A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements
Abstract
The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and FCOVER satellite products are derived moderate to coarse spatial resolution. The launch of Sentinel-2 in 2015 will provide data at decametric resolution with a high revisit frequency to allow quantifying the canopy functioning at the local to regional scales. The aim of this study is thus to evaluate the performances of a neural network based algorithm to derive LAI, FAPAR and FCOVER products at decametric spatial resolution and high temporal sampling. The algorithm is generic, i.e., it is applied without any knowledge of the landcover. A time series of high spatial resolution SPOT4_HRVIR (16 scenes) and Landsat 8 (18 scenes) images acquired in 2013 over the France southwestern site were used to generate the LAI, FAPAR and FCOVER products. For each sensor and each biophysical variable, a neural network was first trained over PROSPECT+SAIL radiative transfer model simulations of top of canopy reflectance data for green, red, near-infra red and short wave infra-red bands. Our results show a good spatial and temporal consistency between the variables derived from both sensors: almost half the pixels show an absolute difference between SPOT and LANDSAT estimates of lower that 0.5 unit for LAI, and 0.05 unit for FAPAR and FCOVER. Finally, downward-looking digital hemispherical cameras were completed over the main land cover types to validate the accuracy of the products. Results show that the derived products are strongly correlated with the field measurements (R2 0.79), corresponding to a RMSE = 0.49 for LAI, RMSE = 0.10 (RMSE = 0.12) for black-sky (white sky) FAPAR and RMSE = 0.15 for FCOVER. It is concluded that the proposed generic algorithm provides a good basis to monitor the seasonal variation of the vegetation biophysical variables for important crops at decametric resolution.
Keywords
FAPAR,FCOVER,LAI,Landsat 8,SPOT4_HRVIR,time series
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Author: Brantley, Steven T. and Zinnert, Julie C. and Young, Donald R. -
Changes in soil nutrients, vegetation structure and herbaceous biomass in response to grazing in a semi-arid savanna of Ethiopia
Author: Tessema, Z.K. and de Boer, W.F. and Baars, R.M.T. and Prins, H.H.T. -
Comparison of different vegetation indices for the remote assessment of green leaf area index of crops
Author: Viña, Andrés and Gitelson, Anatoly A. and Nguy-Robertson, Anthony L. and Peng, Yi -
Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy
Author: Duveiller, Grégory and Baret, Frédéric and Defourny, Pierre -
Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images
Author: Hein, L. and de Ridder, N. and Hiernaux, P. and Leemans, R. and de Wit, A. and Schaepman, M. -
Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study
Author: Maxwell, Susan K. -
Graph-Based Feature Selection for Object-Oriented Classification in VHR Airborne Imagery
Author: Chen, Xi and Fang, Tao and Huo, Hong and Li, Deren -
Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data
Author: Chen, Chi-Farn and Huang, Su-Wei and Son, Nguyen-Thanh and Chang, Li-Yu -
Mapping potato crop height and leaf area index through vegetation indices using remote sensing in Cyprus
Author: Papadavid, George and Hadjimitsis, Diofantos and Toulios, Leonidas and Michaelides, Silas -
Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process
Author: Rizvi, I. A. and Mohan, B. K. -
Real-time retrieval of Leaf Area Index from MODIS time series data
Author: Xiao, Zhiqiang and Liang, Shunlin and Wang, Jindi and Jiang, Bo and Li, Xijia -
Segmentation of Hyperspectral Images via Subtractive Clustering and Cluster Validation Using One-Class Support Vector Machines
Author: Bilgin, G. and Erturk, S. and Yildirim, T. -
Using active learning to adapt remote sensing image classifiers
Author: Tuia, D. and Pasolli, E. and Emery, W.J. -
Using existing agricultural infrastructure for restoration practices: Factors influencing successful establishment of Populus fremontii over Tamarix ramosissima
Author: Bunting, D.P. and Kurc, S.A. and Grabau, M.R. -
Monitoring Maize (Zea mays L.) Phenology with Remote Sensing
Author: Viña, Andrés and Gitelson, Anatoly A. and Rundquist, Donald C. and Keydan, Galina and Leavitt, Bryan and Schepers, James - fCover
- Non-parametric algorithms
- Sentinel satellites
- SPOT
- Vegetation indices