scholarly journals Mapping carbon stocks in Central and South America with SMAP vegetation optical depth

2021 ◽  
Author(s):  
David Chaparro ◽  
Grégory Duveiller ◽  
María Piles ◽  
Mercè Vall-llossera ◽  
Alessandro Cescatti ◽  
...  
Keyword(s):  
South America ◽  
Optical Depth ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Microwave Remote Sensing ◽  
Carbon Stocks ◽  
Airborne Lidar ◽  
Low Frequencies ◽  
L Band ◽  
Airborne Lidar Data

Mapping carbon stocks in the tropics is essential for climate change mitigation. Passive microwave remote sensing allows estimating carbon from deep canopy layers through the Vegetation Optical Depth (VOD) parameter. Although their spatial resolution is coarser than that of optical vegetation indices or airborne Lidar data, microwaves present a higher penetration capacity at low frequencies (Lband) and avoid cloud masking. This work compares the relationships of airborne carbon maps in Central and South America with both (i) SMAP L-band VOD at 9 km gridding and (ii) MODIS Enhanced Vegetation Index (EVI). Models to estimate carbon stocks are built from these two satellite derived variables. Results show that L-band VOD has a greater capacity to model carbon variability than EVI. The resulting VOD-derived carbon estimates are further presented at a detailed (9 km) spatial scale.

scholarly journals THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

2018 ◽  
Vol XLII-3 ◽  
pp. 1623-1627
Author(s):  
S. Talebi ◽  
J. Shi ◽  
T. Zhao
Keyword(s):  
Optical Depth ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
Scattering Effects ◽  
The Relationship

This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

scholarly journals L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

2020 ◽  
Vol 12 (15) ◽  
pp. 2352
Author(s):  
Adriano Camps ◽  
Alberto Alonso-Arroyo ◽  
Hyuk Park ◽  
Raul Onrubia ◽  
Daniel Pascual ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Elevation Angle ◽  
Depth Estimation ◽  
Beech Forest ◽  
Global Navigation Satellite Systems ◽  
Area Index ◽  
Vegetation Impact ◽  
L Band

At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents an analysis of the vegetation impact on GPS L1 C/A (coarse acquisition code) signals in terms of attenuation and depolarization. A dual polarized instrument with commercial off-the-shelf (COTS) GPS receivers as back-ends was installed for more than a year under a beech forest collecting carrier-to-noise (C/N0) data. These data were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and normalized difference vegetation index (NDVI)). The highest correlation observed is between C/N0 and NDVI data, obtaining R2 coefficients larger than 0.85 independently from the elevation angle, suggesting that for beech forest, NDVI is a good descriptor of signal attenuation at L-band, which is known to be related to the vegetation optical depth (VOD). Depolarization effects were also studied, and were found to be significant at elevation angles as large as ~50°. Data were also fit to a simple τ-ω model to estimate a single scattering albedo parameter (ω) to try to compensate for vegetation scattering effects in soil moisture retrieval algorithms using GNSS-R. It is found that, even including dependence on the elevation angle (ω(θe)), at elevation angles smaller than ~67°, the ω(θe) model is not related to the NDVI. This limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R. Finally, errors of the GPS-derived position were computed over time to assess vegetation impact on the accuracy of the positioning.

scholarly journals Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

2020 ◽  
Vol 12 (12) ◽  
pp. 2048
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Laura Duncanson ◽  
Carlos Alberto Silva ◽  
Michael Denbina ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Single Model ◽  
Tropical Regions ◽  
Full Waveform ◽  
Applicable Range ◽  
L Band ◽  
Airborne Lidar Data ◽  
Synergistic Relationships

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.

scholarly journals Using vegetation indices for soil-moisture retrievals from passive microwave radiometry

2001 ◽  
Vol 5 (4) ◽  
pp. 671-678 ◽  
Author(s):  
E.J. Burke ◽  
W.J. Shuttleworth ◽  
A.N. French
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Normalised Difference Vegetation Index

Abstract. Surface soil moisture and the nature of the overlying vegetation both influence microwave emission from land surfaces significantly. One widely discussed but underused method for allowing for the effect of vegetation on soil-moisture retrievals from microwave observations is to use remotely sensed vegetation indices. This paper explores the potential for using the Normalised Difference Vegetation Index (NDVI) in soil-moisture retrievals from L-band (1.4 GHz) aircraft data gathered during the Southern Great Plains '97 (SGP97) experiment. A simplified version of MICRO-SWEAT, a soil vegetation atmosphere transfer (SVAT) scheme coupled with a microwave emission model, was used as the retrieval algorithm. Estimates of the optical depth of the vegetation, the parameter that describes the effect of the vegetation on microwave emission, were obtained by calibrating this retrieval algorithm against measurements of soil moisture at 15 field sites. A significant relationship was found between the optical depth so obtained and the observed NDVI at these sites, although this relationship changed with the resolution of the microwave brightness temperature observations used. Soil-moisture estimates made with the retrieval algorithm using the empirical relationship between optical depth and NDVI applied at two additional sites not used in the calibration show good agreement with field measurements. Keywords: NDVI, soil moisture, passive microwave, SGP97

scholarly journals Synthetic Aperture Radar (L band) and Optical Vegetation Indices for Discriminating the Brazilian Savanna Physiognomies: A Comparative Analysis

2005 ◽  
Vol 9 (15) ◽  
pp. 1-15 ◽  
Author(s):  
Edson E. Sano ◽  
Laerte G. Ferreira ◽  
Alfredo R. Huete
Keyword(s):  
Comparative Analysis ◽  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Primary Response ◽  
Synthetic Aperture ◽  
L Band ◽  
Vegetation Structures ◽  
Aperture Radar

Abstract The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.

scholarly journals Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

2015 ◽  
Vol XL-7/W3 ◽  
pp. 261-267 ◽  
Author(s):  
H. Adhikari ◽  
J. Heiskanen ◽  
E. E Maeda ◽  
P. K. E. Pellikka
Keyword(s):  
Predictive Models ◽  
Vegetation Index ◽  
Incidence Angle ◽  
Vegetation Indices ◽  
Airborne Lidar ◽  
Tree Cover ◽  
Topographic Effects ◽  
Landsat Images ◽  
Topographic Correction ◽  
Tropical Mountains

Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (<i>cos i</i>) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.

scholarly journals Validation of ash optical depth and layer height retrieved from passive satellite sensors using EARLINET and airborne lidar data: the case of the Eyjafjallajökull eruption

2016 ◽  
Vol 16 (9) ◽  
pp. 5705-5720 ◽  
Author(s):  
Dimitris Balis ◽  
Maria-Elissavet Koukouli ◽  
Nikolaos Siomos ◽  
Spyridon Dimopoulos ◽  
Lucia Mona ◽  
...  
Keyword(s):  
Optical Depth ◽  
Volcanic Ash ◽  
Volcanic Eruptions ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Data Sets ◽  
Layer Height ◽  
Satellite Retrievals ◽  
Ash Plume ◽  
Airborne Lidar Data

Abstract. The vulnerability of the European airspace to volcanic eruptions was brought to the attention of the public and the scientific community by the 2010 eruptions of the Icelandic volcano Eyjafjallajökull. As a consequence of this event, ash concentration thresholds replaced the “zero tolerance to ash” rule, drastically changing the requirements on satellite ash retrievals. In response to that, the ESA funded several projects aiming at creating an optimal end-to-end system for volcanic ash plume monitoring and prediction. Two of them, namely the SACS-2 and SMASH projects, developed and improved dedicated satellite-derived ash plume and sulfur dioxide level assessments. The validation of volcanic ash levels and height extracted from the GOME-2 and IASI instruments on board the MetOp-A satellite is presented in this work. EARLINET lidar measurements are compared to different satellite retrievals for two eruptive episodes in April and May 2010. Comparisons were also made between satellite retrievals and aircraft lidar data obtained with the UK's BAe-146-301 Atmospheric Research Aircraft (managed by the Facility for Airborne Atmospheric Measurements, FAAM) over the United Kingdom and the surrounding regions. The validation results are promising for most satellite products and are within the estimated uncertainties of each of the comparative data sets, but more collocation scenes would be desirable to perform a comprehensive statistical analysis. The satellite estimates and the validation data sets are better correlated for high ash optical depth values, with correlation coefficients greater than 0.8. The IASI retrievals show a better agreement concerning the ash optical depth and ash layer height when compared with the ground-based and airborne lidar data.

scholarly journals Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

2020 ◽  
Vol 12 (4) ◽  
pp. 671 ◽  
Author(s):  
Matthew Dannenberg ◽  
Xian Wang ◽  
Dong Yan ◽  
William Smith
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Microwave Remote Sensing ◽  
Self Organizing Maps ◽  
Growth Cycles ◽  
Growing Seasons ◽  
Temperature And Precipitation ◽  
Optical Fluorescence

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.

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