Vegetation Water Content Research Articles

A point-surface fusion method for vegetation water content retrievalconsidering optimization of GNSS sites and modeling elements

The normalized microwave reflectance index (NMRI) based on global navigation satellite system (GNSS) interferometric reflectometry has been proven to reflect the changes in vegetation water content (VWC) effectively, but it is limited to point data. A spatially continuous NMRI product can be obtained by fusing NMRI data (point) and moderate-resolution imaging spectroradiometer (MODIS) data (surface). However, the time resolution of the existing results is limited to 16 d, and the research on the selection of modeling elements is not deep enough. In this paper, a point-surface fusion method for VWC retrieval considering the optimization of GNSS sites and elements is proposed. This method is aimed at using MODIS multi-band to synthesize vegetation indices with 8 d spatial-temporal resolution and establishing the initial element set by combining meteorological elements, followed by optimizing the GNSS sites and modeling elements for best modeling scheme, finally constructing the point-surface fusion method for VWC retrieval, and comprehensively evaluating the performance of the method. The results indicate that optimizing GNSS sites and modeling elements are particularly critical to improving modeling accuracy. Compared with other vegetation elements, normalized difference vegetation index (NDVI), gross primary productivity, and leaf area index are essential elements that affect the modeling effect. Among them, NDVI is the critical element. The NMRI products with 8 d/500 m resolution obtained by GA-BPNN can better reflect the change of VWC. Furthermore, the spatial performance of NMRI products is consistent with the fire forecast products and is suitable for drought and fire forecasts.

Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics

Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics.

Retrieval performances of different crop growth descriptors from full- and compact-polarimetric SAR decompositions

This study investigates the different performances between Compact-Polarimetric (CP) and Full-Polarimetric (FP) SAR to retrieve multiple crop growth parameters including Vegetation Water Content (VWC), Leaf Area Index (LAI), height, and dry biomass. The objective is to study at which conditions the CP SAR can obtain comparable retrieval accuracy as FP SAR for specific crop types and growth descriptors. Polarimetric decompositions were used to extract CP and FP explanatory variables to quantify crop growth status. Then, the sensitivities of CP and FP variables to different crop descriptors were analyzed, revealing higher sensitivity to height than LAI, VWC, and dry biomass. In order to reduce the information redundancy of sets of CP and FP SAR variables, Partial Least Square (PLS) approach was used to develop new orthogonal SAR parameters, while Step-Wise Regression (SWR) was used to determine the optimal SAR parameters, followed by retrievals of multiple crop growth descriptors using the developed estimators. Validated by the ground measurements, the retrieval performances are found to be highly dependent on polarimetry dimension (CP or FP), crop types, and targeted crop descriptors. With crop growth and enhanced depolarization effects, the CP SAR can capture the increasing volume power and decreasing surface power but at a lower rate than FP SAR. The m-δ and m-χ decompositions provide similar scattering components which differ from the Random Volume Over Ground (RVOG) decomposition. The third Stokes parameter g3 of the CP SAR data that indicates the strength of the circular polarization component is a common important parameter to infer the multiple crop parameters of interest. In agreement with the sensitivity analysis, better retrieval accuracies were obtained for height and dry biomass than VWC and LAI. For short vegetation such as soybean, full polarimetry is required to obtain the estimates of all four crop growth descriptors. On the contrary, for tall crop growth parameters, such as canola (height and dry biomass), corn (height, VWC, dry biomass), and wheat (LAI, VWC, dry biomass), the CP SAR features can provide comparable robust retrieval accuracy as the FP SAR features. This study deepens our insights into the CP SAR of the RADARSAT Constellation Mission (RCM) to timely monitor crop growth and health status.

Modelling Floodplain Vegetation Response to Groundwater Variability Using the ArcSWAT Hydrological Model, MODIS NDVI Data, and Machine Learning

This study modelled the relationships between vegetation response and available water below the soil surface using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001–2010) of monthly streamflow data. The average Nash-Sutcliffe efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Nineteen years (2002–2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet seasons. For example, the model generated high positive relationships (r = 0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the sub-basin against the groundwater flow (GW), soil water content (SWC), and combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r = 0.78, and 0.70) during the dry season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. The results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both the dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Response of crop water indices to soil wetness and vegetation water content

Hyperspectral sensors offer a unique opportunity to monitor crop stress through the multiple narrow bands from visible to short wave infrared wavelengths. It provides prominent water absorption bands in near and shortwave regions centered at 970 nm, 1200 nm, 1440 nm, 1664 nm, 1950 nm and 2250 nm, which are unavailable with conventional multispectral sensors. It gives bands which otherwise are not available with traditional multispectral sensors. However, the spectral regions of 1350–1480, 1800–2000 and 2350–2500 nm, has strong absorption of light due to water vapour and hence not useful. This study investigates the indices, namely the Water Band Index (WBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII) and Land Surface Water Index (LSWI), derived from the narrow bands of the AVIRIS-NG sensor for its response to varying degree of soil wetness and vegetation water content. The analyses showed that the WBI and NDWI could discern the saturated soil. At the same time, The NDII and LSWI displayed a better dynamic range because of the stronger absorption due to soil/vegetation liquid water content with reference to the base wavelength of 862 nm, which enables the depiction of the different degrees of soil wetness and vegetation water content. Since the water indices have varied responses to the same soil wetness and vegetation water content, a Water Indices vs Vegetation Index triangle space was used to derive the Water Condition Index (WCI). The analyses of the WCI’s indicate that the values of the water indices are normalized and have similar values under different soil wetness and vegetation water content. However, the WCIWBI had lower values among WCIs, and WCILSWI could demonstrate the variation in the soil wetness and vegetation water content owing to its better dynamic range.

1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset.

Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.

A geospatial approach in monitoring the variations on surface soil moisture and vegetation water content: a case study of Palakkad District, Kerala, India

A geospatial approach in monitoring the variations on surface soil moisture and vegetation water content: a case study of Palakkad District, Kerala, India

The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations

ESA's Soil Moisture Ocean Salinity (SMOS, since 2009) and NASA's Soil Moisture Active Passive (SMAP, since 2015) are the only two space-borne L-band radiometer missions currently in orbit, which provide key information on global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD). However, to date very few studies considered merging SMOS and SMAP for both SM and VOD retrievals simultaneously. This study presents the first global long-term and continuous SM and L-band VOD (L-VOD) dataset retrieved after merging the SMOS and SMAP brightness temperature (TB) observations, called the SMOS-SMAP-INRAE-BORDEAUX or SMOSMAP-IB product. We first developed a fitted SMOS TB dataset at a fixed incidence angle of 40°, and next applied a monthly linear rescaling of SMAP TB to SMOS TB for each polarization to produce a merged SMOS/SMAP TB (θ = 40°) dataset. The retrievals were then based on a mono-angular retrieval algorithm sharing a similar forward model with the SMOS-IC and the official SMOS retrieval algorithms. Results showed that the inter-calibration approach we used here could effectively remove the bias between the SMAP TB and fitted SMOS TB, with bias values reduced to 0.01 K (−0.02 K) compared to 3.45 K (1.65 K) for V (H) polarization before inter-calibration. The SMOSMAP-IB SM and L-VOD retrievals based on this new inter-calibrated SMOS/SMAP TB led to metrics that were equally good or better than those of other products (i.e., ESA CCI, SMOS-IC and the official SMAP products). When considering only long duration products, SMOSMAP-IB SM retrievals exhibited (i) the highest overall median R value of 0.72 with in-situ data from ISMN (International Soil Moisture Network) during 2013–2018, followed by SMOS-IC (R = 0.68) and CCI (R = 0.67), and (ii) the same smallest ubRMSD values as CCI (ubRMSD = 0.057 m3/m3vs 0.061 m3/m3 for SMOS-IC). L-VOD retrievals from SMOSMAP-IB were found to have comparable spatial and temporal skills to SMOS-IC. Spatially, they both correlated well with aboveground biomass (R = 0.87), and temporally, they both showed a good representation of the short vegetation NDVI signal and of the forest area loss in the Brazilian Amazon from 2011 to 2019. Developing SMOSMAP-IB is a step forward towards building a time-continuous L-band SM and VOD products in response to the possible failure of one of the SMOS or SMAP sensors in the future.

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery.

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 0.75, RMSE = 416 g m−2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model

X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.

Satellite‐Observed Vegetation Responses to Intraseasonal Precipitation Variability

AbstractThere is limited understanding of how vegetation responds to intraseasonal modes of rainfall variability despite their importance in many tropical regions. We use observations of precipitation and X‐band Vegetation Optical Depth (VOD) from 2000 to 2018 to assess the relationships between rainfall and vegetation water content on 25–60‐day timescales. Cross‐spectral analysis identifies coherent intraseasonal relationships between precipitation and VOD, mostly in arid or semi‐arid regions where vegetation is water‐limited. Changes in VOD tend to lag anomalous rainfall, usually within 7 days. The fastest vegetation response is observed in sparsely vegetated areas (median 3 days). Following strong intraseasonal wet events, anomalously high VOD can persist for 2 months after the rainfall peak. This vegetation response can feed back onto the atmosphere, so improved representation of vegetation responses in models has the potential to improve subseasonal‐to‐seasonal forecasts.

Investigating the spatio-temporal variation of vegetation water content in the western United States by blending GNSS-IR, AMSR-E, and AMSR2 observables using machine learning methods

Vegetation water content (VWC) is an important parameter when evaluating vegetation growth and climate change. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has provided us with an effective approach for the monitoring of VWC. The normalized microwave reflection index (NMRI) was defined to reflect the change of VWC, based on the fact that the amplitude of the direct and reflected GNSS interferometric signal is related to the variation of VWC. However, the sparse distribution of the ground-site-based observation stations restricts the application of NMRI. Fortunately, microwave vegetation optical depth (VOD) is a dimensionless parameter that describes the rate of attenuation of microwaves as they pass through the vegetation canopy, which can provide spatially continuous vegetation information. In this study, we integrate the GNSS-IR NMRI dataset with Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) VOD datasets by using three machine learning models: the generalized regression neural network (GRNN) model, the back-propagation neural network (BPNN) and multiple linear regression (MLR) models. This method can overcome the drawbacks of sparse distribution and spatial discontinuity of GNSS-IR site data, and obtains spatio-temporally continuous NMRI products that can more intuitively and accurately reflect the variability of VWC in the study area. The results showed that the GRNN model has the best retrieval accuracy, with model fitting and cross-validation R values of 0.76 and 0.74, and root-mean-square error (RMSE) values of 0.034 and 0.035, respectively. Based on the GRNN model, we obtained a spatio-temporally continuous NMRI product to further analyze the changes in VWC from 2007 to 2018. The results indicated that the effects of ENSO events with different intensities on vegetation and precipitation are different, and the response of VWC with different vegetation types to ENSO are also different. The anomalies of VWC show a lag phenomenon and is more stable, not as sensitive as precipitation to the ENSO events.

Deciduous Shrub Stem Water Content in Arctic Alaska

Vegetation water content is a critical aspect of ecosystem water balance and plant physiology, including how plants cope with drought. Deciduous trees store significant water for use in transpiration throughout the growing season in the boreal forest of Alaska. Water content of shrub stems, and their role in the water balance, remains unquantified for many woody species in boreal and arctic tundra ecosystems. We adapted time-domain reflectometry (TDR) to continuously measure shrub water content. We created calibration equations relating apparent dielectric constant to volumetric water content of shrub stems of felt-leaf willow (Salix alaxensis), diamond-leaf willow (S. pulchra) and dwarf birch (Betula nana). Our results show seasonal patterns of volumetric stem water content ranging from 40 to 70%. Stem water content prior to leaf-out was minimal (scaled by biomass, <2mm) compared to total snowmelt water, implying little water uptake of snowmelt water. Stem water content was small compared to other components of the hydrologic water budget (precipitation, runoff, evaporation, evapotranspiration, infiltration), indicating the importance of shrub transpiration and dependence of shrubs on available soil moisture for uptake. The technique and findings here are needed to quantify the role of deciduous shrub vegetation in the hydrologic cycle of ecosystems, particularly those where shrubs predominate.

Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ40o), slope (σ′) and curvature (σ″) over France. The Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM) is used to produce land surface variables (LSVs) that are input to the DNN. The DNN is trained to simulate σ40o, σ′ and σ″ from 2007 to 2016. The predictive skill of the DNN is evaluated during an independent validation period from 2017 to 2019. Normalized sensitivity coefficients (NSCs) are computed to study the sensitivity of ASCAT observables to changes in LSVs as a function of time and space. Model performance yields a near-zeros bias in σ40o and σ′. The domain-averaged values of ρ are 0.84 and 0.85 for σ40o and σ′, compared to 0.58 for σ″. The domain-averaged unbiased RMSE is 8.6% of the dynamic range for σ40o and 13% for σ′, with land cover having some impact on model performance. NSC results show that the DNN-based model could reproduce the physical response of ASCAT observables to changes in LSVs. Results indicated that σ40o is sensitive to surface soil moisture and LAI and that these sensitivities vary with time, and are highly dependent on land cover type. The σ′ was shown to be sensitive to LAI, but also to root zone soil moisture due to the dependence of vegetation water content on soil moisture. The DNN could potentially serve as an observation operator in data assimilation to constrain soil and vegetation water dynamics in LSMs.

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was 0.19 kgm−2 and 4.89 kgm−2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03 m3m−3 while it was approximately 0.06 m3m−3 and 0.09 m3m−3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than 0.12 m3m−3.

Improved Surface Soil Moisture Estimation Model in Semi-Arid Regions Using the Vegetation Red-Edge Band Sensitive to Plant Growth

Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu’an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R2) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm3/cm3, and the MAE was 0.023 cm3/cm3. Thus, this methodology has the potential for SSM estimation in vegetated areas.

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

AbstractAs an indispensable surface parameter, soil moisture is of great significance for analyzing global water cycle, establishing the hydrological model and assessing drought. Due to differences in the capability of establishing vegetation water content (VWC) models based on different vegetation indices, the influence of the different VWC models on retrieval accuracy has not been effectively assessed. Therefore, the suitability of vegetation parameters obtained from different VWC models established by different vegetation indices for accurate soil moisture retrieval requires further investigation. In this study, the different VWC models were established by different vegetation indices derived from Sentinel‐2 data and compared. Based on the Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 data, combined with microwave scattering models, the impact of different VWC models on the accuracy of soil moisture retrieval was investigated using the Look Up Table algorithm. The results indicate that, an exponential relationship between the measured VWC and different vegetation indices was obtained in the growth cycle of winter wheat, except for quadratic relationship of Modified Soil Adjusted Vegetation Index vegetation index. Compared with other VWC models, higher accuracy of VWC could be retrieved using NDWI1, and the short‐wave infrared band located at 1610 nm achieved higher accuracy than the band located at 2190 nm. With respect to all VWC models, the combination of near‐infrared and short‐wave infrared bands was more suitable for VWC retrieval. When the contribution of soil scattering exceeded that of vegetation scattering, the results of soil moisture retrieval with different VWC models established by vegetation indices exhibit no discernible difference, regardless of the differences that occurred in VWC itself.

Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations

Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m2 are 0.0395 cm3/cm3 and 0.0320 cm3/cm3, respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm3/cm3. CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.

Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification

ABSTRACT Hyperspectral LiDAR (HSL) has been utilised as an efficacious technique in objects classification and recognition based on its synchronously obtaining spectral and spatial information. However, the spectral information obtained by most of the developed HSL was in the visible and near-infrared range (VNIR, 400–1000 nm). Whereas spectral information in a longer wavelength range showed more useful for classification and detection, such as detecting vegetation water content. This paper proposed and tested an eight-channel HSL prototype covering visible to near-infrared and even short-wavelength infrared (VIS-NIR-SWIR, 450–1460 nm) based on a Super-continuum (SC) laser. System calibration, range precision and spectral profiles experiments were carried out to test the HSL prototype. The spectral profiles collected by the HSL are consistent with those acquired by the commercial spectrometer (SVC© HR-1024). And these spectral profiles of plants, textiles, camouflage objects, and ore samples collected by the HSL, especially those in the SWIR range, can effectively reveal the health status of the plants, and classify the manufacturing materials and ore species. The unique characteristics of spectral profiles covering VIS-NIR-SWIR promote the HSL shows the potential applications on objects classification related to vegetation, mining and surveillance.