Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

Asilo, Sonia; Nelson, Andrew; Bie, C.A.J.M. de; Skidmore, A.K.; Maunahan, A.; Quilang, Eduardo Jimmy P.

doi:10.3390/rs11121462

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…These results were consistent with those from Jiao et al, 2009, andHosseini et al, 2015, demonstrating that the WCM could estimate LAI over a broader range (LAI of 0.07-3.57 m 2 m −2 for soybeans; 0.04-4.79 m 2 m −2 for corn) [11,21]. More recently, co-polarized X-Band data have also been used to estimate LAI over a study region in the Philippines [19]. They exploited X-HH to estimate LAI of rice during vegetative and reproductive growth stages, with a correlation coefficient of 0.71 reported.…”

Section: Introductionsupporting

confidence: 88%

“…Based on these criteria, data from three sites (Canada, Germany and Poland) were used for calibration and validation. For each crop type and LAI range, half of the in situ measurements were randomly selected for calibration data, a larger percentage than that used in the previous studies [17,19]. The remainder of the sample points were reserved for independent validation.…”

Section: Selection Of Calibration and Validation Pointsmentioning

confidence: 99%

“…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%

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

et al. 2021

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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.

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Section: Introductionsupporting

confidence: 88%

Section: Selection Of Calibration and Validation Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Numerous studies have shown that the appearance of panicles can not only change the light distribution in a crop canopy, but also considerably affect canopy spectral reflectance [25][26][27][28]. Inoue et al [29] and Asilo et al [30] also reported that panicles influence the relationship between X-band and LAI in rice. Therefore, the influence of panicles on canopy structure is a key factor that affects LAI estimation accuracy during the late growth stages of crops.…”

Section: Introductionmentioning

confidence: 99%

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

Zhang

et al. 2019

Remote Sensing

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The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

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“…Bindlish and Barros, 2001). The vegetation backscatter from (agricultural) canopies has been investigated to gain insights on the phenology (Bakar et al, 1997;Vreugdenhil et al, 2018;Asilo et al, 2019), presence of water stress (McNairn et al, 2002;van Emmerik et al, 2017), and productivity (Li et al, 2003;Setiyono et al, 2019). Vegetation water content (VWC) has been estimated from the vegetation backscatter (Notarnicola and Posa, 2007;Srivastava et al, 2015) and radar vegetation index (RVI, Kim and van Zyl, 2004;Kim et al, 2011Kim et al, , 2013Huang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Investigating soil moisture dynamics for improved applications of radar satellites in agricultural water management

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Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

Cited by 14 publications

References 25 publications

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

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

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

Investigating soil moisture dynamics for improved applications of radar satellites in agricultural water management

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