Vegetation Optical Depth Retrievals Research Articles

Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model

This study explores the potential of integrating satellite retrievals of surface soil moisture (SSM) and vegetation conditions into the Noah-MP land surface model. In total, five data assimilation (DA) experiments were carried out. One of the experiments only assimilates SSM retrievals from the Soil Moisture Active Passive mission, two experiments only assimilate retrievals of vegetation conditions: either optical retrievals of leaf area index (LAI) from the Copernicus Global Land Service, or X-band microwave-based retrievals of vegetation optical depth (VOD) from the Advanced Microwave Scanning Radiometer 2. Additionally, two joint DA experiments are performed, each incorporating SSM and one of the vegetation products. The DA experiments are compared with a model-only run, and all experiments are evaluated using independent ground reference data of soil moisture, evapotranspiration, net ecosystem exchange and gross primary production (GPP). Assimilating only SSM improves estimates of the soil moisture profile (median SSM anomaly correlation improves with 0.02 compared to a model-only run), whereas assimilating LAI predominantly improves GPP estimates (reduction in median RMSD of 0.024 gC m−2 day−1 compared to a model-only run). The joint assimilation of SSM and vegetation conditions captures both of these improvements in a single, physically consistent analysis product. The DA increments show that this combined setup allows one satellite product to compensate for potential degradations introduced into the system by the other product. Furthermore, the joint SSM and VOD DA experiment has the smallest ensemble spread in its estimates (21% reduction in SSM spread compared to a model-only run). Overall, our results underline the potential of multi-sensor and multivariate DA, in which information from different sources is combined to improve the estimates of several land surface states and fluxes simultaneously.

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.

Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics

Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics.

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.

Error and uncertainty characterization of soil moisture and VOD retrievals obtained from L-band SMAP radiometer

L-band passive microwave remote sensing has evolved over the past decade to estimate soil moisture (SM) and Vegetation Optical Depth (VOD). Novel Radiative Transfer Model (RTM) schemes and model parameterizations are proposed to achieve this goal. In this work, we attempt to characterize errors and uncertainties that propagate from RTMs and their parameters while retrieving SM and VOD from Soil Moisture Active Passive (SMAP) data. Three RTMs are considered, including a zeroth order model (τ-ω model) and two first order models (First order RTM and 2-Stream Emission model (2S-EM)). Surface roughness (h; characterizes undulations on soil surface) and single scattering albedo (ω; accounts for the scattering of emissions due to vegetation structure) are the two parameters (of RTMs) considered. SM and VOD are retrieved concurrently using a multi-temporal RTM inversion scheme. Errors and uncertainty contributions are determined using the Analysis of Variance (ANOVA) approach. To assess the role of land cover conditions on error and uncertainties in retrievals, ten reference sites are chosen to represent various biomes.Initially, brightness temperatures are simulated by varying RTMs and their parameters to determine their sensitivity. The SM and VOD retrievals are compared with reference datasets, and the performance is found to be acceptable. Error decomposition analysis indicates RTMs and their parameters (h and ω) induce noticeable and significant error in SM and VOD retrievals. Errors contributions due to the above factors are more prominent in VOD retrievals compared to SM. However, the uncertainty contributions indicate minimal influence of RTMs and their parameters on SM retrievals. The ω parameter followed by the choice of RTM have significant contributions to the uncertainty in the VOD retrievals. The h parameter resulted in significant uncertainties in VOD retrievals under sparsely vegetated regions. The results are used to comment on the suitability of each of the three RTMs and the model parameterization design that could alleviate the issue of errors and uncertainties in concurrent retrievals of SM and VOD. These inferences shall contribute towards developing robust multi-parameter retrieval algorithms.

Robustness of Vegetation Optical Depth Retrievals Based on L-Band Global Radiometry

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil-plant-atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM-VOD retrievals are feasible by inverting the τ–ω model with two independent datasets in dual channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TB_v and TB_h), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the Degrees-of-Information (DoI) metric and propose a Signal-to-Noise Ratio (SNR) metric to assess the “retrievability” of VOD given the SMAP TB_v-TB_h linear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting applications of VOD in ecology and hydrology. Results show that regions with mainly non-woody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of under-determined inversions: the L3-DCA and the MTDCA, which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here.

Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth

NASA's Soil Moisture Active Passive (SMAP) satellite mission has been providing high-quality global estimates of soil moisture (SM) and vegetation optical depth (VOD) using L-band radiometry since 2015. To date, a variety of retrieval algorithms as well as surface roughness and scattering albedo have been developed. However, a comprehensive evaluation of different algorithms with the new surface parameters across diverse biomes, climates, and terrain slopes is lacking. To narrow down this knowledge gap, here we examine the performance of various existing algorithms, including V-pol Single Channel Algorithms (SCA-V), H-pol Single Channel Algorithms (SCAH), classic DCA, extended DCA (E-DCA), regularized DCA (RDCA), land parameter retrieval model (LPRM), multi-temporal DCA (MT-DCA), constrained multi-channel algorithm (CMCA), and spatially constrained multi-channel algorithm (S-CMCA). The SM estimates are evaluated against in-situ measurements from the International Soil Moisture Network (ISMN) while VOD estimates are compared with the two-band enhanced vegetation index (EVI2), tree height, and aboveground biomass.This study has led to several important findings: (1) The overall bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of SM estimates from different algorithms generally increase with vegetation density while their temporal correlations with in-situ measurements decrease as the terrain slope increases. (2) The divergence between different SM estimates is relatively larger over forested areas than non-forested areas. (3) In terms of temporal correlation with in-situ measurements, the SCA-V and RDCA outperform other algorithms over most land cover types and climates. (4) SCA-H typically underestimates SM more compared to other algorithms across sparsely vegetated areas and most climates. (5) The ubRMSE values demonstrate that all algorithms have close performance when EVI2 is less than 0.3; however, the performance of classic DCA decays notably when EVI2 exceeds 0.3. (6) VOD retrievals from RDCA exhibit improved spatial correlations with EVI2, tree height, and aboveground biomass across the globe compared to other algorithms. Overall, RDCA exhibits a good compromise between the high performance of SM and VOD.

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.

Impact of temperature and water availability on microwave-derived gross primary production

Abstract. Vegetation optical depth (VOD) from microwave satellite observations has received much attention in global vegetation studies in recent years due to its relationship to vegetation water content and biomass. We recently have shown that VOD is related to plant productivity, i.e., gross primary production (GPP). Based on this relationship between VOD and GPP, we developed a theory-based machine learning model to estimate global patterns of GPP from passive microwave VOD retrievals. The VOD-GPP model generally showed good agreement with site observations and other global data sets in temporal dynamic but tended to overestimate annual GPP across all latitudes. We hypothesized that the reason for the overestimation is the missing effect of temperature on autotrophic respiration in the theory-based machine learning model. Here we aim to further assess and enhance the robustness of the VOD-GPP model by including the effect of temperature on autotrophic respiration within the machine learning approach and by assessing the interannual variability of the model results with respect to water availability. We used X-band VOD from the VOD Climate Archive (VODCA) data set for estimating GPP and used global state-of-the-art GPP data sets from FLUXCOM and MODIS to assess residuals of the VOD-GPP model with respect to drought conditions as quantified by the Standardized Precipitation and Evaporation Index (SPEI). Our results reveal an improvement in model performance for correlation when including the temperature dependency of autotrophic respiration (average correlation increase of 0.18). This improvement in temporal dynamic is larger for temperate and cold regions than for the tropics. For unbiased root-mean-square error (ubRMSE) and bias, the results are regionally diverse and are compensated in the global average. Improvements are observed in temperate and cold regions, while decreases in performance are obtained mainly in the tropics. The overall improvement when adding temperature was less than expected and thus may only partly explain previously observed differences between the global GPP data sets. On interannual timescales, estimates of the VOD-GPP model agree well with GPP from FLUXCOM and MODIS. We further find that the residuals between VOD-based GPP estimates and the other data sets do not significantly correlate with SPEI, which demonstrates that the VOD-GPP model can capture responses of GPP to water availability even without including additional information on precipitation, soil moisture or evapotranspiration. Exceptions from this rule were found in some regions: significant negative correlations between VOD-GPP residuals and SPEI were observed in the US corn belt, Argentina, eastern Europe, Russia and China, while significant positive correlations were obtained in South America, Africa and Australia. In these regions, the significant correlations may indicate different plant strategies for dealing with variations in water availability. Overall, our findings support the robustness of global microwave-derived estimates of gross primary production for large-scale studies on climate–vegetation interactions.

The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes

AbstractThough coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth (VOD) retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019 to 2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the VOD retrievals into a dynamic phenology model. The results demonstrate that assimilating VOD observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019 to 2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff.

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals.

Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Soil moisture variation drives canopy water content dynamics across the western U.S.

Drought stress is a major contributing factor to plant mortality across the globe. Drought effects are often studied at the local scale, but recent advances in remote sensing allow for observations of plant water status across broad geographic scales. The vegetation optical depth (VOD) derived from satellite-based surface microwave emission has been shown to be sensitive to canopy water content, which is increasingly recognized as an important indicator of water relations and incipient mortality in plants. We develop an index which quantifies the normalized difference between night- and daytime diurnal VOD retrievals (nVODr) and apply it across the western U.S. to determine the relative sensitivity of plants to variations in water supply (soil moisture) and atmospheric water demand (vapor pressure deficit -VPD). Canopy water content dynamics were most sensitive to soil moisture variation at intermediate climatic water deficits where tree cover transitions to grass cover. These areas are in transitional climate zones and occur at ecotones between forest and non-forest vegetation where canopy water content dynamics are most sensitive to both soil moisture and VPD variation. Our results suggest that vegetation in semi-arid ecotones is likely to see the most proximal impacts of drought stress as the planet warms.

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.

Assimilation of vegetation optical depth retrievals from passive microwave radiometry

Abstract. Vegetation optical depth (VOD) retrievals from passive microwave sensors provide analog estimates of above-ground canopy biomass. This study presents the development and analysis of assimilating VOD retrievals from X-, C-, and L-band passive microwave instruments within the Noah-MP land surface model over the Continental U.S. The results from this study demonstrate that the assimilation of VOD retrievals have a significant beneficial impact on the simulation of evapotranspiration and GPP, particularly over the agricultural areas of the U.S. The improvements in the water and carbon fluxes from the assimilation of VOD from X- and C-band sensors are found to be comparable to those obtained from the assimilation of vegetation indices from optical sensors. The study also quantifies the relative and joint impacts of assimilating surface soil moisture and VOD from the Soil Moisture Active Passive (SMAP) mission. The utility of soil moisture assimilation for improving evapotranspiration (ET) is more significant over water-limited regions, whereas VOD DA is more impactful over areas where soil moisture is not the primary controlling factor on ET. The results also indicate that the information on moisture and vegetation states from SMAP can be simultaneously exploited through the joint assimilation of surface soil moisture and VOD. Since passive microwave-based VOD retrievals are available in nearly all weather conditions, their use within data assimilation systems offers the ability to extend and improve the utility obtained from the use of optical/infrared-based vegetation retrievals.

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.

Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models

Since 2010, SMOS (Soil Moisture and Ocean Salinity) retrievals of surface soil moisture (SM) and vegetation optical depth (VOD) have been produced through the inversion of the so-called Tau-Omega (TO) vegetation emission model. Tau-Omega is a 0th-order solution of the radiative transfer equations that neglects multiple scattering, conversely to 1st-order solutions as Two-Stream (2S). To date, very little is known about the compared retrieval performances of these emission models. Here, we inter-compared (SM, VOD) retrievals using the SMOS-IC algorithm running with the TO and 2S emission models. Retrieval performances obtained from TO and 2S were found to be relatively similar, except that a larger dry bias and a slightly lower SM unbiased RMSD were obtained with 2S and the VOD values of the two models vary over dense vegetation areas, both in terms of magnitude and seasonal variations. Considering this and the enhanced physical background of 2S that allows its implementation as a unified emission model for different applications, our study reveals the high interest of using Two-Stream in global retrieval algorithms at L-band.

Simultaneous retrieval of global scale Vegetation Optical Depth, surface roughness, and soil moisture using X-band AMSR-E observations

The radiative transfer scheme implemented for the retrieval of soil moisture from passive microwaves is a function of scattering, polarization mixing and attenuation effects of soil and vegetation. Theses factors are usually represented by Vegetation Optical Depth (VOD), vegetation scattering albedo, and surface roughness parameter, along with soil moisture. The VOD is the degree to which vegetation attenuates the microwave radiation. It has generally the dominant effect from vegetation, whereas scattering is negligible and close to zero. The surface roughness (which varies in space but not much in time) is until recently, often assumed to be a global constant. In this work, we attempted to simultaneously retrieve the VOD, the surface roughness parameter, and the soil moisture at the global scale using the Level 3 daily 0.25° X-band brightness temperatures of the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor. The methodology, coined as the Simultaneous Parameter Retrieval Algorithm (SPRA), is based on the premise that the vegetation dynamics undergo slower temporal changes than the soil moisture - an assumption, which is successfully used in the past for microwave radiometric retrievals at lower frequencies. Results indicate that the SPRA produces the VOD retrievals with reduced high-frequency noise when compared to the baseline Land Parameter Retrieval Algorithm (LPRM) retrievals. This effect assisted in identifying the influence of precipitation and cropping patterns on the temporal dynamics of the VOD. Good agreement is observed between the mean SPRA VOD and canopy height data (global correlation = 0.75). The spatial patterns of surface roughness parameter agree well with the roughness product (HR map) developed using Soil Moisture Ocean Salinity (SMOS) sensor based data (global correlation = 0.57). Validation of SPRA and LPRM soil moisture products with in-situ observations over the Contiguous United States (CONUS) indicated an improvement in mean ubRMSE with SPRA product (SPRA-0.11 m3/m3 and LPRM-0.18 m3/m3) and comparable mean Pearson correlations between the two products (SPRA-0.36 and LPRM-0.38). Further, a precipitation based consistency evaluation of SPRA and LPRM soil moisture retrievals indicated better skill of the SPRA product over India.

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.