Correlating the field water balance derived crop coefficient (Kc) and canopy reflectance-based NDVI for irrigated sugarcane

Studies of crop coefficients as a function of vegetation indices are reported less often for sugarcane than other crops because of the variation in crop evapotranspiration (ETc) and spectral response over the long growth period. In this study, the possibility of correlating crop coefficient (Kc) and ground based normalized difference vegetation index (NDVI) of a sugarcane crop was investigated based on 2 years field experiments conducted in 2015 and 2016 in semi-arid India. The Kc values for the full crop season were determined by the field water balance method and ground NDVI was estimated from spectral reflectance measurements using a field spectro-radiometer. The sugarcane Kc values for the tillering (development stage), grand growth (mid-season) and maturity stages (end season) were 0.70, 1.20 and 0.78, respectively. The results found that the Kc was 16.6% less than that suggested by FAO-56. The sugarcane NDVI ranged from 0.48 to 0.69 at the tillering stage and 0.69 to 0.93 in the grand growth stage. Unlike other crops, sugarcane NDVI at the maturity stage did not reduce from 0.85 even at harvest due to the continued production of fresh green leaves at the top of the plant. Regression equations were developed to estimate the seasonal distribution of Kc with NDVI as the dependent variables and ratio of days $$\left(\frac{t}{T}\right)$$ after planting (t) to the total crop period (T) as the independent variable. The relationship between crop Kc and NDVI was characterized with 2nd order polynomial regression but correlation was moderately strong (r = 0.75, n = 315). Stronger correlations between Kc and NDVI were obtained by splitting growth period into the growth phase (r = 0.98, n = 245) and decline phase (r  =  0.99, n  =  70). The estimated Kc will be helpful for correcting irrigation scheduling of sugarcane in semi-arid conditions. The Kc-NDVI relationships for sugarcane investigated in this study are important for potential real time irrigation water management in the future.

[1]  N. Inman-Bamber,et al.  Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements , 2003 .

[2]  Sylvain Moreau,et al.  Evaluation of the sap flow determined with a heat balance method to measure the transpiration of a sugarcane canopy , 2005 .

[3]  L. S. Pereira,et al.  Revised FAO Procedures for Calculating Evapotranspiration: Irrigation and Drainage Paper No. 56 with Testing in Idaho , 2001 .

[4]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

[5]  K. N. Tiwari,et al.  Estimation of Crop Coefficient and Evapotranspiration of Wheat (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS , 2010 .

[6]  G. Rana,et al.  Comparison of Different Ground-Based NDVI Measurement Methodologies to Evaluate Crop Biophysical Properties , 2010 .

[7]  M. Omary,et al.  Evaluation of sugarcane evapotranspiration from water table data in the everglades agricultural area , 1995 .

[8]  Dale F. Heermann,et al.  Development of reflectance-based crop coefficients for corn , 1990 .

[9]  S. Dingre,et al.  Enhancing nutrient use and sugarcane (Saccharum officinarum) productivity with reduced cost through drip fertigation in western Maharashtra. , 2014 .

[10]  V. Lebourgeois,et al.  Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI , 2010 .

[11]  Wim G.M. Bastiaanssen,et al.  Remote sensing for irrigated agriculture: examples from research and possible applications , 2000 .

[12]  Chris Funk,et al.  Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe , 2009 .

[13]  J. Knox,et al.  THE WATER RELATIONS AND IRRIGATION REQUIREMENTS OF SUGAR CANE (SACCHARUM OFFICINARUM): A REVIEW , 2011, Experimental Agriculture.

[14]  Stephan J. Maas,et al.  Combining remote sensing and modeling for estimating surface evaporation and biomass production , 1995 .

[15]  Complex Vegetation Survey in a Fruit Plantation by Spectral Instruments , 2014 .

[16]  M. Mkhabela,et al.  Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR , 2005 .

[17]  P. Pinter,et al.  Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index , 2003, Irrigation Science.

[18]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[19]  G. Donald,et al.  Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series , 2003 .

[20]  Benoît Duchemin,et al.  Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region , 2007 .

[21]  Karl Fred Huemmrich,et al.  High temporal resolution NDVI phenology from micrometeorological radiation sensors , 1999 .

[22]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[23]  J. Jones,et al.  Stability of crop coefficients under different climate and irrigation management practices , 1989, Irrigation Science.

[24]  C. A. Megguer,et al.  Sugar cane crop coefficient by the soil water balance method , 2015 .

[25]  S. G. Reynolds.  The gravimetric method of soil moisture determination Part I A study of equipment, and methodological problems , 1970 .

[26]  A. K. Joshi,et al.  Crop Evapotranspiration Estimation for Wheat (Triticum aestivum L.) Using Remote Sensing Data in Semi-Arid Region of Maharashtra , 2017, Journal of the Indian Society of Remote Sensing.

[27]  Garik Gutman,et al.  On the use of long‐term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer , 1999 .

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

[29]  R. Jackson,et al.  Interpreting vegetation indices , 1991 .

[30]  P. K. Bandyopadhyay,et al.  Actual evapotranspiration and crop coefficients of wheat (Triticum aestivum) under varying moisture levels of humid tropical canal command area , 2003 .

[31]  Vijay P. Singh,et al.  Water requirements and single and dual crop coefficients of sugarcane grown in a tropical region, Brazil , 2012 .

[32]  Ayse Kilic,et al.  Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index , 2013, Remote. Sens..

[33]  M. Jackson.  Soil Chemical Analysis , 2014 .

[34]  C. R. de Souza Filho,et al.  ASTER and Landsat ETM+ images applied to sugarcane yield forecast , 2006 .

[35]  J. Heilman,et al.  Evaluating the Crop Coefficient Using Spectral Reflectance1 , 1982 .

[36]  O. Zamora,et al.  Determination of the Water Requirement and Kc Values of Sugarcane at Different Crop Growth Stages by Lysimetric Method , 2014, Sugar Tech.

[37]  Rubens Augusto Camargo Lamparelli,et al.  Spectral variables, growth analysis and yield of sugarcane , 2005 .

[38]  W. Bausch,et al.  Crop Coefficients Derived from Reflected Canopy Radiation: A Concept , 1987 .

[39]  Tsuyoshi Akiyama,et al.  A Spectroradiometer for Field Use : VI. Radiometric estimation for chlorophyll index of rice canopy , 1986 .

[40]  Ayse Irmak,et al.  Estimation of Crop Coefficients Using Satellite Remote Sensing , 2009 .

[41]  A. Loukas,et al.  Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements , 2019, Water.

[42]  C. Justice,et al.  A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data , 2010 .

[43]  J. Ayars,et al.  Divergence of actual and reference evapotranspiration observations for irrigated sugarcane with windy tropical conditions , 2015 .

[44]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[45]  Christopher M. U. Neale,et al.  Development and validation of canopy reflectance-based crop coefficient for potato , 2007 .

[46]  N. G. Dastane.  Effective rainfall in irrigated agriculture , 1978 .

[47]  W. Bausch.  Remote sensing of crop coefficients for improving the irrigation scheduling of corn , 1995 .

[48]  Terry L. Kastens,et al.  Image masking for crop yield forecasting using AVHRR NDVI time series imagery , 2005 .