Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

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A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Remote Sensing ◽

10.3390/rs13071348 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1348

Author(s):

Mehdi Hosseini ◽

Heather McNairn ◽

Scott Mitchell ◽

Laura Dingle Robertson ◽

Andrew Davidson ◽

...

Keyword(s):

Support Vector Machine ◽

Soil Moisture ◽

Leaf Area Index ◽

Satellite Data ◽

Cloud Model ◽

Support Vector ◽

Area Index ◽

Svm Model ◽

Water Cloud

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

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Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

Remote Sensing ◽

10.3390/rs12010183 ◽

2020 ◽

Vol 12 (1) ◽

pp. 183 ◽

Cited By ~ 2

Author(s):

Chenyang Xu ◽

John J. Qu ◽

Xianjun Hao ◽

Di Wu

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Water Content ◽

Vegetation Index ◽

Surface Soil ◽

Cloud Model ◽

Incidence Angle ◽

Permafrost Region ◽

Surface Soil Moisture ◽

The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

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The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

Remote Sensing ◽

10.3390/rs12121979 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1979

Author(s):

Dandan Xu ◽

Deshuai An ◽

Xulin Guo

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Area Index ◽

Plant Area Index ◽

The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

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Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

Remote Sensing ◽

10.3390/rs13234729 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4729

Author(s):

Veena Shashikant ◽

Abdul Rashid Mohamed Shariff ◽

Aimrun Wayayok ◽

Md Rowshon Kamal ◽

Yang Ping Lee ◽

...

Keyword(s):

Soil Moisture ◽

Oil Palm ◽

Vegetation Index ◽

Cloud Model ◽

Data Retrieval ◽

Optical Data ◽

Backscatter Coefficient ◽

Area Index ◽

Soil Moisture Data ◽

Sar Data

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

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Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

Remote Sensing ◽

10.3390/rs11232842 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2842 ◽

Cited By ~ 5

Author(s):

Daniel Shamambo ◽

Bertrand Bonan ◽

Jean-Christophe Calvet ◽

Clément Albergel ◽

Sebastian Hahn

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Extreme Event ◽

Cloud Model ◽

Large Fraction ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

The Difference ◽

The Impact

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model.

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Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

10.5194/hess-2021-273 ◽

2021 ◽

Author(s):

Sara Modanesi ◽

Christian Massari ◽

Alexander Gruber ◽

Hans Lievens ◽

Angelica Tarpanelli ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Cloud Model ◽

Bayesian Optimization ◽

Irrigation Scheme ◽

Area Index ◽

Observation Operator ◽

The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

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Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

Remote Sensing ◽

10.3390/rs13204023 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4023

Author(s):

Veena Shashikant ◽

Abdul Rashid Mohamed Shariff ◽

Aimrun Wayayok ◽

Md Rowshon Kamal ◽

Yang Ping Lee ◽

...

Keyword(s):

Soil Moisture ◽

Oil Palm ◽

Cloud Model ◽

Water Area ◽

Moisture Determination ◽

Backscatter Coefficient ◽

Area Index ◽

Palm Trees ◽

Water Cloud ◽

High Yields

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.

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Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

Water ◽

10.3390/w13101333 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1333

Author(s):

Giuseppe Francesco Cesare Lama ◽

Mariano Crimaldi ◽

Vittorio Pasquino ◽

Roberta Padulano ◽

Giovanni Battista Chirico

Keyword(s):

Riparian Vegetation ◽

Vegetation Index ◽

Arundo Donax ◽

Water Bodies ◽

Giant Reed ◽

Multispectral Images ◽

Drainage Channel ◽

Area Index ◽

Riparian Plants ◽

The Impact

Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

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A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

Remote Sensing ◽

10.3390/rs13132442 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2442

Author(s):

Jichao Lv ◽

Rui Zhang ◽

Jinsheng Tu ◽

Mingjie Liao ◽

Jiatai Pang ◽

...

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Satellite Data ◽

Vegetation Index ◽

Soil Moisture Content ◽

Support Vector ◽

Observation Data ◽

Multipath Effect ◽

Mars Model ◽

The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030193 ◽

2021 ◽

Vol 10 (3) ◽

pp. 193

Author(s):

Zhaoqi Wang ◽

Xiang Liu ◽

Hao Wang ◽

Kai Zheng ◽

Honglin Li ◽

...

Keyword(s):

Vegetation Index ◽

Source Region ◽

Elevation Gradient ◽

Climate Variation ◽

Mean Annual Precipitation ◽

Near Infrared Reflectance ◽

Area Index ◽

Vegetation Greenness ◽

The Mean ◽

The Impact

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

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