Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

“…The PBIAS between the predicted yield showed a variation of less than ±10 per cent in 5 out of the 10 districts under study. This disagreements between the actual and predicted rice yield estimates could be attributed by other factors, such as atmospheric interference such as cloud (Mkhabela et al, 2011), micro level weather variation and water availability (Son et al, 2013) and uncertaininty associated with ground based estimates (Mosleh and Hassan, 2014).…”

Development of a remote sensing-based rice yield forecasting model

“…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).…”

“…They found that the model performed best (i.e., overly forecasted in the range 14-48%) during the maximum greenness period; (iv) Nuarsa et al (2012) employed Landsat ETM+-derived NDVI-values at 30 m resolution during the peak greenness period (i.e., 63 days after the plantation); and observed a good relationship (i.e., R 2 ≈0.93) between forecasted rice yield and ground-based estimates in Indonesia; (v) Noureldin et al (2013) used different reflective spectral bands of SPOT-4 and several vegetation indices at 20 m resolution during the peak greenness stage over Kafr El-Sheikh Governorate, Egypt. Among these, the spectral bands of R and NIR, and vegetation indices of difference vegetation index, ratio vegetation index, infrared percentage vegetation index, soil adjusted vegetation index, and NDVI demonstrated strong relations (R 2 in the range 0.90 to 0.95) with ground-based rice yield; and (vi) Son et al (2013) used MODIS-derived 8-day composite of enhanced vegetation index (EVI) and leaf area index (LAI), and then developed eight models using linear, quadratic, interaction, and pure quadratic equations over Mekong Delta, Vietnam. They found the quadratic model based on EVI and LAI generated the best results (i.e., R 2 of 0.70 and 0.74 for spring-winter and autumn-summer rice crop, respectively) during ripening stage.…”

Crop yield estimation in 2014 for Vojvodina using methods of remote sensing

“…Since then, many studies have used NDVI as the phenology indicator. Some case studies were based on Enhanced Vegetation Index (EVI) from MODIS (Son et al 2013, Sakamoto et al 2005. Apart from just monitoring phenology, several studies dealt with methods of determining exact phenophases of crops (Curnel & Oger 2007, White et al 2002.…”