Synthetic aperture radar and optical satellite data for estimating the biomass of corn

Hosseini, Mehdi; McNairn, Heather; Mitchell, Scott; Robertson, Laura Dingle; Davidson, Andrew; Homayouni, Saeid

doi:10.1016/j.jag.2019.101933

Cited by 33 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The benefit of SAR data exploitation for vegetation studies involving forests (Frison et al, 2018;Sr. et al, 2018), crops (Veloso et al, 2017;Kumar et al, 2018;Hosseini et al, 2019;Ouaadi et al, 2020) or grasslands (Tamm et al, 2016;Fauvel et al, 2020)) is highlighted by numerous studies. Nevertheless, the extraction of interpretable descriptors is not straightforward.…”

Section: Introductionmentioning

confidence: 99%

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

Garioud

Valero

Giordano

et al. 2021

Remote Sensing of Environment

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

Garioud

Valero

Giordano

et al. 2021

Remote Sensing of Environment

View full text Add to dashboard Cite

“…Several studies have used semi-empirical modeling to characterize the contribution of these soil and vegetation parameters to SAR backscatter, with promising results [8][9][10][11][12]. The water cloud model (WCM) [13] is a semi-empirical radiative transfer model which has been used successfully to estimate crop biophysical parameters such as LAI and biomass [14][15][16][17][18][19]. For example, Bériaux et al (2011) used the WCM with C-Band satellites (ERS-1/2, ENVISAT/ASAR and RADARSAT-2) to estimate wheat LAI, later applying the WCM to corn LAI [11,16].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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 m2 m−2 and mean absolute error (MAE) of 0.51 m2 m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2 m−2 and MAE of 0.61 m2 m−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 m2 m−2 and MAE of 0.30 m2 m−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.

show abstract

“…Considerable researches have been conducted to estimate various crop parameters using satellite Earth observations, including RADARSAT-2 [1,17,[19][20][21], RapidEye [5,19,20,22,23], Sentinel-1 [7,17,[24][25][26][27][28], Sentinel-2 [7,25,[29][30][31][32], Landsat-5 Thematic Mapper (TM) [33,34], Landsat-7 Enhanced Thematic Mapper Plus (ETM+) [34][35][36], Landsat-8 Operational Land Imager (OLI) [7,17,31,32,[35][36][37], Worldview-2/3 [17,27,28,38,39], and MODIS [40,41].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

et al. 2021

Self Cite

View full text Add to dashboard Cite

Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

show abstract

Synthetic aperture radar and optical satellite data for estimating the biomass of corn

Cited by 33 publications

References 32 publications

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

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

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

Contact Info

Product

Resources

About