Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data

Hosseini, Mehdi; McNairn, Heather; Merzouki, A.; Pacheco, Anna

doi:10.1016/j.rse.2015.09.002

Cited by 125 publications

(69 citation statements)

References 37 publications

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“…LAI estimation errors (RMSE in m 2 m −2 ) range from 0.63 to 0.97 [36]. Estimation errors for biomass range from 58.438 to 78.834 g m −2 [37]. These results were confirmed in this study.…”

Section: Discussionsupporting

confidence: 88%

“…In comparison, Yanghao (2015) obtained best case biomass estimations, with an R 2 value of 0.79 and RMSE of about 91.7 g m −2 in rape using a linear regression model [25]. Hosseini et al (2015) also predicted crop biomass using the WCM on Radarsat-2, with a mean RMSE of 78.834 g m −2 ; however, the crop investigated was wheat [37]. Several studies also used polarimetric parameters, and with other inversion models obtained a stem height inversion RMSE of 10.37 cm, LAI inversion RMSE of 0.48 and biomass inversion RMSE of 220 g/m 2 [20].…”

Section: Discussionmentioning

confidence: 99%

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Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Zhang

Chen

et al. 2018

Remote Sensing

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Abstract:In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine the best model for stem height, LAI, and biomass inversion. Of these five models, quadratic models had higher R 2 values than other models in most cases of growth parameter inversions, but when these results were related to physical scattering mechanisms, the inversion results produced overestimation in the performance of some parameters. By contrast, linear and logarithmic models, which had lower R 2 values than the quadratic models, had stable performance for growth parameter inversions, particularly in terms of their performance at each growth stage. The best biomass inversion performance was acquired by the volume component of a quadratic model, with an R 2 value of 0.854 and root mean square error (RMSE) of 109.93 g m −2 . The best LAI inversion was also acquired by a quadratic model, but used the radar vegetation index (Cloude), with an R 2 value of 0.8706 and RMSE of 0.56 m 2 m −2 . Stem height was acquired by scattering angle alpha (α) using a logarithmic model, with an R 2 of 0.926 value and RMSE of 11.09 cm. The performances of these models were also analysed for biomass estimation at the second growth stage (P2), third growth stage (P3), and fourth growth stage (P4). The results showed that the models built at the P3 stage had better substitutability with the models built during all of the growth stages. From the mapping results, we conclude that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m −2 .

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Zhang

Chen

et al. 2018

Remote Sensing

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“…For crop monitoring, however, the spatial resolution of those products tends to remain too coarse [62]. An alternative to cope with the coarse spatial resolution is to derive surface-soil moisture directly from the SAR data used for the LAI retrieval [10,63,64]. Finally, Bériaux et al [62] tackled the issues related to soil influence on the SAR signal in monitoring maize crop growth using the WCM.…”

Section: Discussionmentioning

confidence: 99%

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

et al. 2015

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Abstract:In order to monitor crop growth along the season with synthetic aperture radar (SAR) images, radiative transfer models were developed to retrieve key biophysical parameters, such as the Leaf Area Index (LAI). The semi-empirical water cloud model (WCM) can be used to estimate LAI values from SAR data and surface soil moisture information. Nevertheless, instability problems can occur during the model calibration, which subsequently reduce its transferability in both time and space. To avoid these ill-posed cases, three calibration methodologies are benchmarked in the present study. The accuracy of the retrieved LAI values for each methodology was analyzed, as well as the sensitivity of the signal to LAI for different soil moisture values. The sensitivity of the cross-polarization was highlighted especially for high LAI. The VV polarization was found sensitive for LAI values inferior to 2 m 2 /m 2 . Given the differential sensitivity of the C-band backscatter to maize canopies in each polarization, a Bayesian fusion of the LAI estimates in linear polarizations was developed. This fusion gives lower weights to estimates with a high uncertainty. This method systematically reduces the error and its associated variance. When considering all polarizations, the RMSE on LAI estimation decreased by 0.32 m 2 /m 2 , i.e., one fourth of the error value, as compared to the best estimation from a single polarization, and the associated uncertainty was reduced by a factor of two. Focusing on the two most sensitive polarizations to maize canopies (VV-HV), the error diminished by a third. This fusion framework shows thus a great potential to improve the accuracy and reliability of LAI retrieval of C-band quad-polarized data, as well as dual-polarized data, such as Sentinel-1.

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“…Soil moisture was 743 also retrieved (RSME of 0.065 cm3/cm3). In a slightly modified approach, Hosseini et al [136] 744 used multiple polarizations from RADARSAT-2 and an airborne L-band sensor to invert the it is evident that SAR can provide estimates on LAI to support the monitoring of crop condition.…”

mentioning

confidence: 99%

Radar Remote Sensing of Agricultural Canopies: A Review

Steele‐Dunne

McNairn

Monsiváis-Huertero

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data

Cited by 125 publications

References 37 publications

Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Radar Remote Sensing of Agricultural Canopies: A Review

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