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

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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Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

Geocarto International ◽

10.1080/10106049.2019.1624984 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Vijay Pratap Yadav ◽

Rajendra Prasad ◽

Ruchi Bala

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Satellite Data ◽

Cloud Model ◽

Wheat Crop ◽

Area Index ◽

Water Cloud ◽

Optical Satellite Data ◽

Index Estimation

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

Remote Sensing ◽

10.3390/rs10091370 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1370 ◽

Cited By ~ 15

Author(s):

Junhua Li ◽

Shusen Wang

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Cloud Model ◽

Data Availability ◽

Agricultural Crop ◽

Sar Images ◽

Area Index ◽

Water Cloud ◽

The Impact

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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Using a Modified Water Cloud Model to Retrive Leaf Area Index (LAI) from Radarsat-2 SAR Data Over an Agriculture Area

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518645 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yichuan Ma ◽

Minfeng Xing ◽

Xiliang Ni ◽

Jinfei Wang ◽

Jiali Shang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Cloud Model ◽

Area Index ◽

Water Cloud ◽

Sar Data

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Multiyear independent validation of the water cloud model for retrieving maize leaf area index from SAR time series

International Journal of Remote Sensing ◽

10.1080/01431161.2013.772676 ◽

2013 ◽

Vol 34 (12) ◽

pp. 4156-4181 ◽

Cited By ~ 15

Author(s):

Emilie Beriaux ◽

Cozmin Lucau-Danila ◽

Eric Auquiere ◽

Pierre Defourny

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Cloud Model ◽

Area Index ◽

Maize Leaf ◽

Independent Validation ◽

Water Cloud

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Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Water ◽

10.3390/w13020135 ◽

2021 ◽

Vol 13 (2) ◽

pp. 135

Author(s):

Min Zhang ◽

Fengkai Lang ◽

Nanshan Zheng

Keyword(s):

Surface Roughness ◽

Soil Moisture ◽

Satellite Data ◽

Cloud Model ◽

Growth Cycle ◽

Wheat Growth ◽

Vegetation Fraction ◽

Effective Roughness ◽

Water Cloud ◽

Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

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Nitrogen Estimation for Wheat Using UAV-Based and Satellite Multispectral Imagery, Topographic Metrics, Leaf Area Index, Plant Height, Soil Moisture, and Machine Learning Methods

Nitrogen ◽

10.3390/nitrogen3010001 ◽

2021 ◽

Vol 3 (1) ◽

pp. 1-25

Author(s):

Jody Yu ◽

Jinfei Wang ◽

Brigitte Leblon ◽

Yang Song

Keyword(s):

Soil Moisture ◽

Leaf Area Index ◽

Leaf Area ◽

Plant Height ◽

Satellite Imagery ◽

N Fertilizer ◽

Support Vector ◽

Multispectral Imagery ◽

Area Index ◽

Canopy Nitrogen

To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m2) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R2 of 0.80 and an RMSE of 2.62 g/m2. The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R2 of 0.92 and an RMSE of 1.75 g/m2. The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer.

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SURFACE MOISTURE AND IRRIGATION MAPPING AT AGRICULTURAL FIELD SCALE USING THE SYNERGY SENTINEL-1/SENTINEL-2 DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-357-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 357-361

Author(s):

M. Zribi ◽

N. Baghdadi ◽

S. Bousbih ◽

M. El-Hajj ◽

Q. Gao

Keyword(s):

Soil Moisture ◽

Vegetation Cover ◽

Cloud Model ◽

Global Analysis ◽

Radar Signal ◽

Support Vector ◽

Agricultural Field ◽

Neural Network Approach ◽

Area Index ◽

Sentinel 2

Abstract. Soil moisture plays a key role in various processes at the soil-vegetation-atmosphere interface, such as evapotranspiration, infiltration and runoff. In this study, we firstly propose a global analysis of Sentinel-1 (S1) & Sentinel-2 (S2) data potential to retrieve soil moisture. Two approaches are tested. The first one is based on neural network approach; it uses Integral Equation Model (IEM) coupled to Water Cloud Model for vegetation cover backscattering simulation (El Hajj et al., 2017). The second approach considers change detection methodology. It estimates change of soil moisture with the driest and highest moisture levels, and also change of moisture between successive radar acquisitions (Gao et al., 2017). The proposed approaches are validated over three agricultural regions, south of France, Urgell (Spain) and Merguellil (Tunisia). In these different sites, important ground campaigns have been realized over reference fields with different types of measurements (soil moisture and roughness, Leaf area Index of vegetation cover). The retrieved accuracy of estimated volumetric soil moisture is about 5 vol.%. Based on estimated moisture products, two methodologies are considered to map irrigated areas (Gao et al., 2018, Bousbih et al., 2018). An analysis of different metrics (mean, variance, correlation length, etc.) of radar signal time series and surface parameters (moisture and NDVI) are tested. The proposed classification of irrigated areas is based on a combination of Support Vector Machine (SVM) and decision tree methodologies. For Urgell and Merguellil sites, a mapping of irrigated fields is proposed. The accuracy of mapping is higher than 75% for the two studied sites.

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ESTIMATION OF BIOPHYSICAL PARAMETERS OF WHEAT CROP THROUGH MODIFIED WATER CLOUD MODEL USING SATELLITE DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-239-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 239-244 ◽

Cited By ~ 4

Author(s):

V. P. Yadav ◽

R. Prasad ◽

R. Bala ◽

A. K. Vishwakarma ◽

S. A. Yadav

Keyword(s):

Satellite Data ◽

Cloud Model ◽

Parameters Estimation ◽

Optical Data ◽

Wheat Crop ◽

Landsat 8 ◽

Biophysical Parameters ◽

Area Index ◽

Vegetation Fraction ◽

Water Cloud

Abstract. A modified water cloud model (WCM) was used to estimate the biophysical parameters of wheat crop using Sentinel-1A and Landsat-8 satellite images. The approach of combining the potential of SAR and optical data provided a new technique for the estimation of biophysical parameters of wheat crop. The biophysical parameters estimation was done using non-linear least squares optimization technique by minimizing the cost function between the backscattering coefficients (&sigma;0) computed from the Sentinel-1A image and simulated by the modified WCM followed by look up table algorithm(LUT). The modified WCM integrates the full account of backscattering response on vegetation and bare soil by adding vegetation fraction. The modified WCM was found more sensitive than the original WCM because of incorporation of vegetation fraction (fveg) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R2=83.08% and RMSE=0.502m2/m2) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R2=76% and RMSE=0.560m2/m2) with the observed data in comparison to LAI estimation at VV polarization. The performance indices show that the modified WCM was found more accurate for the estimation of wheat crop parameters during the whole growth season in Varanasi district, India. Thus, the modified WCM shows significant potential for the accurate estimation of LAI and LWAI of wheat crop on incorporating both SAR and optical satellite data.

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