Vegetation Optical Depth Research Articles

Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations

The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.

Interannual Variations of Vegetation Optical Depth are Due to Both Water Stress and Biomass Changes

AbstractMicrowave radiometry can be used to measure vegetation water content through vegetation optical depth (VOD). VOD can vary due to changes in water stress alone, but also scales with aboveground biomass. Several studies have therefore interpreted VOD temporal anomalies as linearly proportional to biomass anomalies, neglecting the influence of water stress. Here, we explicitly test this assumption using a new annual‐resolution data set of biomass derived from optical, radar, and lidar remote sensing. Both L‐band and X‐band VOD datasets are tested. Although VOD and biomass variations are highly correlated in space, their temporal anomalies are almost uncorrelated. At regional scale, it is as common for VOD anomalies to be more correlated to root‐zone soil moisture anomalies (a proxy for water stress) than to biomass anomalies as for the opposite to occur. Care should therefore be taken when deriving biomass anomalies from VOD, especially in the absence of any large‐scale biomass disturbances.

Modeling the Microwave Transmissivity of Row Crops

We develop a method to model the microwave transmissivity of row crops that explicitly accounts for their periodic nature as well as multiple scattering. We hypothesize that this method could eventually be used to improve satellite retrieval of soil moisture and vegetation optical depth in agricultural regions. The method is characterized by unit cells terminated by periodic boundary conditions and Floquet port excitations solved using commercial software. Individual plants are represented by vertically oriented dielectric cylinders. We calculate canopy transmissivity, reflectivity, and loss in terms of S-parameters. We validate the model with analytical solutions and illustrate the effect of canopy scattering. Our simulation results are consistent with both simulated and measured data published in the literature.

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.

Drought effects on leaf fall, leaf flushing and stem growth in the Amazon forest: reconciling remote sensing data and field observations

Abstract. Large amounts of carbon flow through tropical ecosystems every year, from which a part is sequestered in biomass through tree growth. However, the effects of ongoing warming and drying on tree growth and carbon sequestration in tropical forest is still highly uncertain. Field observations are sparse and limited to a few sites, while remote sensing analysis shows diverging growth responses to past droughts that cannot be interpreted with confidence. To reconcile data from field observations and remote sensing, we collated in situ measurements of stem growth and leaf litterfall from inventory plots across the Amazon region and other neotropical ecosystems. These data were used to train two machine-learning models and to evaluate model performance on reproducing stem growth and litterfall rates. The models utilized multiple climatological variables and other geospatial datasets (terrain, soil and vegetation properties) as explanatory variables. The output consisted of monthly estimates of leaf litterfall (R2= 0.71, NRMSE = 9.4 %) and stem growth (R2= 0.54, NRMSE = 10.6 %) across the neotropics from 1982 to 2019 at a high spatial resolution (0.1∘). Modelled time series allow us to assess the impacts of the 2005 and 2015 droughts in the Amazon basin on regional scales. The more severe 2015 drought was estimated to have caused widespread declines in stem growth (−1.8σ), coinciding with enhanced leaf fall (+1.4σ), which were only locally apparent in 2005. Regions in the Amazon basin that flushed leaves at the onset of both droughts (+0.9σ∼+2.0σ) showed positive anomalies in remotely sensed enhanced vegetation index, while sun-induced fluorescence and vegetation optical depth were reduced. The previously observed counterintuitive response of canopy green-up during drought in the Amazon basin detected by many remote sensing analyses can therefore be a result of enhanced leaf flushing at the onset of a drought. The long-term estimates of leaf litterfall and stem growth point to a decline in stem growth and a simultaneous increase in leaf litterfall in the Amazon basin since 1982. These trends are associated with increased warming and drying of the Amazonian climate and could point to a further decline in the Amazon carbon sink strength.

Soil moisture and vegetation optical depth retrievals over heterogeneous scenes using LEWIS L-band radiometer

The Soil Moisture and Ocean Salinity (SMOS) satellite has been providing global data for more than 11 years. The algorithm to retrieve surface soil moisture (SM) and vegetation optical depth (VOD) from Brightness Temperatures (TB) has constantly evolved as our understanding of the instrument and of the responses to various ecosystems increased. The evolution of the radiative transfer modelling used in the inversion process is in part based upon ground measurements done on experimental sites. We present the particular case of an L-band radiometer placed on top of a 800- meter-high cliff. This design allows us to study large and various scenes when compared to classical experiments where a radiometer is placed 10–20 m above a surface. With our set up, the footprints cover areas larger than 300 m in diameter, covering thus several land use types and the footprint can be directed towards different dominant land classes (forest, crops, urban etc…). This paper presents the whole set up consisting of in situ measurements (soil moisture and surface temperatures) and L-band radiometry acquired by the LEWIS instrument at high incidence angles. We perform SM/VOD retrievals from brightness temperatures using various configurations which are compared to the in situ observations. We test in particular a 2-VOD retrieval approach that consists in deriving one SM for the entire scene but two VOD, one for agricultural/low vegetation and a VOD for the forest parts, which improves the performance of the SM/VOD retrievals.

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.

Global Coordination in Plant Physiological and Rooting Strategies in Response to Water Stress

AbstractPlants employ a range of strategies to modulate the impact of water stress, including changes to rooting depth and hydraulic conductance (e.g., xylem conductance). However, it is still poorly understood how these strategies vary in relation to climate and land cover types and how they could coordinate globally. Based on daily microwave vegetation optical depth (VOD) from AMSR‐E and AMSR2 over 2002–2011, we estimate two proxies for stress regulation: (a) an effective plant rooting depth and (b) the effective plant hydraulic conductance to delineate two strategies: deep rooting and strong physiological regulation. We find that plants with deeper (e.g., evergreen/deciduous broadleaf forest) are mostly distributed in warm or wet regions, and maintain a relatively steady nighttime VOD because of access to deeper water. Taller plants exhibit greater drop in daytime VOD due to their greater physiological vulnerability. While physiological regulation appears to be the dominant water stress regulation strategy at Northern high latitudes where open shrubland and (woody) savannas are distributed, this physiological regulation is coupled with deep rooting in forest and (woody) savanna areas in the tropics, Eastern US, and Southeastern China. Meanwhile, grasslands in the Western US, Central Asia, Northeastern China, and Mongolia Plateau may be the regions most susceptible to water stress impact because neither water stress mitigation strategy is present. This new framework paves the road for a better understanding of plant water stress strategies at the global scale, and for enhancing large‐scale drought prediction and drought impact assessment in Earth system models by improving plant water stress response.

VODCA2GPP - A new global, long-term (1988-2020) GPP dataset from microwave remote sensing

Abstract. Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble). These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures. The VODCA2GPP dataset is available at TU Data ( https://doi.org/10.48436/1k7aj-bdz35 ; Wild et al., 2021).

Temperature effects on L-band vegetation optical depth of a boreal forest

ElectroMagnetic (EM) reasons resulting in temperature dependence of L-band Vegetation Optical Depth (L-VOD) are currently overlooked in remote sensing products. Discrepancies in retrievals of geophysical surface properties over vegetated areas can result from this incompleteness. This perception motivated to explore EM considerations in how temperature drives L-VOD of a boreal forest. Thereto, a novel physics-based model is developed and evaluated to assess L-VOD sensitivities to canopy temperature and some other model parameters. The L-VOD model is compared to L-VOD derived from close-range L-band brightness temperatures measured through the tree canopy at the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankylä (Finland) during a 4-week and a 1-day period in 2019. Furthermore, the model's ability to explain L-VOD retrieved from brightness temperatures of the “Soil Moisture and Ocean Salinity” (SMOS) satellite over the “Sodankylä grid cell” is investigated. Experimental L-VOD are maximal at around 0 °C and decrease when canopy temperature is moving away from zero degree Celsius. This temperature response, observed at different temporal- and spatial scales, is captured by the proposed L-VOD model and explained by freezing tree sap-water and the dependence of water permittivity on temperature. The demonstrated EM-induced temperature dependence suggest caution with interpreting satellite-based L-VOD, because increased L-VOD around the freezing point is not solely due to increased biomass or rehydration of the vegetation. Further, our study can find future application to compensate L-VOD for EM-induced temperature sensitivity. This potentially leads to improved explanatory power of temperature normalized L-VOD for characterization of forest phenology. Furthermore, we suggest examining the presence and strength of the demonstrated L-VOD temperature response as a practical L-VOD retrieval quality assessment method under steady forest phenology.

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.

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

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

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.

Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion

Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially, depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is, therefore, crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for parameterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model–data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with data sets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; collectively AMSR-E). VOD is proportional to vegetation water content and, therefore, closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere–Land Exchange Inverse (ALEXI) model is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by plant functional types (PFTs) improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model–data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.

Variations of carbon allocation and turnover time across tropical forests

AbstractAimThe carbon sink in tropical forests is a highly uncertain component of the global carbon budget. An understanding of the processes controlling this sink requires better quantification of carbon allocation, stocks and turnover times.LocationTropical forests.Time period2010–2017.Major taxa studiedTropical forest ecosystem.MethodsWe develop a novel data assimilation system using satellite‐based annual above‐ground biomass derived from L‐band vegetation optical depth with 25 km × 25 km grid spacing, together with leaf area, to constrain the 25 km × 25 km carbon allocation patterns of net primary productivity (NPP) into the wood, leaf and root pools, and their turnover times. Our average data‐driven estimates of these variables are broadly consistent with independent ground‐based estimates of NPP allocation and wood turnover from forest inventory plots.ResultsIn tropical forest, on average, the NPP allocation into wood (0.30 ± 0.04) is significantly higher than that into leaves (0.24 ± 0.07). From the wet to dry tropics, forest NPP allocation into both wood and leaves declines slightly. The turnover times of forest leaf pools exhibit little spatial variation, whereas the turnover times of wood pools in Africa (median and interquartile range: years) are slightly longer than those in South America ( years) and Southeast Asia ( years). Our datasets reveal emergent trade‐offs across climatic and vegetation gradients between growth and life span/turnover for both wood and leaves. The spatial gradients of NPP allocation to wood/leaves are associated with canopy height, adjusted by climate condition and nutrient acquisition. The spatial gradients of wood and leaf turnover times are influenced mainly by climate and leaf characteristics.Main conclusionsOur data‐driven estimates of carbon allocation and turnover times provide a basis for more detailed exploration of these mechanisms in field studies. This highlights that improved model representation of carbon allocation and turnover is necessary for more accurate prediction of future carbon dynamics.

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

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

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2 =0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics

Satellite passive microwave observations from 1.4 to 36 GHz already showed sensitivity to vegetation parameters, primarily through the calculations of the Vegetation Optical Depth (VOD) at individual window frequencies, separately. Here we evaluate the synergy of this frequency range for vegetation characterization, through the estimation of two vegetation parameters, its foliage and the photosynthesis activity as described by the Normalized Difference Vegetation Index (NDVI), and its woody components and carbon stock as described by the Above Ground Carbon (AGC), using different combinations of channels in the considered frequency range. Neural network retrievals are trained on these two vegetation parameters (NDVI and AGC), for several microwave channel combinations, including the future Copernicus Imaging Microwave Radiometer (CIMR) that will observe simultaneously in window channels from 1.4 to 36 GHz, for the first time. This methodology avoids the use of any assumptions in the complex interaction between the surface (vegetation and soil) and the radiation, as well as any ancillary observations, to propose a genuine and objective evaluation of the information content of the passive microwave frequencies for vegetation characterization. Our analysis quantifies the synergy of the microwave frequencies from 1.4 to 36 GHz, and shows the expected potential of the CIMR instrument for the monitoring of vegetation parameters in tropical environments, as compared to current instruments. It also confirms that the 1.4 GHz observations have a high sensitivity to AGC, as compared to the other single frequencies up to 36 GHz, at least under tropical environments.

Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm

Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm3/cm3 to 0.023 cm3/cm3) than the multi-angular version (ubRMSD from 0.026 cm3/cm3 to 0.034 cm3/cm3) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended.