Estimation Of Vegetation Water Content Research Articles

Estimating vegetation water content from Sentinel-1 C-band SAR data over savanna and grassland ecosystems

Studying vegetation water content (VWC) dynamics is essential for understanding plant growth, water and carbon cycles, and ecosystem stability. However, acquiring field-based VWC estimates, consistently through space and time, is challenging due to time and resource constraints. This study investigates the potential of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data for estimating VWC in natural ecosystems in central Brazil. We assessed (i) how well Sentinel-1 SAR data can capture variations in VWC over three different vegetation types (i.e. dry and waterlogged grasslands, and savannas) and (ii) how the studied vegetation types respond to seasonal dry periods in terms of water content. Field data from 82 plots, distributed across the three vegetation types and revisited in four different seasons, were used to calibrate and validate a model for VWC estimation. The calibrated model, with an R 2 of 0.52 and RMSE of 0.465 kg m−2, was then applied to Sentinel-1 SAR backscatter data to generate monthly VWC maps for grassland and savanna ecosystems at 30 m spatial resolution between April 2015 and September 2023. These maps, combined with rainfall and potential evapotranspiration data, provided insights into how the studied vegetation types respond to water shortage during the dry season at the community scale. More specifically, savannas showed to be better able to retain higher levels of water content during the dry season, probably due to a higher water holding capacity of the woody component together with its deep-root system ability to access deeper groundwater. This research demonstrates the potential of Sentinel-1 SAR data for monitoring VWC in natural ecosystems, allowing for future studies to assess ecosystems’ response to drought events and changes in their functioning, ultimately supporting land management decisions.

Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Satellite-based soil moisture (SM) retrieval within agricultural regions is challenging due to pronounced vegetation and land cover variations during the growing season. Vegetation water content (VWC)–used to describe vegetation opacity in both the Soil Moisture Active Passive (SMAP) Dual Channel Algorithm (DCA) and V-polarization Single Channel Algorithm (SCA-V)–is approximated using a climatological Normalized Difference Vegetation Index (NDVI) time series. To understand the impact of VWC algorithms on SMAP SM retrievals, we investigated optimal vegetation indices for VWC estimation, and further evaluated the impact of VWC uncertainty on DCA and SCA-V SM retrievals. Besides NDVI, we applied four dynamic vegetation indices (VIs) acquired from Sentinel-2–including two red-edge VIs (Modified Chlorophyll Absorption Ratio Index (MCARI) and Inverted Red-edge Chlorophyll Index (IRECI)) and two non-red-edge VIs (Normalized Difference Water Index (NDWI1 and NDWI2)) to estimate growing-season corn VWC and the impact of different empirical relationships on VWC estimates. We find that climatological NDVI from Moderate Resolution Imaging Spectroradiometer (MODIS) significantly underestimates VWC, and therefore underestimates SM over croplands in Northwest China during the growing season, whereas dynamic VIs can improve the accuracy of VWC and SM retrievals. With respect to the choice of VI for VWC estimation, MCARI is more accurate during the peak growing season compared to its counterpart non-red-edge VIs; however, it also overestimates VWC during the early growing stage. In addition, the influence of VWC uncertainty on SM accuracy is shown to be higher in SCA-V than in DCA. The results of this study provide insight into preferred approaches for the accurate estimation of VWC and associated improvement in passive microwave algorithms for SM retrieval.

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

Measuring land surface temperature, near-infrared and short-wave infrared reflectance for estimation of water availability in vegetation

The vegetation water status is a crucial variable for modelling of drought impact, vegetation productivity and water fluxes. Methods for spatial estimation of this variable still need to be improved. The integration of remotely sensed data of land surface temperature (LST) and water vegetation indices based on near-infrared (NIR) and short-wave infrared (SWIR) reflectance for estimation of vegetation water content and water available for evapotranspiration require more analysis. This study contains a detailed method and measurements of LST, NIR and SWIR reflectance of soybean, corn and barley taken in field campaigns in central Argentine Pampas and laboratory with a ST PRO Raytek (8–14 µm) and a spectrometer SVC HR-1024i (0.35 and 2.5 µm). Also, relative water content of leaves was measured in laboratory during the dehydration process. This method and dataset could be also used for researching other wavelengths between 0.35 and 2.5 µm as indicator of water vegetation status (e.g. solar-induced chlorophyll fluorescence, photosynthesis).•Procedures useful to measure field spectra of vegetation are presented.•Not only the traditional method to measure leaves spectra in laboratory, but also in field were applied.•The method allows the integration of spectra and thermal data as a proxy of vegetation water status.

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

Obtaining high-precision, long-term sequences of vegetation water content (VWC) is of great significance for assessing surface vegetation growth, soil moisture, and fire risk. In recent years, the global navigation satellite system-interferometric reflection (GNSS-IR) has become a new type of remote sensing technology with low cost, all-weather capability, and a high temporal resolution. It has been widely used in the fields of snow depth, sea level, soil moisture content, and vegetation water content. The normalized microwave reflectance index (NMRI) based on GNSS-IR technology has been proven to be effective in monitoring changes in VWC. This paper considers the advantages and disadvantages of remote sensing technology and GNSS-IR technology in estimating VWC. A point-surface fusion method of GNSS-IR and MODIS data based on the GA–BP neural network is proposed to improve the accuracy of VWC estimation. The vegetation index products (NDVI, GPP, LAI) and the NMRI that unified the temporal and spatial resolution were used as the input and output data of the training model, and the GA–BP neural network was used for training and modeling. Finally, a spatially continuous NMRI product was generated. Taking a particular area of the United States as a research object, experiments show that (1) a neural network can realize the effective fusion of GNSS-IR and MODIS products. By comparing the GA–BP neural network, BP neural network, and multiple linear regression (MLR), the three models fusion effect. The results show that the GA–BP neural network has the best modeling effect, and the r and RMSE between the model estimation result and the reference value are 0.778 and 0.0332, respectively; this network is followed by the BP neural network, in which the r and RMSE are 0.746 and 0.0465, respectively. MLR has the poorest effect, with r and RMSE values of 0.500 and 0.0516, respectively. (2) The spatiotemporal variation in the 16 days/500 m resolution NMRI product obtained by GA–BP neural network fusion is consistent with that in the experimental area. Through the testing of GNSS stations that did not participate in the modeling, the r between the estimated value of the NMRI and the reference value is greater than 0.87, and the RMSE is less than 0.049. Therefore, the method proposed in this paper is optional and effective. The spatially continuous NMRI products obtained by fusion can reflect the changes in VWC in the experimental area more intuitively.

Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.

Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa

Vegetation water content (VWC) is of vital significance to many applications in agriculture, such as crop yield estimation and precision irrigation. However, previous studies mainly estimate VWC at either high spatial resolution or temporal resolution due to the limitation of space-borne observation systems. In this paper, we target on monitoring daily plant VWC as well as canopy VWC at 30 m high spatial resolution with the fusion of optical measurements from the Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) in an agricultural area. The study area locates in the central of Iowa, the United States (USA), the study period ranges from August 1 to August 17, 2016. Landsat 8 OLI observations are with 16-day revisit cycle and 30-m spatial resolution, MODIS daily surface reflectance product used in this study has 500-m spatial resolution. Six Landsat OLI images and thirty MODIS images were used to estimate daily remotely sensed reflectance data at 30m first. Then, the fused satellite measurements were used to calculate Normalized Difference Water Index (NDWI). Ground-based plant VWC and canopy VWC measurements were collected during the Soil Moisture Active Passive Validation Experiment (SMAPVEX16) to calibrate and validate the model for plant VMC and canopy VWC estimation separately with the NDWI. The results of validation with in-situ measurements showed an R2 of 0.44 for the corn plant VWC, an R2 of 0.66 for the corn canopy VWC; and an R2 of 0.78 for the soybean plant VWC, an R2 of 0.85 for the soybean canopy VWC, respectively.

Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize

BackgroundVegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013–2015 in North Plain China.ResultsResults showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CIgreen), red edge normalized ratio (NRred edge), and red-edge chlorophyll index (CIred edge) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT.ConclusionsCompared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Vegetation water content (VWC) is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms. Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i.e., “agricultural crops,” based on climatological databases. Determining crop type–specific equations for water content could lead to improvements in the soil moisture retrievals. Data to address this issue are lacking, and as a part of the calibration and validation program for NASA’s Soil Moisture Active Passive (SMAP) Mission, field experiments are conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP soil moisture products for these intensive agricultural regions. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of 2016 that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation. In Iowa, regression equations for corn and soybean are developed that provided VWC with root mean square error (RMSE) values of 1.37 and 1.10 kg / m2, respectively. In Manitoba, corn and soybean equations are developed with RMSE values of 0.55 and 0.25 kg / m2. Additional crop-specific equations are developed for winter wheat (RMSE of 0.07 kg / m2), canola (RMSE of 0.90 kg / m2), oats (RMSE of 0.74 kg / m2), and black beans (RMSE of 0.31 kg / m2). Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains.

Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model

In this study, is presented a new methodology for retrieving surface soil moisture (SSM) under conditions of partial vegetation cover based on the synergy between Sentinel-1 Synthetic Aperture Radar (SAR) and Landsat Operational Land Image (OLI) data. To remove the effect of vegetation on SSM retrieval, the Landsat OLI spectral index is applied to build a model for the vegetation water content estimation. The model is substituted into the original water-cloud model, and thus a modified water-cloud model with a spectral index is built. Additionally, an SSM estimation model is developed based on the modified water-cloud model. The technique was tested at two experimental sites in the UK and Spain on which reference data of SSM are acquired operationally by ground observational networks. In overall, the key findings of our study were: (1) For a vegetation-covered surface, the normalized difference water index (NDWI) obtained from the 1.57–1.65 μm band reflectance data was the most suitable for removing the effects of vegetation cover on soil water content estimation; (2) Compared to the Sentinel-1 VH polarization, the backscattering coefficient at VV polarization was more suitable for soil moisture retrieval and obtained a higher accuracy; (3) The developed model could be used to retrieve SSM under vegetation cover with a high accuracy that indicates the correlation coefficient (R) between the estimated and measured soil moisture was 0.911 and that the root mean square error (RMSE) was 0.053 cm3/cm3; (4) The model can be used to retrieve regional SSM with a high spatial and temporal resolution. Our methodology for deriving SSM offers a number of advantages for many practical applications and research alike and its use by the wider community remains to be seen.

Estimation of vegetation water content at leaf and canopy level using dual-wavelength commercial terrestrial laser scanners.

Vegetation water content, quantified as the leaf equivalent water thickness (EWT), can serve as an indicator of vegetation stress. The intensity data recorded by terrestrial laser scanning (TLS) instruments, operating at shortwave infrared wavelengths, can be used to estimate the three-dimensional distribution of EWT, after a full and rigorous calibration for the range and incidence angle effects. However, TLS instruments do not record the incidence angles automatically, making calibration challenging. In this study, intensity data from two commercially available TLS instruments (Leica P40, 1550 nm shortwave infrared wavelength, and Leica P20, 808 nm near-infrared wavelength) were combined in a normalized difference index (NDI). The NDI was found to minimize the incidence angle effects with no need for further calibration. A dry-down experiment was conducted using deciduous and conifer canopies. The NDI was found to be highly correlated to EWT at leaf level (R2 of 0.91 and 0.74) and at canopy level (R2 of 0.89 and 0.74) for the deciduous and conifer canopies, respectively. Three-dimensional distributions of EWT at canopy level were generated, which revealed some vertical heterogeneity.

Incorporation of Stem Water Content into Vegetation Optical Depth for Crops and Woodlands

Estimation of vegetation water content (VWC) by optical remote sensing improves soil moisture retrievals from passive microwave radiometry. For a variety of vegetation types, the largest unknown for predicting VWC is stem water content, which is assumed to be allometrically related to the water content of the plant canopy. For maize and soybean, measured stem water contents were highly correlated to canopy water contents, so VWC was calculated directly from the normalized difference infrared index (NDII), which contrasts scattering at near-infrared wavelengths with absorption of shortwave infrared wavelengths by liquid water. Woodland tree height is linearly related to woody stem volume, and hence to stem water content. We hypothesized that tree height is positively correlated with canopy water content, and thus with NDII. Airborne color-infrared imagery was acquired at two study areas in a mixed agricultural and woodland landscape, and photogrammetric structure-from-motion point clouds were derived to estimate tree heights. However, estimated tree heights were only weakly correlated with measured data acquired for validation. NDII was calculated from Landsat 8 Operational Line Imager (30-m pixel) and WorldView-3 (7.5 m pixel); but contrary to the hypothesis, NDII was not correlated with woodland tree height. Lastly, the interaction of woodland and crops stem water contents on total VWC in a mixed landscape were simulated for 2 days, one in the early summer and one in the late summer. VWC for the region varied from 2.5 to 3.0 kg m−2, which was just under a threshold for accuracy for soil moisture retrievals using Coriolis WindSat. Woodland tree height should be included as an ancillary data set along with land cover classification for soil moisture retrieval algorithms.

A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves

The variability of leaf water content has considerable significance for plant–environment interactions, ecosystem functioning and crop growth. This paper describes a methodology used to create spectral similarity water indices (SWIs) to accurately retrieve leaf water content based on the similarity between the leaf reflectance spectra and the specific water absorption spectrum. The abilities of six common distance metrics to capture spectral features were tested using two in situ datasets (LOPEX93 and PANAMA) and one simulation dataset (PROSPECT). These three datasets were also used to determine the most appropriate spectral intervals and to verify the performance of SWIs against six frequently used spectral indices that were specifically designed to estimate vegetation water content. Our results demonstrate that the Spectral Angle Cosine (SAC) is the most effective metric to capture useful spectral information pertinent to the equivalent water thickness (EWT), and three spectral intervals (970–1150, 1330–1350 and 1584–1760nm) are suitable for the retrieval of leaf water content. The SWIs were then created based on the SAC distances in these three spectral intervals respectively. The results demonstrate that SWISAC indices are better indicators of leaf water content and more tolerant to species differences than the six spectral indices, including the Shortwave Angle Normalized Index (SANI), Shortwave Angle Slope Index (SASI), Moisture Stress Index (MSI), Normalized Difference Infrared Index (NDII), Normalized Difference Water Index (NDWI) and Maximum Difference Water Index (MDWI). In addition, the most accurate estimates of EWT were achieved from a single SAC distance with nRMSE of 4.08% (R¯2=0.98), 3.63% (R¯2=0.95) and 8.11% (R¯2=0.80) for PROSPECT, LOPEX93 and PANAMA, respectively. Models that combine two SAC distances from the near-infrared and shortwave infrared regions produce an even better overall performance. Spectral similarity metrics may be a new effective tool to capture useful spectral information pertinent to leaf biochemical components, not only EWT but also other components such as chlorophyll and nitrogen content and they have potential to be adapted to canopy level observations.

Estimating Vegetation Water Content of Corn and Soybean Using Different Polarization Ratios Based on L- and S-Band Radar Data

Vegetation water content (VWC) is an important parameter of agriculture and forestry. In this letter, specific polarization ratios were evaluated for estimating VWC of corn and soybean. Backscattering coefficients ( $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}, \mathrm {\boldsymbol{\sigma }}_{\mathbf {vv}}$ , $\mathrm {\boldsymbol{\sigma }}_{\mathbf {vh}}$ and $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hv}}$ ), polarization ratios ( $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {vv}}$ , $\mathrm {\boldsymbol{\sigma }}_{\mathbf {vv}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {vh}} $ , and $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {hv}}$ ), and the radar vegetation index derived from L-band (1.26 GHz) and S-band (3.15 GHz) radar data of the passive and active L- and S-band sensor (PALS) in Soil Moisture Experiments 2002 were implemented to develop various linear relationship models with field VWC measurements for corn and soybean, respectively. L-band $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {vv}}$ was found to be most correlated with corn VWC (R = 0.81), while for soybean, L-band $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {hv}}$ was the best parameter to estimate VWC with an R of 0.90. Based upon these analyses, prediction equations for the estimation of corn and soybean VWC using the polarization ratios were developed. Results indicated that L-band $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {vv}}$ was able to estimate corn VWC with a root mean square error (RMSE) of 0.53 kg/m2 and a mean absolute relative error (MARE) of 11.48%. As for soybean, L-band $\mathrm {\boldsymbol{\sigma }}_{\mathbf {hh}}\mathrm {\mathbf {/}}\mathrm {\boldsymbol{\sigma }}_{\mathbf {hv}}$ was capable of estimating soybean VWC with an RMSE of 0.12 kg/m2 and an MARE of 13.33%. The main reason for these differences is most likely due to the disparate structure features and VWC distribution of corn and soybean. This letter proposes an effective method for acquiring VWC in regional areas, and it is also considered to be a powerful supplement for the current methods based on optical remotely sensed data.

Estimation of Vegetation Water Content From the Radar Vegetation Index at L-Band

Information on vegetation water content (VWC) is important in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with $r=0.49, 0.62, \mbox{and} 0.65$ and root-mean-square error $(\mbox{RMSE})=0.42, 0.37, \mbox{and} 0.36$ kg/m2, the relationship for pasture was poor, with $r=-0.06,-0.14, \mbox{and} -0.002$ and $\mbox{RMSE}=0.15,0.15, \mbox{and} 0.15$ , kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study ( $r=0.98$ and $\mbox{RMSE}=0.12 \mbox{kg/m}^2$ ) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced.

Theretrieval of Vegetation Water Content based on ASTER Images in Middle of Heihe River Basin

Vegetation Water Content(VWC)is one of the main limiting factors of affecting growth of plants,which is an important parameter to character vegetation physiological status and morphology.Quantitative estimation of VWC by utilizing remote sensing technology has important significances for agricultural drought monitoring,crop yield estimation and scientific research.In this paper,six periods ASTER images and ground-based measurements of VWC at 11 sampling sites are used to develop the empirical inversion model of VWC,which are obtained during the Heihe Watershed Allied Telemetry Experimental Research(Hi-WATER)in 2012.The four types of vegetation indexes(NDVI,RVI,SAVI,and MSAVI)are adopted in this study.We analyze the relationship between different vegetation indexes and the measured VWC,then develop and validate these VI-based empirical models for VWC retrieval.Results show that the correlation is very high between the measured VWC and the selected four vegetation indexes(R20.846).It indicates that we can retrieve VWC with high accuracy by using the four types of vegetation indexes.Among these vegetation indexes,the MSAVI-based retrieval model achieves the highest accuracy and the root mean square error(RMSE)is only 0.794kg/m2.The study also prove that the developed VWC retrieval model with MSAVI is reliable and an effective way for monitoring spatial variation of regional VWC.

Optical Sensing of Vegetation Water Content: A Synthesis Study

Vegetation water content (VWC) plays an important role in parameterizing the vegetation influence on microwave soil moisture retrieval. During the past decade, relationships have been developed between VWC and vegetation indices from satellite optical sensors, in order to create large-scale VWC maps based on these relationships. Among existing vegetation indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) have been most frequently used for estimating VWC. This work compiles and inter-compares a number of equations developed for VWC derivation from NDVI and NDWI using satellite data and ground samples collected from field campaigns carried out in the United States, Australia, and China. Four vegetation types are considered: corn, cereal grains, legumes, and grassland. While existing equations are reassessed against the entire compiled data sets, new equations are also developed based on the entire data sets. Comparing with existing equations, results show superiorities for the new equations based on statistical analysis against the entire data set. ${\mathrm {NDWI}_{1640}}$ and NDVI are found to be the preferred indices for VWC estimation based on the availability and the error statistics of the compiled data sets. It is recommended that the new equations can be applied in the future global remote sensing application for VWC map retrieval.

Errors associated with estimating vegetation water content from radar for use in passive microwave brightness temperature algorithms

The Soil Moisture Active Passive Validation Experiment 2012 was conducted as a pre-launch validation campaign for the Soil Moisture Active Passive mission over 6 weeks in June and July 2012. During this campaign, the Passive Active L-Band System (PALS) was flown at a low altitude, providing radar and radiometer measurements that were contained within a single agricultural field. The campaign domain consisted of 55 agricultural fields, where soil moisture was measured coincident to the PALS flight times and measurements of vegetation volumetric water content (VWC) and leaf area index (LAI) were measured weekly. The low-altitude flights allowed for the comparison between measured VWC and LAI for 11 fields to radar parameters derived from the radar backscatter. Only the correlation between the HV backscatter and the soybean VWC was considered strong (|r| > 0.7). All other correlations between the radar parameters and the VWC (or LAI) were moderate (0.3 < |r| < 0.7) or weak (|r|< 0.3). The established relationships between radar parameters and VWC were used in a forward radiation transfer model to estimate H-pol brightness temperature. It was found that the RMSE between the brightness temperatures estimated using the measured VWC was lowest when using the relationship between VWC and LAI (3.9 K for soybeans, 6.8 K for spring wheat, and 9.3 K when all crop data are combined). Despite a lower correlation, the RMSE associated with using the radar vegetation index relationship with VWC was less than when HV was used (7.9 K) for soybeans, which would result in an error in soil moisture estimation of just over 4%. The RMSEs for all other VWC and radar parameter relationships were greater than 10 K.

The new method monitoring crop water content based on NIR-Red spectrum feature space

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

Normalized Microwave Reflection Index: Validation of Vegetation Water Content Estimates From Montana Grasslands

The Normalized Microwave Reflection Index (NMRI) measures the intensity of GPS reflections, which is affected by vegetation within $\sim{\bf 100}\nbsp\hbox{\bf m}$ of GPS antennas. In a companion paper, the theoretical basis for NMRI and how it is derived from data archived at geodetic GPS installations are described. NMRI is calculated by normalizing the standard GPS metric ${\bf MP}_{\bf 1}{\bf rms}$ on a site-by-site basis to minimize terrain effects. Here, we validate NMRI as a metric for estimating vegetation water content (VWC) and evaluate the normalization procedure. In situ measurements of plant height, biomass, and VWC were taken on a biweekly basis during 2012 at four grassland sites in Montana. These measurements were compared to time series of ${\bf MP}_{\bf 1}{\bf rms}$ , NMRI, and Normalized Difference Vegetation Index (NDVI) from each site. At each site, a significant linear relationship exists between ${\bf MP}_{\bf 1}{\bf rms}$ and VWC. However, this relationship is not consistent across sites. Once normalized, a linear relationship exists between NMRI and VWC ( ${\bf r}^{\bf 2} = {\bf 0.71}$ ) that is consistent across the four sites. This suggests that VWC could be predicted from NMRI at sites without in situ observations, as long as vegetation and climate are similar. There is no clear relationship between NMRI and either vegetation height or biomass. The importance of normalization is shown using data from eight additional sites. After normalization, a strong positive correlation is apparent between NMRI and NDVI across all grassland GPS sites in Montana.