Improving canopy variables estimation from remote sensing data by exploiting 
ancillary information. Case study on sugar beet canopies

Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

Section: Exploiting Radiometric Informationsupporting

confidence: 93%

“…Instead we explore radiometric information and information on the variables in two successive steps ( Figure 1). The successive exploration of radiometric and a priori information has been successfully implemented by other authors too [37,49].…”

Section: Lookup Table Inversionmentioning

confidence: 99%

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

“…First, a series of Gaussian white noise, from 0-20% with a step 1%, was added to LUT simulate canopy reflectance to account for uncertainties attached to the models and measurements [55,56,73,77]. Second, multiple best solutions were used instead of a single best solution to calculate the estimated values [55,56,73,78]. Several studies have demonstrated that the single best parameter combination corresponding to the smallest distance calculated by a cost function (e.g., RMSE) does not necessarily lead to the best accuracies [43,78].…”

Section: Cost Function Algorithmmentioning

confidence: 99%

“…Second, multiple best solutions were used instead of a single best solution to calculate the estimated values [55,56,73,78]. Several studies have demonstrated that the single best parameter combination corresponding to the smallest distance calculated by a cost function (e.g., RMSE) does not necessarily lead to the best accuracies [43,78]. Different optimization numbers and the mutual effect on these regularization options have been systematically assessed in previous studies [55,56].…”

Section: Cost Function Algorithmmentioning

confidence: 99%

Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation

Qiu

Huang

et al. 2018

Yunnan pine shoot beetles (PSB), Tomicus yunnanensis and Tomicus minor have spread through southwestern China in the last five years, leading to millions of hectares of forest being damaged. Thus, there is an urgent need to develop an effective approach for accurate early warning and damage assessment of PSB outbreaks. Remote sensing is one of the most efficient methods for this purpose. Despite many studies existing on the mountain pine beetle (MPB), very little work has been undertaken on assessing PSB stress using remote sensing. The objective of this paper was to develop a spectral linear mixing model aided by radiative transfer (RT) and a new Yellow Index (YI) to simulate the reflectance of heterogeneous canopies containing damaged needles and quantitatively inverse their PSB stress. The YI, the fraction of dead needles, is a physically-explicit stress indicator that represents the plot shoots damage ratio (plot SDR). The major steps of this methods include: (1) LIBERTY2 was developed to simulate the reflectance of damaged needles using YI to linearly mix the green needle spectra with the dead needle spectra; (2) LIBERTY2 was coupled with the INFORM model to scale the needle spectra to the canopy scale; and (3) a look-up table (LUT) was created against Sentinel 2 (S2) imagery and inversed leaf chlorophyll content (LCC), green leaf area index (LAI) and plot SDR. The results show that (1) LIBERTY2 effectively simulated the reflectance spectral values on infested needles (mean relative error (MRE) = 1.4-18%), and the YI can indicate the degrees of needles damage; (2) the coupled LIBERTY2-INFORM model is suitable to estimate LAI (R 2 = 0.73, RMSE = 0.17 m m −2 , NRMSE = 11.41% and the index of agreement (IOA) = 0.92) and LCC (R 2 = 0.49, RMSE = 56.24 mg m −2 , NRMSE = 25.22% and IOA = 0.72), and is better than the original LIBERTY model (LAI: R 2 = 0.38, RMSE = 0.43 m m −2 , NRMSE = 28.85% and IOA = 0.68; LCC: R 2 = 0.34, RMSE = 76.44 mg m −2 , NRMSE = 34.23% and IOA = 0.57); and (3) the inversed YI is positively correlated with the measured plot SDR (R 2 = 0.40, RMSE = 0.15). We conclude that the LIBERTY2 model improved the reflectance simulation accuracy of both the needles and canopies, making it suitable for assessing PSB stress. The YI has the potential to assess PSB damage.

“…The inversion approach is promising because it is based on physical models [1,4,7]. Unfortunately, the ill-posed inversion problem [10] is prevalent in the model-based retrieval procedure and results in uncertainty in the retrieved LAI. The use of a priori information has been demonstrated to alleviate the ill-posed inversion problem [1,6,9].…”

Section: Introductionmentioning

confidence: 99%

An Extended Fourier Approach to Improve the Retrieved Leaf Area Index (LAI) in a Time Series from an Alpine Wetland

Quan

Wang

et al. 2014

An extended Fourier approach was presented to improve the retrieved leaf area index (LAIr) of herbaceous vegetation in a time series from an alpine wetland. The retrieval was performed from the Aqua MODIS 8-day composite surface reflectance product (MYD09Q1) from day of year (DOY) 97 to 297 using a look-up table (LUT) based inversion of a two-layer canopy reflectance model (ACRM). To reduce the uncertainty (the ACRM inversion is ill-posed), we used NDVI and NIR images to reduce the influence of the soil background and the priori information to constrain the range of sensitive ACRM parameters determined using the Sobol's method. Even so the uncertainty caused the LAIr versus time curve to oscillate. To further reduce the uncertainty, a Fourier model was fitted using the periodically LAIr results, obtaining LAIF. We note that the level of precision of the LAIF potentially may increase through removing singular points or decrease if the LAIr data were too noisy. To further improve the precision level of the LAIr, the Fourier model was extended by considering the LAIr uncertainty. The LAIr, the LAI simulated using the Fourier model, and the LAI simulated using the extended Fourier approach (LAIeF) were validated through comparisons with the field measured LAI. The R 2 values were 0.68, 0.67 and 0.72, the residual sums of squares (RSS) were 3.47, 3.42 and 3.15, and the root-mean-square errors (RMSE) were 0. 31, 0.30 and 0.29, respectively, on DOY 177 (early July 2011