Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics
Open Access
- 12 October 2014
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 6 (10), 9653-9675
- https://doi.org/10.3390/rs6109653
Abstract
Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.Keywords
This publication has 36 references indexed in Scilit:
- Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ dataRemote Sensing of Environment, 2012
- Estimating Global Cropland Extent with Multi-year MODIS DataRemote Sensing, 2010
- Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) ProjectRemote Sensing, 2010
- The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component AnalysisRemote Sensing of Environment, 2010
- Cropland distributions from temporal unmixing of MODIS dataRemote Sensing of Environment, 2004
- A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, PakistanAgriculture, Ecosystems & Environment, 2002
- Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS dataRemote Sensing of Environment, 2002
- Bagging predictorsMachine Learning, 1996
- Global discrimination of land cover types from metrics derived from AVHRR pathfinder dataRemote Sensing of Environment, 1995
- The Role of Remote Sensing In Determining The Distribution and Yield of CropsAdvances in Agronomy, 1975