Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km × 1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n = 45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.

[1]  S. S. Ray,et al.  Crop assessment using remote sensing - Part-II: Crop condition and yield assessment. , 2000 .

[2]  Alexey Terekhov,et al.  Estimation of spring crops sowing calendar dates using MODIS in Northern Kazakhstan , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[3]  K. Mallick,et al.  Efficiency based wheat yield prediction in a semi-arid climate using surface energy budgeting with satellite observations , 2011 .

[4]  J. Monteith SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[5]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[6]  Steven W. Running,et al.  A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index , 2003 .

[7]  M. Dingkuhn,et al.  Effect of drainage date on yield and dry matter partitioning in irrigated rice , 1996 .

[8]  Rahul Nigam,et al.  Formulation of Time Series Vegetation Index from Indian Geostationary Satellite and Comparison with Global Product , 2012, Journal of the Indian Society of Remote Sensing.

[9]  A. J. Stern,et al.  Crop Yield Assessment from Remote Sensing , 2003 .

[10]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[11]  K. R. Manjunath,et al.  Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data , 2003 .

[12]  R. Jongschaap Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen , 2006 .

[13]  Roger W. Elmore,et al.  Soybean Sowing Date: The Vegetative, Reproductive, and Agronomic Impacts , 2008 .

[14]  V. Sehgal,et al.  Wheat yield modelling using satellite remote sensing with weather data : Recent Indian experience , 2009 .

[15]  V. Sehgal,et al.  Improved regional yield prediction by crop growth monitoring system using remote sensing derived crop phenology , 2012 .

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

[17]  Jai Singh Parihar,et al.  Multiple production forecasts of wheat in India using remote sensing and weather data , 2006, SPIE Asia-Pacific Remote Sensing.