Improved sugarcane LAI estimation using radiative transfer models with spatial constraint

Sugarcane crop, cultivated in subtropical and tropical regions, provides major sugar supply, and makes great contributions to human life and economic development. The sugarcane leaf area index (LAI) is highly related to the production. Our research aims at estimating sugarcane LAI through remote sensing observations. The physically-based radiative transfer model (RTM) inversion methods are widely applied in vegetation variable estimation. However, ill-posedness problem widely exists in the model inversion processes. Therefore, the study develops a spatial constraint method to regularize the RTM inversion, and LAI variable is estimated on object-level. The estimated object-level LAI variable is compared with the pixel-level, and validated using the SNAP biophysical processor. The results shows that the object-level LAI estimates show great performance.

[1]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[2]  R. Dickinson,et al.  The land surface climatology of the community land model coupled to the NCAR community climate model , 2002 .

[3]  Yingying Dong,et al.  Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[5]  Roshanak Darvishzadeh,et al.  Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  F. Baret,et al.  Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies , 2002 .

[7]  J. Chen,et al.  Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand , 1991 .

[8]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[9]  Alemu Gonsamo,et al.  Methodology comparison for slope correction in canopy leaf area index estimation using hemispherical photography , 2008 .

[10]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[11]  J. Hogg.  Quantitative remote sensing of land surfaces , 2004 .

[12]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[13]  P. Alton,et al.  The sensitivity of models of gross primary productivity to meteorological and leaf area forcing: A comparison between a Penman–Monteith ecophysiological approach and the MODIS Light-Use Efficiency algorithm , 2016 .

[14]  V. K. Dadhwal,et al.  Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data , 2004 .

[15]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[16]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[17]  Gregory P. Asner,et al.  Ecological Research Needs from Multiangle Remote Sensing Data , 1998 .

[18]  R. Dickinson,et al.  Future global warming from atmospheric trace gases , 1986, Nature.

[19]  Wolfram Mauser,et al.  Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study , 2018, Remote. Sens..

[20]  Albert Olioso,et al.  Conversion of 400-1100 nm vegetation albedo measurements into total shortwave broadband albedo using a canopy radiative transfer model , 2002 .

[21]  Elfatih M. Abdel-Rahman,et al.  The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature , 2008 .

[22]  Gui-zhou Wang,et al.  Hyper-spectral Estimation of Forest Leaf Area Index from Earth Observing 1 (EO-1) Hyperion Imagery Based on Empirical-Statistical Approach and Grey Relational Analysis , 2017, ICGSP.

[23]  Lionel Jarlan,et al.  Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa , 2008 .