Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

Abstract

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.

Development of a remote sensing-based rice yield forecasting model

“…9). In fact, our findings were similar to other studies, such as: (i) Nuarsa et al (2011) found R 2 ≈ 0.93 over Bali Province, Indonesia; (ii) Rahman et al (2009;2012) observed reasonable relationships (i.e., R 2 ≈ 0.56 for aus rice and R 2 ≈ 0.89 for aman rice) over Bangladesh; (iii) Chang (2012) reported good agreements (i.e., R 2 in the range 0.57 to 0.61) over Shi-ko, Taiwan; (iv) Huang et al (2013) predicted the rice yield over five rice growing provinces of China and observed good results (i.e., R 2 in the range 0.84 to 0.97, and overall RE of 5.82%); (v) Noureldin et al (2013) Despite good agreements, it would be worthwhile to note that our forecasting would hold if the rice crop not be affected by natural disturbances (that include cyclone, insect outbreak, etc.). In addition, approximately 14 to 24% of disagreements between the ground-based and forecasted rice yield estimates could be attributed by other factors, such as (i) satellite images might be affected by atmospheric effects (e.g., cloud), which degrade the quality of the acquired data and thus the developed crop-yield model (Mkhabela et al, 2011); (ii) variation in climatic conditions at microlevel during the growing season could potentially impact the agreement level of rice yield (Son et al, 2013;Mosleh et al, 2015); and (iii) uncertainty associated with ground-based yield estimates due to insufficient observations could lead to poor rice yield assessment (Mosleh & Hassan, 2014).…”

“…These images, in general, depict crop-specific characteristics, which could be important in developing pre-harvest yield forecasting models (Jing-Feng et al, 2002;Liu & Kogan, 2002;Prasad et al, 2006;Salazar et al, 2007;Mkhabela et al, 2011). For example: (i) Patel et al (1991) established an empirical relationship between the Indian Remote Sensing Linear Imaging Self Scanning (IRS LISS)-derived ratio between near infrared (NIR) and red (R) spectral bands and ground-based yield; and found that the coefficient of determination (R 2 ), root mean square error (RMSE), and deviations were 0.52, 2.62, and in the range 2-14%, respectively, over India; (ii) Rahman et al (2009;2012) utilized Advanced Very High Resolution Radiometer (AVHRR)-derived 7-day composite of normalized difference vegetation index (NDVI) and brightness temperature at 16 km resolution to compute several vegetation health-related indices such as vegetation condition index, temperature condition index, and vegetation health index in forecasting yield for two types of rice, i.e., aus and aman over Bangladesh. They observed that modelled and ground-based rice yield revealed a R 2 of 0.56 and 0.89 for aus and aman respectively; (iii) Savin & Isaev (2010) used MODIS-derived 10-day composite of NDVI at 250m resolution, fraction of absorbed radiation and two meteorological variables (i.e., temperature and incident solar radiation) to develop a process-based model for forecasting rice yield over Republic of Kalmykia.…”

Using AVHRR-Based Vegetation Health Indices For Estimation Of Potato Yield In Bangladesh

“…In PCR, PCs were sequentially tested for their contribution to improve the regression model for potato yield, keeping only those that resulted in a significant (at the 0.05 level) reduction in residual variance [9]. The first PC, corresponding closely to the average TCI over weeks 50-51 and VCI over weeks 51-52, 1-2, was the best predictor, with PC 4 and 6, corresponding to more detailed patterns in TCI and VCI, also significant predictors.…”

“…In each administrative division spatial average values of NDVI and BT were calculated for each week during 1993-2005. VCI and TCI for the entire Bangladesh were calculated as averages over the six administrative divisions [9]. For consistency, weekly TCI and VCI were detrended by subtracting a five-year moving average in exactly the same way as the potato yield time series was.…”

Analysis of Agricultural Drought in East Java Using Vegetation Health Index

“…Kogan, 2001;Dalezios, Blanta, Spyropoulos, & Tarquis, 2014;Ghaleb, Mario, & Sandra, 2015;Khalil et al, 2013). For instance, the index has been used for estimating the crops productivity in Bangladesh (Rahman, Roytman, Krakauer, Nizamuddin, & Goldberg, 2009). Therefore, the index is considered to be more effective than other vegetation indices because VHI is a combination of other two drought indices, specifically TCI (Temperature Condition Index) and VCI (Vegetation Condition Index).…”