This study investigates the applicability of empirical and radiative transfer models to estimatewater content at leaf and landscape level. Themain goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborneMultispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather thanGWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfermodel to estimate leaf EWTandGWCand compared the resultswith those obtained bymeans of empirical models. The inversion of thismodel showed that leaf EWTcan be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECTmodel showed some difficulties in the simultaneous estimation of leaf EWTand drymatter content, which led to large errors in GWC estimation. At landscape levelwith MIVIS data,we tested the performance of different spectral indices to estimate canopywater per unit ground area (EWTcanopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWTat landscape level (PEWT). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWTat landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWTcanopy and PEWT was finally compared with that obtained from inversion of the PROSPECT+SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWTcanopy and an overestimation of leaf PEWT with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.
Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling
Giardino C;
2008
Abstract
This study investigates the applicability of empirical and radiative transfer models to estimatewater content at leaf and landscape level. Themain goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborneMultispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather thanGWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfermodel to estimate leaf EWTandGWCand compared the resultswith those obtained bymeans of empirical models. The inversion of thismodel showed that leaf EWTcan be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECTmodel showed some difficulties in the simultaneous estimation of leaf EWTand drymatter content, which led to large errors in GWC estimation. At landscape levelwith MIVIS data,we tested the performance of different spectral indices to estimate canopywater per unit ground area (EWTcanopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWTat landscape level (PEWT). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWTat landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWTcanopy and PEWT was finally compared with that obtained from inversion of the PROSPECT+SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWTcanopy and an overestimation of leaf PEWT with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
