Mapping within-field biomass variability: a remote sensing-based approach

Biomass production is a diagnosis tool for the evaluation of the effect of climate, crop genomic and management. The differences in biomass accumulation are necessary for the assessment of the fertilization necessities in the strategies for variable nitrogen doses. Remote sensing-based data provide a direct observation of the differences in canopy development across time and space and can be integrated into the physiological basis of crop growth models to provide estimates of biomass production at fine scales. The proposed approach was applied in a wheat field in Albacete, Spain and the results were compared with measurements of aboveground biomass and yield maps obtained by a combined-mounted grain yield monitor.

[1]  E. Kirby,et al.  Leaf Emergence, Tiller Growth, and Apical Development of Nitrogen-Dificient Spring Wheat , 1993 .

[2]  R. Fischer.  Irrigated spring wheat and timing and amount of nitrogen fertilizer. II. Physiology of grain yield response , 1993 .

[3]  Thomas S. Colvin,et al.  Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors , 2002, Precision Agriculture.

[4]  Armando Apan,et al.  Identifying the spatial variability of soil constraints using multi-year remote sensing , 2011 .

[5]  S. Orlandini,et al.  Durum wheat in-field monitoring and early-yield prediction: assessment of potential use of high resolution satellite imagery in a hilly area of Tuscany, Central Italy , 2013, The Journal of Agricultural Science.

[6]  D. Tennant,et al.  Water Use and Water Use Efficiency of Old and Modern Wheat Cultivars in a Mediterranean-type Environment , 1990 .

[7]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[8]  J. I. Ortiz-Monasterio,et al.  Satellite detection of earlier wheat sowing in India and implications for yield trends , 2013 .

[9]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[10]  J. Porter,et al.  A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought , 1998 .

[11]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[12]  D. Raes,et al.  AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description , 2009 .

[13]  Victor O. Sadras,et al.  Physiological basis of the response of harvest index to the fraction of water transpired after anthesis: A simple model to estimate harvest index for determinate species , 1991 .

[14]  J. Aase,et al.  Assessing winter wheat dry matter production via spectral reflectance measurements , 1981 .

[15]  David B. Lobell,et al.  Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields , 2014 .

[16]  Nithya Rajan,et al.  Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery , 2012 .