IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
ANALYSIS OF WITHIN-FIELD VARIABILITY OF CROP AND SOIL USING FIELD DATA 
AND SPECTRAL INFORMATION AS A PRE-CURSOR TO PRECISION CROP 
MANAGEMENT | 
S. S. Ray *^*, J. P. Singh!, S. Dutta* and Sushma Panigrahy* 
* Agricultural Resources Group, Space Applications Centre, Ahmedabad - 380015 
! Central Potato Research Station, Jalandhar - 144 003 
*Commission VII, Working Group VIL2 
KEYWORDS: Within-field variability, Remote sensing, Spectroradiometer, Precision crop management, Spectral indices 
ABSTRACT: 
This paper highlights a study undertaken, in the Central Potato Research Station Farm of Jalandhar, India, to analyse the crop and the soil 
variability in a well-managed agricultural farm and to correlate it with the spectral variability. The analysis of the variability occurring 
within the field was carried out by measuring soil and plant parameters through conventional methods as well as through spectral 
techniques using groundtruth spectroradiometer (350-1800 nm) and satellite data (merged data of IRS 1C LISS III and Pan). The soil 
parameters measured included 35 observations at fixed grid locations from a field of 4.43 ha for organic carbon, availability of nitrogen, 
' phosphorus and potassium. The plant parameters included 30 observations at fixed grid locations from filed of 4.15 ha for leaf area index 
(LAI), biomass at harvest and yield of wheat crop. Various narrow band indices for wheat crop such as NDVI, RVI and MSI (moisture 
stress index) were computed using spectral reflectance collected through spectroradiometers. For soil, four radiometric indices, related soil 
colour, were computed, which included BI (brightness index), SI (saturation index), HI (hue index), CI (coloration index) and redness index 
(RI). The analysis showed that the coefficient variations (CV) of wheat crop parameters were 19.22, 14.04, 12.99, 20.37, 2.63 and 11.87, 
for LAI at maturity, biomass at harvest, grain yield, RVI, NDVI and MSI, respectively. Similarly the CV of soil parameters were 22.98, 
30.16, 20.62, 30.08, 7.62, 17.43, 10.96 and 50.98 for organic carbon (%), available P (ppm), available K (ppm), BI, SI, HI, CI and RI, 
respectively. The correlation study showed that NDVI and RVI were significantly positively correlated with wheat yield, whereas MSI 
was significantly negatively correlated with the yield. Most of the soil colour related indices were negatively correlated with the soil 
chemical properties, especially available potassium. The variability maps, derived from both satellite data and field measurements, 
  
  
   
  
   
  
  
  
  
   
  
  
  
  
   
  
  
  
  
    
showed the direction of variability. 
1. INTRODUCTION 
The conventional agronomic practices follow a standard 
management option for a large area irrespective of the 
variability occurring within and among the field. By catering to 
this variability, called precision farming, one can improve the 
productivity or reduce the cost of production and also diminish 
the chance of environmental degradation caused by excess use 
of inputs (Pierce and Nowak, 1999). Thus mapping and 
analysis of within field variability is an essential input for 
precision crop management. 
The information for varibilty map can be obtained from soil 
tests for nutrient availability, yield monitors for crop yield, soil 
samples for organic matter content, information in soil maps, or 
ground conductivity meters for soil moisture (Mulla, 1997). 
Generally, the fields are manually sampled along a regular grid 
and the analysed results of the samples are interpolated using 
*Corresponding Author (Email: shibendu_ray@hotmail.com) 
geostatistical techniques. These techniques are time consuming, 
labour intensive and in many cases destructive in Especially, for 
agricultural situation in India, with small field size and low - 
income of farmers this kind of methodology is not feasible. 
Various workers (Hanson et al. 1995, Taylor et al, 1997, 
Moran et al, 1997)) have shown the advantages of using remote 
sensing technology to obtain spatially and temporally variable 
information for precision farming. In an earlier work, Ray et al. 
(2001) have shown the usefulness of IRS merged data in 
mapping the variability. 
However, the field sizes being small in Indian condition, there is 
limited capability, with respect to spatial resolution, of existing 
satellite based remote sensing data to map a wide range of 
variability within the field. Also there is need to establish the 
relationship between spectrally derived parameters and the filed 
parameters. 
     
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