Vegetation Optical Depth Research Articles

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation.

Enhanced Satellite Monitoring of Dryland Vegetation Water Potential Through Multi‐Source Sensor Fusion

AbstractDrylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine‐learning was employed to re‐construct global satellite microwave vegetation optical depth (VOD) retrievals to 500‐m resolution. The re‐constructed results were able to delineate diverse vegetation conditions undetectable from the original 25‐km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro‐climate variability over diverse sub‐regions. The re‐constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil‐vegetation‐atmosphere continuum in heterogeneous drylands.

Hysteresis in seasonal land-atmospheric interactions over India and its characteristics across croplands and forests

Abstract Satellite-derived Vegetation Optical Depth and Soil Moisture (SM) data reveal the critical role of the soil-vegetation continuum in storing rainwater during the Indian Summer Monsoon and supporting evapotranspiration (ET) during the dry non-monsoon season. During the non-monsoon drier period, the climatologically estimated spatial mean of ET exceeds precipitation input, a phenomenon known as the Soil-Vegetation Capacitor (SVC) effect, which is pivotal in maintaining ecosystem productivity. Notably, our analysis reveals significant variations in the capacitor period between croplands and forests, with croplands exhibiting a ~77 days longer due to dual crop seasons influenced by regional precipitation. The well-recognized hysteresis curves, observed in magnetization and soil-water characteristic curves (SWCC), highlight phenomena where a system's state is influenced by its historical inputs or states and are integral to our findings. We report a previously undocumented seasonal hysteresis in the relationship between the evaporative fraction (EVF) and SM for Indian croplands and forests. We further found that the croplands SM-EVF relation exhibits a reversal in hysteresis in the case of root-zone SM. The surface SM-EVF hysteresis is not present in forests with large root depths and reduced soil evaporation due to high canopy shading, and yet it is present for the root-zone SM. With its reversal for croplands, the newly found hysteresis must be addressed in redefining the critical SM threshold to demarcate the energy and water-limiting regimes. It should be incorporated in the land surface modeling parameterization. Additionally, we observed hysteresis in the soil moisture-gross primary productivity (SM-GPP) relationship across both land covers and soil profiles (surface and root-zone), underscoring the need to investigate such processes to consider their dynamics in future ecological and hydrological models.

VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring

VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring

Strong positive direct impact of soil moisture on the growth of central asian grasslands

As the issue of global climate change becomes increasingly prominent, the grassland ecosystems in Central Asia are facing severe challenges posed by the impacts of climate change. However, the dominant factors, impact pathways, and cumulative and time-lagged effects of climate factors on various grassland indices remain to be explored. This study selected data from 1988 to 2019, including Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), Net Primary Productivity (NPP), and Vegetation Optical Depth (VOD), to characterize grassland coverage, greenness, biomass accumulation, and water content features. Utilizing multiple linear regression, path analysis, and correlation analysis, this study investigated the dominant effects, direct impacts, indirect influences, and cumulative and time-lagged effects of climate factors on various grassland indices from spatial and climatic zone perspectives. The research findings indicate that over time, the grassland FVC and NPP exhibited increasing trends, while the LAI and VOD showed decreasing trends. Grassland indices are primarily influenced by precipitation and soil moisture (SM). The direct impact of SM on grassland indices was higher than precipitation. Vapour pressure deficit (VPD) has a direct negative impact on grassland indices. Grassland indices are subject to positive indirect effects from precipitation via SM and negative indirect effects from VPD via SM. Precipitation and SM mainly exhibited no cumulative and time-lagged effects on the impact of grassland VOD. VPD primarily demonstrated cumulative and time-lagged effects on grassland indices. The research findings offer valuable insights for conserving grassland ecosystems in Central Asia, as well as for shaping socioeconomic strategies and formulating climate policies.

Assimilation of Sentinel‐1 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass

AbstractThis study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.

Global L-band equivalent AI-based vegetation optical depth dataset

The L-band vegetation optical depth data garners significant interest for its ability to effectively monitor vegetation, thanks to minimal saturation within this frequency range. However, the existing datasets have limited temporal coverage, constrained by the start of the respective satellite missions. Global L-band equivalent AI-Based Vegetation Optical Depth or GLAB-VOD is a global long-term consistent microwave vegetation optical depth dataset created using machine learning to expand the SMAP-IB VOD dataset temporal coverage from 2015-2020 to 2002-2020. The GLAB-VOD dataset has an 18-day temporal resolution and 25 km spatial resolution on the EASE2 grid and covers 2002-2020. An auxiliary consistent daily brightness temperature product, called GLAB-TB, is developed in parallel and ensures the consistency of the VOD product across time periods with different microwave satellites. As a result of its temporal consistency, this dataset can be used to study long-term global and regional trends in vegetation biomass and utilized in any other applications where long-term consistency is necessary. The GLAB-VOD dataset shows excellent spatial correlation globally when compared with biomass (up to R = 0.92) and canopy height (R = 0.93), outperforming its target dataset, SMAP-IB VOD.

EVALUATING REMOTE SENSING-BASED DROUGHT INDICES: STRENGTHS, LIMITATIONS, AND APPLICABILITY ACROSS SUB-SAHARAN AFRICA'S AGRO-ECOLOGICAL ZONES: A REVIEW

This study reviews the application and effectiveness of various remote sensing (RS) indices for drought monitoring in Sub-Saharan Africa (SSA). Given the region’s diverse climatic zones and frequent drought occurrences, accurate and timely assessment tools are crucial. The study examines indices from different spectral regions, including optical, thermal infrared, and microwave bands, focusing on their spatial and temporal resolutions, data availability, strengths, and limitations. Optical indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are effective in semi-arid and sub-humid zones where vegetation density varies. Thermal infrared indices, including the Temperature Condition Index (TCI), the Vegetation Health Index (VHI), and the Temperature Vegetation Dryness Index (TVDI), provide insights into thermal anomalies and vegetation health, with TCI particularly suited for semi-arid zones and TVDI useful in both semi-arid and sub-humid zones. Microwave indices, such as the Normalized Backscatter Moisture Index (NBMI), Vegetation Optical Depth (VOD), and the Microwave Polarization Difference Index (MPDI), excel in capturing soil moisture and vegetation water content, proving useful in humid forest and semi-arid zones. The integration of these indices with other meteorological and hydrological data enhances drought monitoring and management strategies. Recommendations are made for the optimal use of these indices across different SSA agroecological zones.

A novel AMSR2 retrieval algorithm for global C-band vegetation optical depth and soil moisture (AMSR2 IB): Parameters' calibration, evaluation and inter-comparison

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (HR) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and HR values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and HR variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and HR. As a benchmark for the calibration, we optimized the values of ω and HR by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and HR, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and HR.For the globally-constant calibration, the optimal values of ω = 0.05 and HR = 0.1 were found to yield the best results. For the land cover-based calibration, an inverse relationship between ω/HR and canopy height was observed, with ω ranging from 0.04 to 0.06 and HR ranging from 0.1 to 0.7 for heights between 0 and 30 m. The algorithm employing a land cover-based calibration (INRAE Bordeaux 2, IB2) exhibited better performance than the one utilizing a globally-constant calibration (INRAE Bordeaux 1, IB1) in evaluating retrieved SM against in situ measurements, as well as in evaluating C-VOD vs various vegetation variables including aboveground biomass (AGB), tree cover, canopy height and several optical vegetation indices. Comparison with LPRM suggested that our IB2 C-VOD retrievals present improved performances in terms of both spatial and temporal results with all considered vegetation variables (spatial correlation (R) between various vegetation variables and C-VOD of 0.76–0.83 for IB2 vs 0.69–0.79 for LPRM), and exhibited lower saturation effects when compared with AGB. In addition, the IB2 SM produced lower root mean squared error (RMSE) (0.147 vs 0.217 m3/m3), bias (−0.03 vs 0.09 m3/m3), and ubRMSE (0.066 vs 0.067 m3/m3) when compared with in situ measurements, although it showed a lower R compared to LPRM SM.

P-band radiometry for enhanced vegetation optical depth (VOD) and soil moisture retrieval in dense crop canopies

P-band (0.3–1 GHz) radiometry shows promise in enhancing the next generation of radiometer missions for global soil moisture and vegetation optical depth (VOD) monitoring, particularly in moderately to densely vegetated areas, which remain challenging for current L-band (1.4 GHz) missions such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, the superiority of P-band over L-band in this regard has not been experimentally verified, and the quantitative differences remain unexplored. To address this gap, the P-band Radiometer Inferred Soil Moisture (PRISM) tower-based experiment was conducted, pioneering a comprehensive evaluation of P- (0.75 GHz) and L-band (1.4 GHz) radiometry in densely vegetated areas with vegetation water content (VWC) up to 20 kg/m2. The results revealed that P-band VOD was consistently lower than L-band VOD, suggesting reduced vegetation attenuation and thus a higher VWC threshold for reliable soil moisture sensing at P-band. The VWC thresholds for P- and L-band were estimated at 7.7 kg/m2 and 4.0 kg/m2, respectively, suggesting that P-band radiation could go through the vegetation approximately twice as effectively as L-band radiation. Additionally, the Microwave Polarization Difference Index (MPDI) was identified as a strong predictor of retrieval performance because it established a good link between retrieval errors and VWC. Regarding VOD retrieval, robust logarithmic relationships (R2 > 0.9) between VOD and VWC were established for both bands within the 0–20 kg/m2 VWC range, challenging the previously assumed linear relationship. Moreover, P-band demonstrated greater retrieval robustness as its dual-polarized TB observations contain more independent information than those of L-band. This research establishes the foundation for a successful passive microwave mission at P-band, aiming to improve the quality of global VOD and soil moisture datasets.

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Improvements to a Crucial Budyko-Fu Parameter and Evapotranspiration Estimates via Vegetation Optical Depth over the Yellow River Basin

Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between ω and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter ω for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter ω established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter ω was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development.

Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability

Abstract. Recent observation-based studies suggest that the Amazon rainforest has lost substantial resilience since 1990, indicating that the forest might undergo a critical transition in the near future due to global warming and deforestation. The idea is to use trends in a lag-1 auto-correlation of leaf density as an early-warning signal of an imminent critical threshold for rainforest dieback. Here we test whether the observed change in auto-correlations could arise from internal variability using historical and control simulations of nine sixth-generation Earth system model ensembles (Phase 6 of the Coupled Model Intercomparison Project, CMIP6). We quantify trends in the leaf area index auto-correlation from both models and satellite-observed vegetation optical depth from 1990 to 2017. Four models reproduce the observed trend with at least one historical realization whereby the observations lie at the upper limit of model variability. Three out of these four models exhibit similar behavior in control runs, suggesting that historical forcing is not necessary for simulating the observed trends. Furthermore, we do not observe a critical transition in any future runs under the strongest greenhouse gas emission scenario (SSP5-8.5) until 2100 in the four models that best reproduce the past observed trends. Hence, the currently observed trends could be caused simply by internal variability and, unless the data records are extended, have limited applicability as an early-warning signal. Our results suggest that the current rapid decline in the Amazon rainforest coverage is not foremost caused by global warming.

Emerging Methods to Validate Remotely Sensed Vegetation Water Content

AbstractSatellite‐retrieved vegetation optical depth (VOD) has provided extensive insights into global plant function (such as, carbon stocks, water stress, crop yields) because of VOD's ability to monitor plant water stress and biomass at near daily temporal frequency under all‐weather conditions. However, arguably, the greatest challenge with broadly applying VOD is its lack of validation partly because of VOD's simultaneous sensitivity to plant water status and biomass changes, as well as intensive methods required to measure these properties in‐situ. Here, inspired by the recent Yao et al. (2024), https://doi.org/10.1029/2023GL107121 article, I argue that VOD estimated from global navigation satellite systems (GNSS) and land surface models with plant hydraulic schemes are two emerging methods that show promise for more widely validating satellite‐based VOD. I encourage wider adoption of these approaches to validate and further advance satellite‐based VOD research.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

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.

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.

Investigating Diurnal and Seasonal Cycles of Vegetation Optical Depth Retrieved From GNSS Signals in a Broadleaf Forest

AbstractVegetation Optical Depth (VOD) has emerged as a valuable metric to quantify water stress on vegetation's carbon uptake from a remote sensing perspective. However, existing spaceborne microwave remote sensing platforms face limitations in capturing the diurnal VOD variations and global products lack site‐level validation against plant physiology. To address these challenges, we leveraged the Global Navigation Satellite System (GNSS) L‐band microwave signal, measuring its attenuation by the canopy of a temperate broadleaf forest using a pair of GNSS receivers. This approach allowed us to collect continuous VOD observations at a sub‐hourly scale. We found a significant seasonal‐scale correlation between VOD and leaf water potential. The VOD diurnal amplitude is affected by soil moisture, plant transpiration and leaf surface water. Additionally, VOD can help independently estimate plant transpiration. Our findings pave the way for a deeper understanding of response of the vegetation to water stress at finer temporal scales.

Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

The success of ecological engineering projects on vegetation restoration in China strongly depends on climatic conditions

China has implemented extensive ecological engineering projects (EEPs) during recent decades to restore and enhance ecosystem functioning. However, the effectiveness of these interventions can vary due to factors such as local climate and specific project objectives. Here, we used two independent satellite remote sensing datasets, including the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) and vegetation optical depth from Ku-band (Ku-VOD), to investigate the vegetation trends in two hotspot regions of EEPs characterized by different climate conditions, i.e., the xeric/semi-xeric Loess Plateau and mesic southwest China. We found diverging vegetation greenness/biomass trend shift patterns in these two regions as a result of the combined effects of EEPs and climate variations, as indicated by changes in the Standardized Precipitation Evapotranspiration Index (SPEI). In the Loess Plateau, where no significant climate variations were observed, NDVI/Ku-VOD increased continuously after the implementation of key EEPs in 2000. Conversely, southwest China has experienced persistent drying since 2000, and vegetation greenness/biomass showed an increasing trend during the initial stages of ecological engineering implementation but subsequently reversed towards a decline due to the continued dry climatic conditions. We used the residual trend method to separate the influence of EEPs from climate variations on vegetation trends and found a positive effect of the ecological management practices in the Loess Plateau, yet a predominantly negative effect in the southwest China region, which means that projects implemented in southwest China did not lead to a long-term improvement in vegetation growth under the given climate conditions in southwest China. This adverse impact suggests that ecological engineering practices could potentially increase the ecosystem's vulnerability to droughts, owing to the increased transpirational water demands introduced by ecological engineering interventions. Our study highlights the importance of considering the expected occurrence and magnitude of climatic variability when implementing large-scale EEPs.