, AMSR-E, and AMSR2 missions allow the study of vegetation seasonality and, subsequently, information about the plant status, from leaves to stems. Multi-frequency retrievals could reveal the partitioning of the extinction, in microwave radiation caused by vegetation, into scattering and absorption.

In this special issue on VOD, we invite contributions on the following topics:

VOD retrieval algorithms, based on both physical models and data-driven methods.
Various complementary VODs (L-, C-, X-band) and information derived from other sensors (optical).
Downscaling VOD merging data from different sensors with different spatial resolutions.
Approaches for the harmonized processing of data coming from different sensors to construct longer, coherent, VOD records.
Field measurements of VOD, relationships of vegetation water content and validation of satellite VOD products.
Applications of remotely sensed VOD data including data assimilation and disaster assessment (fires, droughts, and C-stocks monitoring).
Theoretical modeling of vegetation for quantifying optical depth and transmissivity.

Dr. Arnaud Mialon
Dr. Nemesio Rodriguez-Fernandez
Dr. Mariette Vreugdenhil
Dr. Tianjie Zhao
Dr. Roberto Fernández Morán
Guest Editors

Manuscript Submission Information

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Keywords

X-VOD
C-VOD
L-VOD
SMOS
SMAP
AMSR
ASCAT
CIMR
Active Microwave
Passive Microwave
Above Ground Biomass(AGB)

Published Papers (4 papers)

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Research

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20 pages, 3563 KiB

Open AccessArticle

Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations

by Huixian Zhang, Daniel Fiifi Tawia Hagan, Ricardo Dalagnol and Yi Liu

Remote Sens. 2021, 13(12), 2238; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122238 - 08 Jun 2021

Cited by 7 | Viewed by 4478

Abstract

Canopy dynamics associated with fires in tropical forests play a critical role in the terrestrial carbon cycle and climate feedbacks. The aim of this study was to characterize forest canopy dynamics in the southern Amazon during the 2019 fire season (July–October) using passive microwave-based vegetation optical depth (VOD) and three optical-based indices. First, we found that precipitation during July–October 2019 was close to the climatic means, suggesting that there were no extreme hydrometeorological events in 2019 and that fire was the dominant factor causing forest canopy anomalies. Second, based on the active fire product (MCD14ML), the total number of active fires over each grid cell was calculated for each month. The number of active fires during the fire season in 2019 was above average, particularly in August and September. Third, we compared the anomalies of VOD and optical-based indices (the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burn ratio (NBR)) against the spatiotemporal distribution of fires during July–October 2019. Spatially, the location with a concentrated distribution of significant negative VOD anomalies was matched with the grid cells with fire activities, whereas the concentrated distribution of strong negative anomalies in optical-based indices were found in both burned and unburned grid cells. When we focused on the temporal pattern over the grid cells with fire activity, the VOD and the optical-based indices behaved similarly from July to October 2019, i.e., the magnitude of negative anomalies became stronger with increased fire occurrences and reached the peak of negative anomalies in September before decreasing in October. A discrepancy was observed in the magnitude of negative anomalies of the optical-based indices and the VOD; the magnitude of optical-based indices was larger than the VOD in August–September and recovered much faster than the VOD over the grid cells with relatively low fire activity in October. The most likely reason for their different responses is that the VOD represents the dynamics of both photosynthetic (leaf) and nonphotosynthetic (branches) biomass, whereas optical-based indices are only sensitive to photosynthetic (leaf) active biomass, which recovers faster. Our results demonstrate that VOD can detect the spatiotemporal of canopy dynamics caused by fire and postfire canopy biomass recovery over high-biomass rainforest, which enables more comprehensive assessments, together with classic optical remote sensing approaches. Full article

(This article belongs to the Special Issue Vegetation Optical Depth: Remote Sensing Retrievals and Applications)

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20 pages, 5141 KiB

Open AccessArticle

The Value of L-Band Soil Moisture and Vegetation Optical Depth Estimates in the Prediction of Vegetation Phenology

by Bonan Li, Stephen P. Good and Dawn R. URycki

Remote Sens. 2021, 13(7), 1343; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071343 - 01 Apr 2021

Cited by 2 | Viewed by 2347

Abstract

Vegetation phenology is a key ecosystem characteristic that is sensitive to environmental conditions. Here, we examined the utility of soil moisture (SM) and vegetation optical depth (VOD) observations from NASA’s L-band Soil Moisture Active Passive (SMAP) mission for the prediction of leaf area index (LAI), a common metric of canopy phenology. We leveraged mutual information theory to determine whether SM and VOD contain information about the temporal dynamics of LAI that is not contained in traditional LAI predictors (i.e., precipitation, temperature, and radiation) and known LAI climatology. We found that adding SMAP SM and VOD to multivariate non-linear empirical models to predict daily LAI anomalies improved model fit and reduced error by 5.2% compared with models including only traditional LAI predictors and LAI climatology (average R² = 0.22 vs. 0.15 and unbiased root mean square error [ubRMSE] = 0.130 vs. 0.137 for cross-validated models with and without SM and VOD, respectively). SMAP SM and VOD made the more improvement in model fit in grasslands (R² = 0.24 vs. 0.16 and ubRMSE = 0.118 vs. 0.126 [5.7% reduction] for models with and without SM and VOD, respectively); model predictions were least improved in shrublands. Analysis of feature importance indicates that LAI climatology and temperature were overall the two most informative variables for LAI anomaly prediction. SM was more important in drier regions, whereas VOD was consistently the second least important factor. Variations in total LAI were mostly explained by local daily LAI climatology. On average, the R²s and ubRMSE of total LAI predictions by the traditional drivers and its climatology are 0.81 and 0.137, respectively. Adding SMAP SM and VOD to these existing predictors improved the R²s to 0.83 (0.02 improvement in R²s) and reduced the ubRMSE to 0.13 (5.2% reduction). Though these improvements were modest on average, in locations where LAI climatology is not reflective of LAI dynamics and anomalies are larger, we find SM and VOD to be considerably more useful for LAI prediction. Overall, we find that L-band SM and VOD observations can be useful for prediction of LAI, though the informational contribution varies with land cover and environmental conditions. Full article

(This article belongs to the Special Issue Vegetation Optical Depth: Remote Sensing Retrievals and Applications)

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19 pages, 6114 KiB

Open AccessArticle

Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

by Mariette Vreugdenhil, Claudio Navacchi, Bernhard Bauer-Marschallinger, Sebastian Hahn, Susan Steele-Dunne, Isabella Pfeil, Wouter Dorigo and Wolfgang Wagner

Remote Sens. 2020, 12(20), 3404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203404 - 16 Oct 2020

Cited by 36 | Viewed by 6426

Abstract

Vegetation products based on microwave remote sensing observations, such as Vegetation Optical Depth (VOD), are increasingly used in a variety of applications. One disadvantage is the often coarse spatial resolution of tens of kilometers of products retrieved from microwave observations from spaceborne radiometers and scatterometers. This can potentially be overcome by using new high-resolution Synthetic Aperture Radar (SAR) observations from Sentinel-1. However, the sensitivity of Sentinel-1 backscatter to vegetation dynamics, or its use in radiative transfer models, such as the water cloud model, has only been tested at field to regional scale. In this study, we compared the cross-polarization ratio (CR) to vegetation dynamics as observed in microwave-based Vegetation Optical Depth from coarse-scale satellites over Europe. CR was obtained from Sentinel-1 VH and VV backscatter observations at 500 m sampling and resampled to the spatial resolution of VOD from the Advanced SCATterometer (ASCAT) on-board the Metop satellite series. Spatial patterns between median CR and ASCAT VOD correspond to each other and to vegetation patterns over Europe. Analysis of temporal correlation between CR and ASCAT VOD shows that high Pearson correlation coefficients (

R_{p}

) are found over croplands and grasslands (median

R_{p}

> 0.75). Over deciduous broadleaf forests, negative correlations are found. This is attributed to the effect of structural changes in the vegetation canopy which affect CR and ASCAT VOD in different ways. Additional analysis comparing CR to passive microwave-based VOD shows similar effects in deciduous broadleaf forests and high correlations over crop- and grasslands. Though the relationship between CR and VOD over deciduous forests is unclear, results suggest that CR is useful for monitoring vegetation dynamics over crop- and grassland and a potential path to high-resolution VOD. Full article

(This article belongs to the Special Issue Vegetation Optical Depth: Remote Sensing Retrievals and Applications)

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10 pages, 8325 KiB

Open AccessLetter

Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale

by Arnaud Mialon, Nemesio J. Rodríguez-Fernández, Maurizio Santoro, Sassan Saatchi, Stéphane Mermoz, Emma Bousquet and Yann H. Kerr

Remote Sens. 2020, 12(9), 1450; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091450 - 04 May 2020

Cited by 28 | Viewed by 3979

Abstract

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data. Full article

(This article belongs to the Special Issue Vegetation Optical Depth: Remote Sensing Retrievals and Applications)

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