Microwave Vegetation Indices Research Articles

Divergent Responses of Summer Terrestrial Evapotranspiration to Cloud Increase in East Asia

AbstractCloud impact on terrestrial evapotranspiration (ET) can be significant, while a fundamental understanding of ET response to cloud change has not been addressed in regional aspects. In this study, seven ET datasets from satellite and land surface models are used to analyze the summer ET in response to low cloud cover (LCC) over East Asia, in combination with satellite microwave and optical vegetation data, as well as ERA5 climatic and surface parameters. In general, all ET datasets present a strong negative relationship with LCC over dense vegetation (e.g., forests), which leads to the ET reduction of more than −0.5 mmday−1 under the large LCC increase, compared with the least cloudy sky conditions. Such reduction is stronger under more humid and denser forests (aridity index AI > 0.5 and enhanced vegetation index EVI > 0.6). In contrast, a positive relationship between ET and LCC presents over arid and sparse lands (AI < 0.2 and EVI < 0.2), accompanied by enhanced ET (>0.2 mmday−1). Analysis shows that under increased LCC conditions, substantial reduction in radiation availability results in the decline in both plant transpiration (ETveg) and soil evaporation (ETsoil) over humid dense vegetation, dominating the overall negative response. The increase of near‐surface relative humidity over arid sparse vegetation may indicate a stronger moisture availability, which promotes ETsoil and partly offsets the negative effect of radiation reduction. The change of vegetation water content, indicated by a satellite microwave vegetation index, is important for ETveg over arid mountainous lands with cloud cover. These findings contribute new insights to the understanding of vegetation‐atmosphere interactions.

Study on the Regeneration Probability of Understory Coniferous Saplings in the Liangshui Nature Reserve Based on Four Modeling Techniques

Forests are one of the most important natural resources for humans, and understanding the regeneration probability of undergrowth in forests is very important for future forest spatial structure and forest management. In addition, the regeneration of understory saplings is a key process in the restoration of forest ecosystems. By studying the probability of sapling regeneration in forests, we can understand the impact of different stand factors and environmental factors on sapling regeneration. This could help provide a scientific basis for the restoration and protection of forest ecosystems. The Liangshui Nature Reserve of Yichun City, Heilongjiang Province, is a coniferous and broadleaved mixed forest. In this study, we assess the regeneration probability of coniferous saplings (CRP) in natural forests in 665 temporary plots in the Liangshui Nature Reserve. Using Sentinel-1 and Sentinel-2 images provided by the European Space Agency, as well as digital elevation model (DEM) data, we calculated the vegetation index, microwave vegetation index (RVI S1), VV, VH, texture features, slope, and DEM and combined them with field survey data to construct a logistic regression (LR) model, geographically weighted logistic regression (GWLR) model, random forest (RF) model, and multilayer perceptron (MLP) model to predict and analyze the CRP value of each pixel in the study area. The accuracy of the models was evaluated with the average values of the area under the ROC curve (AUC), kappa coefficient (KAPPA), root mean square error (RMSE), and mean absolute error (MAE) verified by five-fold cross-validation. The results showed that the RF model had the highest accuracy. The variable factor with the greatest impact on CRP was the DEM. The construction of the GWLR model considered more spatial factors and had a lower residual Moran index value. The four models had higher CRP prediction results in the low-latitude and low-longitude regions of the study area, and in the high-latitude and high-longitude regions of the study area, most pixels had a CRP value of 0 (i.e., no coniferous sapling regeneration occurred).

Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements

Although satellite-based light use efficiency (LUE) model is widely used to estimate gross primary production (GPP) of terrestrial ecosystems, microwave observations have not been integrated into LUE models. This study developed a new LUE model coupled with a passive microwave vegetation index (Emissivity Difference Vegetation Index, EDVI) for daily GPP estimation. Normalized EDVI (nEDVI), an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation (FPAR). EDVI-based evaporative fraction (EDVI-EF) under all sky was used to indicate synoptic-scale water stress on LUE. 8-year in-situ measurements from seven flux tower sites (four forests, two grasslands and one croplands) of ChinaFLUX network were used to evaluate the model. We found that nEDVI-based FPAR better captured the short-term variations in daily in-situ GPP (GPPobs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPPobs at all sites showed that EDVI-based GPP (GPPEDVI) generated an overall small bias of −0.47 gC m−2 day−1 (−8.1%) and good Taylor score (S) of 0.86 at the daily scale. Better accuracy of GPPEDVI was found at forests sites with R2 of 0.43 to 0.73, bias of-5.29% to 3.03% and S of 0.58 to 0.78, respectively. At a tropical forest site with most frequent cloud cover, the model also well captured the variation in GPPobs from clear sky to cloudy sky (R2 of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPPEDVI was found to be comparable with global satellite optic MOD17 GPP (GPPMOD17) and EC-LUE GPP (GPPECLUE) from 8-day to yearly scales across the sites. In particular, GPPEDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPPMOD17 and GPPECLUE were overestimated, suggesting that there could be less saturation for microwave-based LUE model over dense vegetation. Although all three satellite LUE models severely underestimated GPP of crop, GPPEDVI generated lower bias (−29.8%) than GPPMOD17 (−66.8%) and GPPECLUE (−59.5%). Overall, this study is the first attempt toward the integration of microwave-derived variables into LUE model for daily GPP estimation. The microwave-based LUE model has a potential of mapping spatiotemporally continues daily GPP under various clouds.

A three-source satellite algorithm for retrieving all-sky evapotranspiration rate using combined optical and microwave vegetation index at twenty AsiaFlux sites

Evapotranspiration (ET) composed of transpiration from vegetation (ETveg) and evaporation from soil (ETsoil) and interception water (ETint) is a crucial process for driving global water and energy cycle. In this study, a three-source satellite ET algorithm is developed for estimating instantaneous ET rate (ETcal) under clear and non-rainy cloudy sky conditions and is validated at 20 AsiaFlux sites of multiple vegetation types (evergreen and deciduous forests, croplands and grasslands) in East Asia. The ET algorithm is completely independent from ground-based measurements and thus has the potential to be used on regional scale. All-sky surface net radiation flux is derived from satellite products. Satellite optical Normalized Difference Vegetation Index (NDVI) is used to partition the surface available energy into vegetated area and bare soil area. Satellite microwave Emissivity Difference Vegetation Index (EDVI), an indicator of vegetation water content, is utilized to estimate canopy resistance and transpiration from vegetation. Evaporation components from canopy interception water and soil surface are estimated based on Priestley-Taylor method. The estimated evaporation fraction (EF) for all sites can produce a small absolute bias of less than 0.03 (or 8.1%) with a good determination coefficient (R2) larger than 0.42 when validated against the corrected in-situ measured EF on instantaneous and long-term mean scales. Instantaneous ETcal under all sky conditions are found to have a R2 of 0.61, a bias of 16.5 Wm-2 (13.7%) and a root mean square error (RMSE) of 63.6 Wm-2 (52.5%) over all sites, respectively. The performances of this algorithm are better achieved on 16-day (R2 = 0.66, RMSE = 44.1 Wm-2) and monthly (R2 = 0.70, RMSE = 39.1 Wm-2) scales. More importantly, EDVI-based ET method is able to produce ETcal with stable and good accuracy from clear sky to overcast sky (bias < 22 Wm-2 or 18%, RMSE < 77 Wm-2 or 59%). The daily ET and its components (ETveg, ETsoil and ETint) derived from our algorithm are further compared with those from Global Land Evaporation Amsterdam Model (GLEAM) ET products. EDVI-based ET (EDVI-ET) generally shows equal performances with GLEAM-ET when using in-situ ET as reference. EDVI-ETveg shows a fairly good correlation (R2 = 0.21) and an underestimation of -0.32 mm/day compared with GLEAM-ETveg. EDVI-ETint shows a significant correlation (R2 = 0.30) with GLEAM-ETveg after excluding three outlier tropical forest sites. Meanwhile, EDVI-ETsoil shows a fairly good positive correlation (R2 = 0.15) and an overestimation of 0.23 mm/day compared with GLEAM-ETsoil. The results indicate that our method has great potential for regional ET estimation under various cloud conditions when combined with satellite optical and microwave remote sensing.

Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling

Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( MVI B P ) and (ii) time invariant ( MVI B T ), based on model simulations at the L band. Results show that the MVI B T has a stronger sensitivity to vegetation properties compared with MVI B P . MVI B T is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that MVI B T has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.

THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

Abstract. This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

EVALUATION OF MICROWAVE VEGETTION INDEX (MVI) IN MULTI-FREQUENCY AND MULTI-ANGLE

Abstract. Microwave portion of the electromagnetic spectrum are effective for observations of vegetation not only to leaf but also woody parts of vegetation. Specifically, microwave emissivity varies strongly with surface roughness, polarization, look–angle and water content. Microwave Vegetation Index (MVI) is one of the microwave indexes that are based on zero-order model. The zero order models applicable when the scattering contribution with in the vegetation is negligible. In this paper MVI in different frequencies (Base on WCOM project) and different angles (Base on SMOS data) are calculated by Matrix Doubling model to take in to account multi-scattering effects within the vegetation. Then because MVI is depends on vegetation information we tried to analysis its behaviour in different densities of corn canopy by comparing to vegetation optical depth. The result shows linear relationship with height correlation between MVI and effective optical depth. So it can be a useful index in vegetation study for future satellite mission as WCOM.

Correction: Yao, P. et al. Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopted the Microwave Vegetation Index. Remote Sens. 2017, 9, 35

After publication of the research paper [1], the authors wish to make the following correction to this paper. In the fourth line from the bottom in abstract, due to a typing error, “RMSE = 0.84 m3/m3” should be replaced with “RMSE = 0.084 m3/m3”.[...]

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass.

Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010–June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m3/m3 and Bias = −0.0005 m3/m3, particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003–2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.084 m3/m3 and Bias = −0.002 m3/m3. We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM).

Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN

Land Surface Temperature (LST) with high spatio-temporal resolution is in demand for hydrology, climate change, ecology, urban climate and environmental studies, etc. Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatio-temporal resolution LST under cloudy conditions during daytime and nighttime without employing in-situ LST measurements. To achieve this, Artificial Neural Networks (ANNs) based models are employed for different land cover classes, utilizing Microwave Polarization Difference Index (MPDI) at finer resolution with ancillary data. MPDI was derived using resampled (from 0.25° to 1km) brightness temperatures (Tb) at 36.5GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology is tested over Cauvery basin in India and the performance of the model is quantitatively evaluated through performance measures such as correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86)K to 4.34(4.00)K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. During nighttime, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70)K to 2.43(2.12)K and NSE from 0.43(0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with air temperature (Ta) and indicate that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes.

Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation

Vegetation optical depth (VOD) and effective vegetation optical depth (EVOD) are key factors for estimating soil moisture and vegetation parameters. Microwave vegetation indices (MVIs, including A and B parameters) have been recently developed for short-vegetation covered surfaces. The MVIs parameter B (MVIs_B) is mainly related to vegetation conditions, which makes it provide a potential way of EVOD retrieval. A theoretical expression deriving EVOD was deduced using MVIs_B from WindSat data. Global patterns of EVOD were analyzed subsequently. It has been shown that EVOD retrieved from MVIs performed a consistent global pattern and seasonal variation with normalized difference vegetation index. Time-series data from the Central Tibetan Plateau Soil Moisture/Temperature Monitoring Network, which is grassland dominated, was selected for temporal analysis. It was found that the temporal EVOD from WindSat MVIs can capture the growth trend of vegetation. Comparisons between EVOD estimations from MVIs and a radiative transfer model were also performed over this network. It was found that EVOD from the two methods exhibited comparable values and similar trends. MVIs_B-derived EVOD can be obtained without any other auxiliary data and has great potential in land-surface parameter retrieval over short-vegetation covered areas.

The Development of Microwave Vegetation Indices from WindSat Data

The microwave vegetation indices (MVIs), including parameter A ( $\mathbf{MVIs}\_\mathbf{A}$ ) and parameter B ( $\mathbf{MVIs}\_\mathbf{B}$ ), have been recently developed based on advanced microwave scanning radiometer-earth observing system (AMSR-E) measurements. Coriolis/WindSat (WindSat) is a space-borne multifrequency polarimetric microwave radiometer with similar frequencies to the AMSR-E. Unlike the AMSR-E instrument configuration, the WindSat observation angles vary at the different frequencies and range from 49.9° to 55.3°. This variation results in significant uncertainty in deriving MVIs using WindSat data. In this study, we extended our algorithm for deriving MVIs from AMSR-E to that under WindSat sensor configuration by considering the measurements from different observation angles. We will present the theoretical basis for this new algorithm and then compare the two MVIs derived from these two sensors’ data at global and pixel scales, respectively. We found that the MVIs from WindSat data and AMSR-E data share similar global distribution patterns and temporal trends. The $\mathbf{MVIs}\_\mathbf{B}$ at the 6.8- and 10.7-GHz frequency pair [ $\mathbf{MVIs}\_\mathbf{B}$ (6.8,10.7)] from WindSat data is somewhat higher than that from AMSR-E data, whereas the $\mathbf{MVIs}\_\mathbf{B}$ at the 10.7- and 18.7-GHz frequency pair [ $\mathbf{MVIs}\_\mathbf{B}$ (10.7,18.7)] is higher for AMSR-E. The MVIs from both the WindSat data and the AMSR-E data can be affected by satellite overpass times. The extended MVIs are expected to provide possible complementary information and contribute to vegetation monitoring, vegetation water content, biomass and soil moisture retrieval, and global climate change research.

Prediction of Land Surface Temperature Under Cloudy Conditions Using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions.

Refinement of Microwave Vegetation Index Using Fourier Analysis for Monitoring Vegetation Dynamics

Knowledge of the vegetation phenological dynamics is crucial to the understanding of the Earth ecosystems and carbon cycles. A dual-frequency dual-polarization microwave vegetation index (MVI) has been developed recently for the Advanced Microwave Scanning Radiometer for the Earth Observing System; however, the noisy behavior of MVI limits its applications in the studies on terrestrial vegetation. In this letter, a method based on Fourier analysis is proposed to refine MVI. By excluding the high-frequency variations, the method helps to recover the trend of vegetation seasonal changes inherently contained in the raw MVI data series. Comparisons between the refined MVI and the normalized difference vegetation index (NDVI) data sets from years 2002 to 2005 show that the correlation between the refined MVI and NDVI is significantly increased for the three study sites. The refined MVI along with the optical vegetation indices provides complementary information on vegetation and can be served as a useful tool to monitor regional and global vegetation dynamics.

Retrieving crop leaf area index by combining optical and microwave vegetation indices: A feasibility analysis

Retrieving crop leaf area index by combining optical and microwave vegetation indices: A feasibility analysis

A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices

[1] Surface soil moisture is the key state variable in various hydrological processes. A physically based statistical methodology for surface soil moisture measurement in the Tibet Plateau was developed in this study. The approach was established based on theoretical relationships from the derivation of physical models. The methodology was calibrated using statistical analysis of a large data set obtained during a long-term experiment in Tibet. The procedure was conducted using multichannel brightness temperature observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E). The most interesting results of this study were that the newly developed microwave vegetation indices (MVIs) are a function of vegetation water content or vegetation transmissivity. The B parameter of MVIs decreased with increased vegetation water content but increased with increased vegetation transmissivity. This enabled the use of MVIs for the correction of vegetation effects in soil moisture inversion. The methodology was tested against several experimental data sets collected from Tibet and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage. The results also provided a complementary data set of soil moisture for hydrology and climatology studies in the Tibet Plateau.

The development of an algorithm to enhance and match the resolution of satellite measurements from AMSR-E

Microwave radiometers have many applications because of their penetration ability. However, two major problems remain that obstruct the development of microwave research. One factor that limits their commercial application is the relatively low resolution of microwave radiometers. The other is the non-uniform spatial resolution for each frequency of the radiometer. The resolution mismatch becomes a critical consideration when observations at two or more frequencies must be combined. In this paper, we have used the Backus-Gilbert method to solve these two problems, while AMSR-E is chosen as the research object. First, we derived the Backus-Gilbert method in detail. The simulated data were then used to decide the optimum parameters in the Backus-Gilbert method. To enhance the resolution, the Backus-Gilbert method has been applied to the AMSR-E data, which covered the Mexico Gulf and the Amazon River. After resolution was enhanced, detailed information was obtained and compared with visible high resolution data. To match the resolution, the AMSR-E data from the Oklahoma Little Washed were used to compute the Microwave Vegetation Index (MVI), which was developed by J. C. Shi. Compared to the original MVIs, the information contained in the MVIs that were processed by the Backus-Gilbert method is more reliable.

Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model ( ω– τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.

Cover Microwave vegetation index A new long-term global data set for biospheric studies

Cover Microwave vegetation index A new long-term global data set for biospheric studies