Estimating Forest fAPAR from Multispectral Landsat-8 Data Using the Invertible Forest Reflectance Model INFORM

Abstract

Abstract:The estimation of the Fraction of Absorbed Photosynthetically Active Radiation in forests (forest fAPAR) from multi-spectral Landsat-8 data is investigated in this paper using a physically based radiative transfer model (Invertible Forest Reflectance Model, INFORM) combined with an inversion strategy based on artificial neural nets (ANN). To derive the forest fAPAR for the Dabie mountain test site in China in 30 m spatial resolution (size approximately 3000 km 2 ), a database of forest canopy spectral reflectances was simulated with INFORM taking into account structural variables such as leaf area index (LAI), crown coverage and stem density as well as leaf composition. To establish the relationship between forest fAPAR and the reflectance modeled by INFORM, a logarithmic relationship between LAI and fAPAR was used previously established using on-site field measurements. On this basis, predictive models between Landsat-8 reflectance and fAPAR were established using an artificial neural network. After calibrating INFORM for the test site, forty-two forest stands were used to validate the performance of the method. The results show that spectral signatures modeled by INFORM correspond reasonably well with the forest canopy reflectance spectra derived from Landsat data. Deviations increase with increasing angle between surface normal of the hilly terrain and OPEN ACCESS Remote Sens. 2015, 7 7426 sun incidence. The comparison of estimated and measured fAPAR (R 2 = 0.47, RMSE = 0.11) demonstrates that INFORM can be inverted using neural nets to provide acceptable estimates of forest fAPAR. The accuracy of the predictions increased significantly when excluding pixels located in very steep terrain. This demonstrates that the applied topographic correction was not sufficiently accurate and should be improved for making optimum use of radiative transfer models such as INFORM.

Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model

“…The model offers a compromise concerning the feasibility of inversion and exhaustive characterisation of the canopy structure (Schlerf and Atzberger, 2006). The capacity and suitability of INFORM for retrieving forest structural and biochemical variables such as fAPAR, LAI, leaf dry matter, leaf water content, specific leaf area, leaf and canopy nitrogen content has been demonstrated in both broadleaf and conifer stands with varying levels of success (Ali et al, 2016c;Atzberger, 2012, 2006;Wang et al, 2018;Yang et al, 2011;Yuan et al, 2015;Zhu et al, 2019). However, the model has not previously been used for retrieving leaf chlorophyll, in particular using high-resolution multispectral data.…”

“…Therefore, this study aimed to evaluate the performance of RapidEye and Sentinel-2 spectral data, through inversion of INFORM for retrieving foliar chlorophyll content in spruce stands. The INFORM model has been used successfully for retrieving other plant traits from hyperspectral and Landsat 8 data (Ali et al, 2016c;Atzberger, 2012, 2006;Wang et al, 2018;Yuan et al, 2015). However, to our knowledge, its potential to retrieve leaf chlorophyll content has not been investigated.…”

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

“…Radiative transfer (RT) modelling is also a particularly well-suited method for the derivation of quantitative information on the composition of forest canopies or on the retrieval of biophysical forest parameters, e.g., [28]. Especially the more recent generation of ray tracing models, which simulate the photon interactions within the vegetation layer, can give detailed insight into the distribution of all plant parts, regardless of their visibility from the direction of observation.…”

Study of the Remote Sensing Model of FAPAR over Rugged Terrains

“…To reduce the impact of terrain on the retrieved values of the effective LAI, a topographic radiation correction for imagery was conducted before effective LAI retrieval. The topographic radiation correction here aggravates the complexity of the FAPAR calculation process, and cannot eliminate terrain effects perfectly [21]. In further research, the effective LAI can be retrieved directly using the bidirectional reflectance distribution function model in rugged terrains [24,25]; thus, the FAPAR can be calculated directly using the FAPAR-PR model.…”