Vegetation Optical Depth Research Articles

Pronounced loss of Amazon rainforest resilience since the early 2000s

The resilience of the Amazon rainforest to climate and land-use change is crucial for biodiversity, regional climate and the global carbon cycle. Deforestation and climate change, via increasing dry-season length and drought frequency, may already have pushed the Amazon close to a critical threshold of rainforest dieback. Here, we quantify changes of Amazon resilience by applying established indicators (for example, measuring lag-1 autocorrelation) to remotely sensed vegetation data with a focus on vegetation optical depth (1991–2016). We find that more than three-quarters of the Amazon rainforest has been losing resilience since the early 2000s, consistent with the approach to a critical transition. Resilience is being lost faster in regions with less rainfall and in parts of the rainforest that are closer to human activity. We provide direct empirical evidence that the Amazon rainforest is losing resilience, risking dieback with profound implications for biodiversity, carbon storage and climate change at a global scale.

Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band

Dual-polarimetric (i.e., HH and VV) scattering responses at X- and C-bands from indigenously designed far-field bistatic specular (bi-spec) scatterometer acquired over the entire rice crop phenology have been analyzed using a modified parametric radiative transfer model (MRTM). The scattering responses are examined over a wide-ranging bi-spec incidence angle varying from 20° to 60° at 10° intervals. Furthermore, optimization of the bi-spec scatterometer system showed high sensitivity at 40° specular angle of incidence based on the correlation analysis between the measured value of bi-spec scattering coefficient (σMeasured0) and vegetation biophysical parameters such as leaf area index (LAI) and plant water content (PWC). The MRTM implied to investigate the dominance of surface (σSurface0) and vegetation(σVegetation0) specular scattering components within the total value of simulated bi-spec scattering coefficient (σSimulated0) in forward scattering alignment (FSA) convention. The vegetation phase function (VPF) and a bi-directional reflectance distribution function (BRDF) are parameterized to approximate scattering responses from the vegetation volume layer and the surface beneath vegetation. In addition, empirical frequency-specific parameters (i.e., b1and b2) are used to simulate temporal dynamics of σSimulated0 using a linear relationship between vegetation optical depth (VOD) with LAI and PWC. The model and empirical frequency-specific parameters are calibrated using a constrained non-linear least square optimization algorithm, and the results are validated against the value of σMeasured0. According to the simulation findings, the total specular scattering decomposition offers a robust model for interpreting time-series microwave scattering scenarios through vegetation in the FSA convention. Moreover, as compared to C-band, the inverse modeling of MRTM showed high retrieval accuracies of LAI at VV polarization and PWC at HH polarization for the X-band.

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

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

Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China

A new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI) -combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE (< 0.04 m3/m3) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m3/m3), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple independent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite- and model-based soil moisture products.

Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China

Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.

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 &#x03C4;&#x2013;&#x03C9; 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 &#x201C;retrievability&#x201D; 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.

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

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

Passive Microwave Retrieval of Soil Moisture Below Snowpack at L-Band Using SMAP Observations

Soil and its water content can remain unfrozen below an insulative snow cover and modulate snowmelt infiltration and runoff. In this paper, an emission model is proposed to account for L-band microwave emission of wet soils below a dry snowpack covered with an emerging moderately dense vegetation canopy. The model links the well-known tau-omega emission model with the snowpack dense media radiative transfer theory as well as a multi-layer composite reflection model to account for the impacts of a snow layer on the upwelling soil and the downwelling vegetation emission, respectively. It is demonstrated that even though a dry snow is a low-loss medium at L-band, omission of its presence leads to underestimation of soil moisture (SM), especially when soil (snow) becomes wetter (denser). Constrained inversion of the proposed emission model, using brightness temperatures from the Soil Moisture Active and Passive (SMAP) satellite, shows that the retrievals of SM and vegetation optical depth (VOD) are achievable with unbiased root mean squared errors of 0.060 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and 0.124 [-], when compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data from the International Soil Moisture Network (ISMN) as well as VOD-derived values from the Normalized Difference Vegetation Index (NDVI) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations.

Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method

Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth data and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH) and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of the PLMR’s dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R) ≥ 0.864, root mean square error (RMSE) ≤0.04 m3/m3, and unbiased RMSE (unRMSE) ≤ 0.035 m3/m3), while the 3CH attempt yields the lowest uncertainty. Additionally, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.

A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval

Remotely sensed soil moisture (SM) with high accuracy and high spatial&#x2013;temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) and the surface roughness parameter (<inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>). The CYGNSS L1 Version 2.1 data of the years 2017&#x2013;2018 and 2019&#x2013;2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and <i>in situ</i> SM data on a global scale (<inline-formula> <tex-math notation="LaTeX">$R =0.679$ </tex-math></inline-formula>, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.051\,\,\text{m}^{3}\text{m}^{-3}$ </tex-math></inline-formula>, and MAE <inline-formula> <tex-math notation="LaTeX">$=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ </tex-math></inline-formula> against SMAP SM; <inline-formula> <tex-math notation="LaTeX">$R = 0.729$ </tex-math></inline-formula> against <i>in situ</i> SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2&#x0025; and spatial coverage increased by 31.6&#x0025;.

Monitoring the Reduced Resilience of Forests in Southwest China Using Long-Term Remote Sensing Data

An increase in the frequency and severity of droughts associated with global warming has resulted in deleterious impacts on forest productivity in Southwest China. Despite attempts to explore the response of vegetation to drought, less is known about forest’s resilience in response to drought in Southwest China. Here, the reduced resilience of the forest was found based on remotely sensed optical and microwave vegetation products. The spatial distribution and temporal variation of resilience-reduced forest were assessed using the standardized precipitation evapotranspiration index (SPEI) and vegetation optical depth (VOD). Our findings showed that 40–50% of the forest appeared to have abnormally low resilience approximately 6 months after the severe drought. The spatial distributions of abnormally low resilience had a good agreement with the regions affected by the 2009–2011 drought events. In particular, our results indicated that areas of afforestation were more susceptible to drought than natural forest, maybe due to the different water uptake strategy of the diverse root systems. Our findings highlight the vulnerability of afforestation areas to climate change, and recommend giving more attention to soil water availability.

Tropical tall forests are more sensitive and vulnerable to drought than short forests

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring

The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (so-called b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest.

A Global Bottom‐Up Approach to Estimate Fuel Consumed by Fires Using Above Ground Biomass Observations

AbstractReal‐time estimates of the fuel consumed during a fire (dry‐matter) relies on indirect estimate from remotely sensed released energy combined with biome dependent conversion coefficients. The uncertainties in the conversions lead to the use of inflation factors to avoid large underestimations in the prediction of aerosol load during fires. In this study, adopting two different fire inventories for burned areas, we apply above ground biomass (AGB) observations [from Soil Moisture and Ocean Salinity (SMOS) L‐band vegetation optical depth] as proxy for fuel load in deriving estimates of dry‐matter. These new estimates are then converted into biomass burning aerosols and validated against independent aerosol optical depth observations from the AERONET in situ global network. Results showed that use of AGB as a proxy observation of fuel load improves fire emission estimates and substituting the need for indirect dry‐matter estimates from remotely sensed fire activity or for the use of inflation factors.

Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets

There has been a debate regarding whether the Amazon rainforest is greening during the dry season. This is partially because of the great uncertainty associated with the ability of different vegetation indices to accurately assess tropical vegetation status. This paper, revisit this issue by comprehensively examining the seasonal variations in vegetation recorded in various satellite-based vegetation datasets, namely, the leaf area index (LAI), contiguous solar-induced fluorescence (CSIF), enhanced vegetation index (EVI), and vegetation optical depth (VOD). All four of these vegetation datasets show an increase in vegetation during the dry season in most parts of the Amazon; however, the vegetation changes are not only spatially variable, but also differ among the datasets. This may be attributable in part to the different physical characteristics captured by each of the datasets. For example, the seasonal maximum value occurs first in the LAI, followed by the CSIF, EVI, and VOD, in that order. The seasonal cycle of the LAI agrees reasonably well with in-situ observations of leaf flush and leaf fall. As new leaf production offsets senescence and abscission, the dry-season vegetation increases in most parts of the Amazon rainforest. Partial correlation analysis was used to further investigate the potential climatic cues (i.e., precipitation, temperature and radiation) associated with the seasonal changes recorded in the vegetation data. We found that precipitation and radiation were the dominant potential cues for seasonal VOD (48%) and LAI (59%) changes, respectively. However, CSIF appears to be associated more closely with temperature and precipitation, with significant correlations observed across ∼x223C 37% of the Amazon rainforest area for both with CSIF. Finally, variations in the EVI showed similar sensitivity to all three climatic variables considered. The findings presented here will greatly improve our understanding of vegetation dynamics and the carbon cycle in the Amazon rainforest ecosystem.

A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations

Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).

Time-variations of zeroth-order vegetation absorption and scattering at L-band

Surface soil moisture and vegetation optical depth (VOD), as an indicator of vegetation wet biomass, from passive microwave remote sensing have been increasingly applied in global ecology and climate research. Both soil moisture and VOD are retrieved from satellite brightness temperature measurements assuming a zeroth order radiative transfer model, commonly known as the tau-omega model. In this model the emission of a vegetated surface is dependent on soil moisture, vegetation absorption and vegetation scattering. Vegetation scattering is normally represented by the single scattering albedo, ω, and is commonly assumed to be a time-invariant calibration parameter to achieve high accuracy in soil moisture estimation. Therefore, little is known about ω dynamics in the context of its ecological information content. Furthermore, VOD and ω are functions of more fundamental absorption and scattering coefficients κa and κs. In this study, we retrieve both VOD and ω as well as κa and κs in two separate tau-omega model frameworks using known soil moisture information. Our sensitivity analysis confirms that vegetation attenuation has a strong impact on the sensitivity of the retrieval to noise. If vegetation attenuation approaches zero, ω is weakly constrained, which leads to strong ω dynamics. If vegetation attenuation is very high, VOD, κa and κs are increasingly less constrained and susceptible to noise, while ω becomes quasi time-invariant. Coinciding with our sensitivity study, global retrievals from SMAP brightness temperatures exhibit large ω dynamics in drylands and deserts, decreasing rapidly with increasing vegetation cover. In drylands ω might peak during the dry season at the seasonal VOD minimum. With increasing vegetation attenuation, ω dynamics start to follow leaf phenology before they transition to a nearly time-invariant ω in forested areas. VOD, κa and κs follow the dynamics of wet biomass and peak after the maximum of leaf area index and precipitation. Our results suggest that zeroth order scattering is generally time-invariant over a majority of the global vegetated areas. In general, time dynamic ω and κs might provide additional information on the state of the vegetation canopy, which can be valuable for biosphere studies and relevant for the parameterization of vegetation scattering in soil moisture retrievals.

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

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.