Aus rice statistics and satellite data for Bangladesh were used in this study. Aus rice (AR) production data were collected from the Bangladesh Bureau of Statistics, which estimates aus rice production and area sown from sampling surveys. Yield was calculated by dividing total AR production by AR yield time series for 1991–2005. The satellite data used were from the NOAA Global Vegetation Index (GVI) data set, which was developed by aggregating the 4 square km Global Area Coverage (GAC) daily AVHRR products to 16 square km spatial resolution and seven–day composite.
GVI is based on NDVI and BT AVHRR products, which are derived from the visible (VIS, 0.58–0.68 μm, Ch1), near infrared (NIR, 0.72–1.00 μm, Ch2) and (thermal) infrared (IR, 10.3–11.3 μm, Ch4) AVHRR channels. Post–launch–calibrated VIS and NIR intensities were converted to reflectance and used to calculate the Normalized Difference Vegetation Index (NDVI = (NIR – VIS)/ (NIR + VIS)). The Ch4 counts were converted to brightness (radiative) temperature (BT) .
Details of the algorithm for calculating GVI from NDVI and BT are presented in Kogan. Briefly, this involves: (a) elimination of high frequency noise from NDVI and BT time series, (b) estimation of the mean annual cycle, (c) calculation of multi–year climatology and (d) estimation of weekly fluctuations from the mean seasonal cycle (departure from climatology) associated with weather variations. GVI include the indices VCI characterizing plant greenness, TCI characterizing thermal conditions and VHI, a linear combination of VCI and TCI. These indices were calculated as:
VCI = 100 (NDVI-NDVImin) / (NDVImax-NDVImin)...............................(1)
TCI=100 (BTmax−BT) / (BTmax−BTmin)..................................(2)
VHI=a*VCI+(1−a)*TCI.............................................................(3)
where NDVI, NDVImax, NDVImin, BT, BTmax and BTmin are the smoothed weekly NDVI or BT and their 1991–2005 absolute maximum and minimum, respectively; a is the coefficient quantifying a share of VCI and TCI contribution to the VHI, which is thus a weighted average of the two. Since this share is not known for a specific location we follow the standard definition of VHI, where the shares are equal and a=0.5 (future investigation could evaluate also other combinations of VCI and TCI as possible predictors of crop yield). All three indices are scaled to range from 0 (severe vegetation stress) to 100 (exceptionally favorable conditions).
The GVI product, at 16 km2 resolution, was averaged over land pixels in each of the six administrative divisions of Bangladesh. In each administrative division spatial average values of Vegetation Health Indices were calculated for each week during 1991–2005. Mean VH Indices data for the entire Bangladesh were calculated as area-weighted average vegetation health indices for the six administrative divisions.
The research strategy employed was to correlate annual yield with weekly NDVI and BT, expressed in the form of VH indices. We hypothesized that there may be a strong correlation between these remotely sensed surface indicators during the early spring, i.e. around the time of the sowing and early growth of AR, and AR yields for that year. Finding and quantifying a strong correlation early in the growing season between these remotely sensed surface indicators and AR yields would allow early prediction of national AR harvest size from remote sensing, aiding farmers and consumers in decision making and providing several months’ lead time to initiate relief efforts.