Remotely sensed innovative approach for the cumulative meteorological effects on cotton production

In this study an innovative approach for investigating the accumulated meteorological effects on cotton production during the growing season is presented. The quantification of the meteorological effects is based on the incorporation of the Bhalme and Mooley Drought Index (BMDI) methodology into the Vegetation Condition Index (VCI) extracted by NOAA/AVHRR data. The resulted Bhalme and Mooley Vegetation Condition Index (BMVCI) uses the same scale as the Z-Index of the Palmer Drought Severity Index (PDSI) for drought monitoring. The study area consists of the country of Greece. Eighteen years of NOAA/AVHRR data are examined and processed with the BMVCI to examine the unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The method is developed based on the first sixteen years time series data and validated utilizing the following two years. The resultant high correlation coefficient and the approximation of the production for the validated years refer to very favourable results and confirms the usefulness of this integrated methodological approach as an effective tool to assess cotton production in Greece.

[1]  P. A. Schultz,et al.  Global correlation of temperature, NDVI and precipitation , 1993 .

[2]  S. M. E. Groten,et al.  NDVI—crop monitoring and early yield assessment of Burkina Faso , 1993 .

[3]  Xiaoyang Zhang,et al.  On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake, PR China , 1998 .

[4]  S. Thiruvengadachari,et al.  An integrated PC environment for assessment of drought , 1993 .

[5]  Gary E. Johnson,et al.  The Role of NOAA Satellite Data in Drought Early Warning and Monitoring: Selected Case Studies , 1993 .

[6]  Nicolas R. Dalezios,et al.  Early cotton production assessment in Greece based on a combination of the drought Vegetation Condition Index (VCI) and the Bhalme and Mooley Drought Index (BMDI) , 2004 .

[7]  Nicolas R. Dalezios,et al.  Severity-duration-frequency analysis of droughts and wet periods in Greece , 2000 .

[8]  Mark Howden,et al.  Drought in Australia: Prediction, Monitoring, Management, and Policy , 1993 .

[9]  F. Kogan.  Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data , 1995 .

[10]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[11]  Felix Kogan,et al.  Development of global drought-watch system using NOAA/AVHRR data , 1993 .

[12]  Nicolas R. Dalezios,et al.  Remotely sensed estimation of annual cotton production under different environmental conditions in Central Greece , 2005 .

[13]  F. Kogan,et al.  Global Drought Watch from Space , 1997 .

[14]  A. Dijk,et al.  Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data , 1987 .

[15]  M. Hayes,et al.  Using NOAA AVHRR data to estimate maize production in the United States Corn Belt , 1996 .

[16]  F. Kogan.  Application of vegetation index and brightness temperature for drought detection , 1995 .

[17]  Maria Gruszczynska,et al.  Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices , 2002 .

[18]  John A. Dracup,et al.  The Quantification of Drought: An Evaluation of Drought Indices , 2002 .

[19]  Theodore S. Karacostas,et al.  Drought assessment for the potential of precipitation enhancement in northern Greece , 1991 .

[20]  F. Kogan.  Remote sensing of weather impacts on vegetation in non-homogeneous areas , 1990 .

[21]  Nicolas R. Dalezios,et al.  Cotton yield estimation based on NOAA/AVHRR produced NDVI , 2001 .

[22]  E. Olukayode Oladipo,et al.  A comparative performance analysis of three meteorological drought indices , 1985 .

[23]  F. Kogan,et al.  Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices , 2002 .

[24]  Compton J. Tucker,et al.  Satellite Remote Sensing of Drought Conditions , 1987, Planning for Drought.

[25]  J. Dracup,et al.  The Quantification of Drought: An Evaluation of Drought Indices , 2002 .

[26]  N. Silleos,et al.  The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction , 1993 .

[27]  D. A. Mooley,et al.  Large-Scale Droughts/Floods and Monsoon Circulation , 1980 .

[28]  G. B Samarasinghe,et al.  Growth and yields of Sri Lanka's major crops interpreted from public domain satellites , 2003 .

[29]  Sharon E. Nicholson,et al.  A STUDY OF RAINFALL AND VEGETATION DYNAMICS IN THE AFRICAN SAHEL USING NORMALIZED DIFFERENCE VEGETATION INDEX , 1990 .

[30]  A. Subbiah,et al.  Indian Drought Management: From Vulnerability to Resilience , 1993 .