Vegetation Optical Depth Products Research Articles

Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China

ABSTRACT The microwave-derived vegetation optical depth (VOD) products were used to monitor aboveground biomass (AGB) at regional to global scales, but the ability of VOD to monitor AGB in China is uncertain. This study evaluated the sensitivity of four VOD products (e.g. L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) and optical vegetation indices (VI) (e.g. NDVI, EVI, LAI, and tree cover from MODIS) to the AGB across China. Our results showed tree cover product has the highest spatial agreement with reference AGBs (indicated by the median correlation value of 0.85), followed by L-VOD (with a median correlation value of 0.80), which performs better than other VIs and VODs. Further comparisons between reference and estimated AGB computed using the fitted logistic regression showed that AGB estimations from tree cover and L-VOD outperformed the estimations from other VIs and VODs over most vegetation types (except forest), indicated by the higher median correlation value of 0.86 and 0.83 and lower RMSD of 23.9 and 27.3 Mg/ha, respectively. The good performance of tree cover could be partly due to that tree cover product is not independent from the reference AGBs. The good performance of L-VOD can be explained by its higher sensitivity to the vegetation characteristics of the entire canopy (including woody component), relative to other VODs and VIs. Among the six reference AGB products, Saatchi-WT and Saatchi-RF products were found to have the best correlations with VIs and VODs. This study demonstrates that microwave VODs, particularly L-VOD, are effective proxies for large-scale monitoring of vegetation AGB in China.

First mapping of polarization-dependent vegetation optical depth and soil moisture from SMAP L-band radiometry

Soil moisture (SM) and vegetation optical depth (VOD) estimates using passive microwave remote sensing at L-band (1.4 GHz) are essential for attaining a better understanding of water exchanges at the land-atmosphere interface. However, current retrieval algorithms often ignore the polarization dependence of vegetation effects. This study proposed a parameter self-calibrating framework for the multi-channel collaborative algorithm (MCCA) and presented a new SM and the first polarization-dependent VOD product based on the dual-polarized L-band observations at a fixed angle (40°) from the NASA Soil Moisture Active Passive (SMAP) mission. The parameter self-calibrating framework utilizes an information theory-based approach to obtain surface roughness and effective scattering albedo globally. Furthermore, the MCCA does not require auxiliary data for vegetation or soil moisture to constrain the retrieval process. Comparison with other SM and VOD products, such as MT-DCA version 5, DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB, demonstrate analogous spatial patterns. The MCCA-derived SM exhibits the lowest unbiased root mean square deviation (ubRMSD, about 0.055 m3/m3), followed by SMAP-IB and DCA (0.061 m3/m3), with an overall Pearson's correlation coefficient of 0.744 (SMAP-IB performed best with R = 0.764) when evaluated against in-situ observations from 18 dense soil moisture networks. The MCCA generates VOD values for both vertical and horizontal polarization, demonstrating a slight polarization difference of vegetation effect at the satellite scale. Both VODs exhibit a strong linear relationship with above-ground biomass and canopy height. The polarization difference of VODs is primarily observed in densely vegetated and arid areas.

A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison

Active microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator related to vegetation water content and biomass. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from this active sensor. This study presents a new publicly released global long-term and continuous (2007–2020) C-band VOD dataset retrieved from the ASCAT observations, named the ASCAT INRAE-BORDEAUX or ASCAT IB VOD product. The retrieval algorithm is based on the Water Cloud Model (WCM) including the Ulaby bare soil model. The algorithm takes advantage of a multi-temporal (MT) retrieval method relying on a cost function where constraints to the retrieved parameters are implemented and a reanalysis soil moisture (SM) dataset from ERA5-Land is used as an input. The performance of ASCAT IB VOD was evaluated by inter-comparing it with ASCAT Technische Universität Wien (TUW), the Advanced Microwave Scanning Radiometer 2 (AMSR2), and VOD Climate Archive (VODCA) VOD products (the last two products are estimated from passive microwave observations). Results showed that ASCAT IB VOD presented the highest spatial correlation with aboveground biomass (R ∼ 0.83) and with the Global Ecosystem Dynamics Investigation (GEDI) canopy height (R ∼ 0.84–0.85). In terms of temporal performance, ASCAT IB VOD had the highest correlation R values with leaf area index (LAI) and Normalized Difference Water Index (NDWI) in most parts of the globe from 2013 to 2018. This contrasts with AMSR2 VODs which correlated better with Normalized Difference Vegetation Index (NDVI). The new ASCAT-based VOD product on a global scale highlighted the potential benefit of combining active (namely ASCAT) and passive (namely AMSR2) VOD products for vegetation studies.

A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm

Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions.

Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties

Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic.

Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests

Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applications, e.g., carbon stock, phenology and vegetation monitoring. However, VOD retrieval over dense forests is subject to uncertainties caused by the thick canopy and complex multiple scattering effects. Thus, a comprehensive evaluation of VOD products over dense forests is needed for effective and accurate applications. This study evaluated the seasonal variations of eight recently developed/reprocessed VOD products at different frequencies (e.g., Ku-, X-, C- and L-band) over Amazon intact forests, supported by the ORCHIDEE-CAN-NHA model-simulated vegetation water content. Furthermore, we also explored the potential causes of VOD retrieval issues, in terms of retrieval algorithm uncertainties.We first confirmed that soil water availability dominated seasonal dynamics of vegetation water content over Amazon intact forests. This was verified by model-simulated vegetation water content and by C-band radar backscatter observations. Generally, evening or midday vegetation water content shows higher correlations with soil moisture than morning or midnight vegetation water content. In terms of ability of morning or midnight VOD products to follow the seasonality of soil moisture, active microwave ASCAT-IB C-VOD (median seasonal correlation with soil moisture (R) ∼ 0.50) outperforms the passive microwave VOD products, followed by passive microwave AMSR2 X-VOD (R ∼ 0.26) and VODCA X-VOD (R ∼ 0.16). However, SMOS-IC L-VOD (R ∼ −0.15) and AMSR2 C1-VOD (R ∼ −0.20) show obviously negative seasonal correlations with soil moisture across most pixels. This implausible behavior is likely to be caused by the inappropriate setting of time-invariant scattering effects in the passive microwave VOD retrieval algorithms, which could lead to an overestimation of the VOD amplitude during dry seasons. Thus, we recommend that the seasonal scattering effects be considered in the passive microwave VOD retrieval algorithms. These findings can contribute to the improvement of VOD retrieval algorithms and help with the development of their ecological applications over Amazon dense forests.

Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.

A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison

Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD), which are essential to monitor the Earth water and carbon cycles. Currently, only two space-borne L-band radiometer missions are operating: the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions in orbit since 2009 and 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter SMAP-IB) of SM and L-band VOD (L-VOD) from the dual-channel SMAP radiometric observations. The retrievals are based on the L-MEB (L-band Microwave Emission of the Biosphere) model which is the forward model of SMOS-IC and of the official SMOS retrieval algorithms. The SMAP-IB product aims at providing good performances for both SM and L-VOD while remaining independent of auxiliary data: neither modelled SM data nor optical vegetation indices are used as input in the algorithm. Inter-comparison with other SM and L-VOD products (i.e., MT-DCA, SMOS-IC, and the new versions of DCA and SCA-V extracted from SMAP passive Level 3 product) suggested that SMAP-IB performed well for both SM and L-VOD. In particular, SMAP-IB SM retrievals presented the higher scores (R = 0.74) in capturing the temporal trends of in-situ observations from ISMN (International Soil Moisture Network) during April 2015–March 2019, followed by MT-DCA (R = 0.71). While the lowest ubRMSD value was obtained by the new version of SMAP DCA (0.056 m3/m3), SMAP-IB SM retrievals presented best scores for R, ubRMSD (~ 0.058 m3/m3) and bias (0.002 m3/m3) when considering only products independent of optical vegetation indices (e.g., NDVI). L-VOD retrievals from SMAP-IB, MT-DCA, and SMOS-IC were well correlated (spatially) with aboveground biomass and tree height, with spatial R values of ~0.88 and ~ 0.90, respectively. All three L-VOD products exhibited a smooth non-linear density distribution with biomass and a good linear relationship with tree height, especially at high biomass levels, while the L-VOD datasets incorporating optical information in the algorithms (i.e., SCA-V and DCA) showed obvious saturation effects. It is expected that this new algorithm can facilitate the fusion of both SM and L-VOD retrievals from SMOS and SMAP to obtain long-term and continuous L-band earth observation products.

Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth From SMAP Measurements

In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> polarized brightness temperature. Due to a lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.

ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite

Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.

An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales

Vegetation optical depth (VOD) retrieved from microwave observations has been found to be useful to monitor the dynamics of the vegetation features at global scale. Particularly, many applications could be developed in several fields of research (ecology, water and carbon cycle, etc.) from VOD products retrieved from the SMOS and SMAP observations at L-band, and from the combined AMSR-E (2002−2011)/AMSR2 (2012-present) observations at C- and X-bands. One of the main difficulties in retrieving VOD is that the microwave observations are sensitive to both the soil (mainly soil moisture) and vegetation (mostly VOD) features. The AMSR-E/2 sensors provide only mono-angular observations at two polarizations. These dual-channel observations may be strongly correlated so that retrieving SM and VOD simultaneously can be an ill-posed problem. Here, to overcome this problem, we proposed and evaluated a new retrieval approach from AMSR2 observations at X-band to produce a new X-VOD product. The X-VOD retrievals were based on the inversion of the X-MEB model, an extension of the L-MEB model (L-band microwave emission of the biosphere) to the X-band. The main originality in comparison to previous algorithms is that (i) only VOD was retrieved while SM was estimated from a reanalysis data set (ERA5-Land); (ii) model inversion was based on an innovative approach to initialize the cost function; and (iii) new values for the soil and vegetation X-MEB model parameters were calibrated. To evaluate the methodology, we performed the VOD retrievals over the whole African continent over 2014–2016, including a dry (2015) and a wet (2014) year. In a first step, we calibrated a set of three parameters: effective scattering albedo (ω), soil roughness (HR) and VOD first guess (VODini). Several datasets of vegetation indices as Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) were chosen as reference data to optimize these model parameters. Globally-constant values (ω = 0.06 and HR = 0.6) were found to achieve high spatial and temporal correlations between retrieved X-VOD and the reference vegetation parameters. Comparison with other X-VOD products suggested IB X-VOD had competitive advantages in terms of both spatial and temporal performances. In particular, spatial correlation with three biomass datasets was found to be higher than for previous X-VOD products (R2 ~ 0.76–0.83) and temporal correlation with LAI or NDVI showed obvious improvements, especially in dense vegetation.

Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products

The vegetation optical depth (VOD), a vegetation index retrieved from passive or active microwave remote sensing systems, is related to the intensity of microwave extinction effects within the vegetation canopy layer. This index is only marginally impacted by effects from atmosphere, clouds and sun illumination, and thus increasingly used for ecological applications at large scales. Newly released VOD products show different abilities in monitoring vegetation features, depending on the algorithm used and the satellite frequency. VOD is increasingly sensitive to the upper vegetation layer as the frequency increases (from L-, C- to X-band), offering different capacities to monitor seasonal changes of the leafy and/or woody vegetation components, vegetation water status and aboveground biomass. This study evaluated nine recently developed/reprocessed VOD products from the AMSR2, SMOS and SMAP space-borne instruments for monitoring structural vegetation features related to phenology, height and aboveground biomass.For monitoring the seasonality of green vegetation (herbaceous and woody foliage), we found that X-VOD products, particularly from the LPDR-retrieval algorithm, outperformed the other VOD products in regions that are not densely vegetated, where they showed higher temporal correlation values with optical vegetation indices (VIs). However, LPDR X-VOD time series failed to detect changes in VOD after rainfall events whereas most other VOD products could do so, and overall daily variations are less pronounced in LPDR X-VOD. Results show that the reprocessed VODCA C- and X-VOD have almost comparable performance and VODCA C-VOD correlates better with VIs than other C-VOD products. Low frequency L-VOD, particularly the new version (V2) of SMOS-IC, show a higher temporal correlation with VIs, similar to C-VOD, in medium-densely vegetated biomes such as savannas (R ~ 0.70) than for other short vegetation types. Because the L-VOD indices are more sensitive to the non-green vegetation components (trunks and branches) than higher frequency products, they are well-correlated with aboveground biomass: (R ~ 0.91) across space between predicted and observed values for both SMOS-IC V2 and SMAP MT-DCA. However, when compared with forest canopy height, results at L-band are not systematically better than C- and X-VOD products. This revealed specific VOD retrieval issues for some ecosystems, e.g., boreal regions. It is expected that these findings can contribute to algorithm refinements, product enhancements and further developing the use of VOD for monitoring above-ground vegetation biomass, vegetation dynamics and phenology.

Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)

Abstract. Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.

Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events?

Plant water use strategy is one of the key factors to predict drought impact on vegetation and land-atmosphere fluxes. Vegetation optical depth (VOD) based on microwave radiative transfer inversion has recently been used to assess plant water use strategy. However, VOD is sensitive to both total aboveground biomass (AGB) and leaf water content, with only the latter being a proxy of leaf water potential whose diurnal variation can be used to characterize vegetation iso/anisohydricity. In this study, by using a network of soil water measurements (used as a proxy for predawn leaf water potential), satellite retrieved normalized difference vegetation index (NDVI, as a proxy for AGB), and two satellite VOD products from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor, we compare three linear models and one machine learning model to investigate to what extent can VOD be used to represent leaf water potential changes during soil moisture dry-down periods. Linear models with both NDVI and leaf water potential, on average, can explain 33% and 51% of VOD variations of each product respectively. Models using only NDVI explain 27% and 46% of the VOD variance, compared to less than 10% by models using leaf water potential only. With the NDVI and leaf water potential (full) model, leaf water potential contributes around 17% of the VOD variance, which is smaller than NDVI (33%). The machine learning model has overall better performance than the linear models, and also highlight the dominant contribution of AGB to VOD signals. After the AGB contribution to VOD is eliminated by normalizing daytime VOD with nighttime VOD, the residuals carry the information of diurnal variations of leaf water potential and calculations from both VOD datasets are consistent with each other (r = 0.42±0.17, P < 0.01 for 88 out of 94 sites). The response of VODdaytimeVODnighttime to soil water potential can also be used as a new metric for ecosystem iso/anisohydricity. Our study demonstrates that a large proportion of variations in VOD are caused by AGB for temperate ecosystems, and higher accuracy VOD products with additional root-zone soil water potential are needed for ecosystem iso/anisohydricity estimations.

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30 GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4 GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the impact of the VOD parameter on SM retrievals is more considerable than the effects of HR and ω. Overall, the inclusion of the VOD parameter in the SMAP SM retrieval algorithm was found to be a very interesting approach and showed the large potential benefit of the synergy between SMAP and SMOS.

Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010)

Abstract. Consistent forest loss estimates are important to understand the role of forest loss and deforestation in the global carbon cycle, for biodiversity studies, and to estimate the mitigation potential of reducing deforestation. To date, most studies have relied on optical satellite data and new efforts have greatly improved our quantitative knowledge on forest dynamics. However, most of these studies yield results for only a relatively short time period or are limited to certain countries. We have quantified large-scale forest loss over a 21-year period (1990–2010) in the tropical biomes of South America using remotely sensed vegetation optical depth (VOD). This passive microwave satellite-based indicator of vegetation water content and vegetation density has a much coarser spatial resolution than optical data but its temporal resolution is higher and VOD is not impacted by aerosols and cloud cover. We used the merged VOD product of the Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave Imager (SSM/I) observations, and developed a change detection algorithm to quantify spatial and temporal variations in forest loss dynamics. Our results compared reasonably well with the newly developed Landsat-based Global Forest Change (GFC) maps, available for the 2001 onwards period (r2 = 0.90 when comparing annual country-level estimates). This allowed us to convert our identified changes in VOD to forest loss area and compute these from 1990 onwards. We also compared these calibrated results to PRODES (r2 = 0.60 when comparing annual state-level estimates). We found that South American forest exhibited substantial interannual variability without a clear trend during the 1990s, but increased from 2000 until 2004. After 2004, forest loss decreased again, except for two smaller peaks in 2007 and 2010. For a large part, these trends were driven by changes in Brazil, which was responsible for 56 % of the total South American forest loss area over our study period according to our results. One of the key findings of our study is that while forest loss decreased in Brazil after 2005, increases in other countries partly offset this trend suggesting that South American forest loss as a whole decreased much less than that in Brazil.

Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices

Abstract The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.

Global long-term passive microwave satellite-based retrievals of vegetation optical depth

[1] Vegetation optical depth (VOD) retrievals from three satellite-based passive microwave instruments were merged to produce the first long-term global microwave-based vegetation product. The resulting VOD product spans more than two decades and shows seasonal cycles and inter-annual variations that generally correspond with those observed in the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Some notable differences exist in the long-term trends: the NDVI, operating in the optical regime, is sensitive to chlorophyll abundance and photosynthetically active biomass of the leaves, whereas the microwave-based VOD is an indicator of the vegetation water content in total above-ground biomass, i.e., including wood and leaf components. Preliminary analyses indicate that the fluctuations in VOD typically correlated to precipitation variations, and that the mutually independent VOD and NDVI do not necessarily respond in identical manners. Considering both products together provides a more robust structural characterization and assessment of long-term vegetation dynamics at the global scale.