Estimating the Leaf Area Index of Agricultural Crops us- ing multi-temporal dual-polarimetric TerraSAR-X Data: A case study in North-Eastern Germany
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[1] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[2] F. Ulaby,et al. Vegetation modeled as a water cloud , 1978 .
[3] C. Willmott. Some Comments on the Evaluation of Model Performance , 1982 .
[4] Fawwaz T. Ulaby,et al. Relating the microwave backscattering coefficient to leaf area index , 1984 .
[5] John A. Richards,et al. Radar backscatter modelling of forests: a review of current trends , 1990 .
[6] F. Baret,et al. Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .
[7] S. T. Gower,et al. Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations , 1991 .
[8] G. Guyot,et al. Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer , 1993 .
[9] T. Carlson,et al. On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .
[10] Ranga B. Myneni,et al. Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..
[11] Yoshio Inoue,et al. Ku- and C-band SAR for discriminating agricultural crop and soil conditions , 1998, IEEE Trans. Geosci. Remote. Sens..
[12] Jean-Pierre Wigneron,et al. A simple approach to monitor crop biomass from C-band radar data , 1999 .
[13] P. Thenkabail,et al. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .
[14] Julian D. Olden,et al. Torturing data for the sake of generality: How valid are our regression models? , 2000 .
[15] Seiho Uratsuka,et al. Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables , 2002 .
[16] R. J. Brown,et al. Providing crop information using RADARSAT-1 and satellite optical imagery , 2002 .
[17] R. Harris,et al. Extracting biophysical parameters from remotely sensed radar data: a review of the water cloud model , 2003 .
[18] Ray Harris,et al. Constructing a water-use model for input to the water cloud backscatter model , 2003 .
[19] Shusen Wang,et al. Remote sensing of grassland–shrubland vegetation water content in the shortwave domain , 2006 .
[20] D. Singh,et al. Scatterometer performance with polarization discrimination ratio approach to retrieve crop soybean parameter at X‐band , 2006 .
[21] Laura Dente,et al. Integration of MERIS and ASAR Data for LAI Estimation of Wheat Fields , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.
[22] Hans-Georg Engler,et al. DEMMIN – a test site for the validation of Remote Sensing data products. General description and application during AgriSAR 2006. , 2007 .
[23] Juan M. Lopez-Sanchez,et al. Potentials of polarimetric SAR interferometry for agriculture monitoring , 2009 .
[24] Hui Lin,et al. The relationship between the leaf area index (LAI) of rice and the C‐band SAR vertical/horizontal (VV/HH) polarization ratio , 2009 .
[25] Jakob J. van Zyl,et al. A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[26] Hui Lin,et al. Monitoring Sugarcane Growth Using ENVISAT ASAR Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[27] Heather McNairn,et al. The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index , 2011 .
[28] Niko E. C. Verhoest,et al. On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters , 2011, IEEE Geoscience and Remote Sensing Letters.
[29] Matthew O. Jones,et al. Satellite passive microwave remote sensing for monitoring global land surface phenology , 2011 .
[30] Thomas J. Jackson,et al. Monitoring soybean growth using L-, C-, and X-band scatterometer data , 2013 .
[31] D. Mulla. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .
[32] Heather McNairn,et al. Evaluating the Cloude–Pottier and Freeman–Durden scattering decompositions for distinguishing between unharvested and post-harvest agricultural fields , 2013 .
[33] Simonetta Paloscia,et al. Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin , 2013 .
[34] Irena Hajnsek,et al. Soil Moisture Estimation Using Dual-Polarimetric Coherent (HH/VV) TerraSAR-X and TanDEM-X Data , 2013 .
[35] Irena Hajnsek,et al. Polarimetric Soil Moisture Retrieval at Short Wavelength , 2013 .
[36] Jiali Shang,et al. Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada , 2014, Remote. Sens..
[37] Cuizhen Wang,et al. Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice , 2014 .
[38] Xiaojing Bai,et al. A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data , 2014, Remote. Sens..
[39] Jiali Shang,et al. Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data , 2014, Remote. Sens..
[40] Cuizhen Wang,et al. Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice , 2014, Remote. Sens..
[41] Amine Merzouki,et al. Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data , 2015 .
[42] Heather McNairn,et al. International Journal of Applied Earth Observation and Geoinformation , 2014 .
[43] Manoj K. Arora,et al. Study of water cloud model vegetation descriptors in estimating soil moisture in Solani catchment , 2015 .
[44] José A. M. Demattê,et al. A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters , 2016, Remote. Sens..
[45] Jean-Pierre Wigneron,et al. Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices , 2016 .
[46] German Remote Sensing Data Center , .