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

SUMMARY Modern agriculture is based on the control of in-field variability, which is determined by the interactions of numerous factors such as soil, climate and crop. For this reason, the use of remote sensing is becoming increasingly important, thanks to the technological development of satellites able to supply information with high spatial resolution and revisit frequency. Despite the large number of studies on the use of remote sensing for crop monitoring, very few have addressed the problem of spatial variability at field scale or the early prediction of crop yield and grain quality. The aim of the current research was to assess the potential use of high resolution satellite imagery for monitoring durum wheat growth and development, addressing forecast grain yield and protein content, through vegetation indices at two stages of crop development. To best represent the natural variability of agricultural production, the study was conducted in wheat fields managed by local farmers. As regards dry weight, leaf area index and nitrogen (N) content, the possibility of describing the crop state is evident at stem elongation, while at anthesis this potential is completely lost. However, satellites seem to be unable to estimate the N concentration. Aboveground biomass accumulated from emergence to stem elongation is strictly related to the final yield, while it has been confirmed that the crop parameters observed at anthesis are less informative, despite approaching harvesting time.

[1]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[2]  G. Fitzgerald,et al.  Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI) , 2010 .

[3]  Deli Chen,et al.  Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments , 2011 .

[4]  S. Altenbach New insights into the effects of high temperature, drought and post-anthesis fertilizer on wheat grain development , 2012 .

[5]  Hervé Nicolas,et al.  Using remote sensing to determine of the date of a fungicide application on winter wheat , 2004 .

[6]  J. Peñuelas,et al.  Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice , 2008 .

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

[8]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[9]  José Luis Araus,et al.  Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat , 2002 .

[10]  S. Orlandini,et al.  The influence of climate on durum wheat quality in Tuscany, Central Italy , 2011, International journal of biometeorology.

[11]  J. Zadoks A decimal code for the growth stages of cereals , 1974 .

[12]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[13]  N. Zhang,et al.  Precision agriculture—a worldwide overview , 2002 .

[14]  L. Schrader,et al.  Exploitation of physiological and genetic variability to enhance crop productivity , 1985 .

[15]  Felix Rembold,et al.  Analysis of GAC NDVI Data for Cropland Identification and Yield Forecasting in Mediterranean African Countries , 2001 .

[16]  Rick L. Lawrence,et al.  Wheat yield estimates using multi-temporal NDVI satellite imagery , 2002 .

[17]  Influence of climate on durum wheat production and use of remote sensing and weather data to predict quality and quantity of harvests , 2011 .

[18]  Wenjiang Huang,et al.  Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images , 2006 .

[19]  Shusen Wang,et al.  Crop yield forecasting on the Canadian Prairies using MODIS NDVI data , 2011 .

[20]  A. Skjelvåg,et al.  Effect of temperature variation during grain filling on wheat gluten resistance , 2011 .

[21]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[22]  David B. Lobell,et al.  Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties , 2003 .

[23]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[24]  E. Hunt,et al.  Early season remote sensing of wheat nitrogen status using a green scanning laser , 2011 .

[25]  Nitrogen remobilization and post-anthesis nitrogen uptake in relation to elevated grain protein concentration in durum wheat , 2011 .

[26]  Georg Bareth,et al.  Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages , 2010, Precision Agriculture.

[27]  J. Clarke,et al.  Nitrogen remobilization and post-anthesis nitrogen uptake in relation to elevated grain protein concentration in durum wheat , 2011, Canadian Journal of Plant Science.

[28]  Victor O. Sadras,et al.  Temporal prediction of nitrogen status in wheat under the influence of water deficiency using spectral and thermal information. , 2005 .

[29]  D. Cammarano,et al.  Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment , 2012 .

[30]  Daniel Rodriguez,et al.  Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts , 2006 .

[31]  A. Castrignanò,et al.  Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: A multivariate geostatistical approach , 2012 .

[32]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[33]  J. V. Stafford,et al.  Providing operational nitrogen recommendations to farmers using satellite imagery. , 2005 .

[34]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[35]  Ammatzia Peled,et al.  Geographical model for precise agriculture monitoring with real-time remote sensing , 2009 .

[36]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[37]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[38]  A. Thomsen,et al.  Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression , 2002, The Journal of Agricultural Science.

[39]  J. V. Stafford,et al.  Precision Agriculture '05 , 2005 .

[40]  Troy Jensen,et al.  Relating satellite imagery with grain protein content , 2003 .

[41]  Christopher Baraloto,et al.  Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana , 2010, Annals of Forest Science.

[42]  J. Araus,et al.  Spectral vegetation indices as nondestructive tools for determining durum wheat yield. , 2000 .

[43]  Vuolo Francesco,et al.  Retrieval of biophysical vegetation products from RapidEye imagery , 2010 .

[44]  Sylvain Villette,et al.  Combining spatial and spectral information to improve crop/weed discrimination algorithms , 2012, Other Conferences.

[45]  Francesco Montemurro,et al.  Precision nitrogen management of wheat. A review , 2012, Agronomy for Sustainable Development.

[46]  D. Roberts,et al.  Sensitivity of Narrow-Band and Broad-Band Indices for Assessing Nitrogen Availability and Water Stress in an Annual Crop , 2008 .

[47]  L. Alonso,et al.  A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems , 2013 .