Rice Yield Estimation Using Landsat ETM+ Data and Field Observation

Abstract

<p>Forecasting rice yield before harvest time is important to supporting planners and decision makers to predict the amount of rice that should be imported or exported and to enable governments to put in place strategic contingency plans for the redistribution of food during times of famine. This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation. The result showed that the rice yield could be estimated using the exponential equation of y = 0.3419e^4.1587x, where y and x are rice yield and NDVI, respectively. The R² and SE of the estimation were 0.852 and 0.077 ton/ha, respectively. An accuracy assessment of rice yield estimation using Landsat images was performed by comparing the rice yields from the estimation result and the reference data. The results show that the linear relationship with the R² and SE of the estimation were 0.9262 and 0.21 ton/ha, respectively. The R² is greater than or equal to 0.8, which demonstrates a strong agreement between the remotely sensed estimation and the reference data. Thus, the Landsat ETM+ has good potential for application to rice yield estimation.</p

Development of a remote sensing-based rice yield forecasting model

“…Despite the accuracy of these data and its ability to depict historical trends, this method has two major drawbacks: (i) time-consuming, subjective, costly, and labour-intensive (Reynolds et al, 2000;Prasad et al, 2006;Nguyen et al, 2012); and (ii) the outcomes are usually made available to the government and public after several months of the harvesting of the crop; thus not useful for food security purposes (Noureldin et al, 2013). In order to address these issues, an alternate method is the use of the remote sensing-based techniques that have already demonstrated effectiveness in forecasting the rice yield (Jing-Feng et al, 2002;Wang et al, 2010;Chen et al, 2011;Nuarsa et al, 2012;Huang et al, 2013) and assessing the yield for other crops (Bonilla et al, 2015;Fortes et al, 2015). It is being possible as remote sensing platforms are able to acquire cropping season dynamics over a large geographic extent on timely fashion in the form of images.…”

“…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. 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.…”

Multitemporal MODIS Data to Mapping Rice Field Distribution in Bali Province of Indonesia Based on the Temporal Dynamic Characteristics of the Rice Plant

“…Most of remote sensing research of rice plant is about rice productivity estimation (e.g. Nuarsa et al, 2012;Shao et al, 2001;Gumma et al, 2011). So that, it is needed to do other research related with rice plant; one of them is mapping the rice field distribution.…”

The Use of Remotely Sensed Data for Estimating of Rice Yield Considering Soil Characteristics

“…In previous agricultural research works using remote sensing data, development of the algorithms for rice field mapping and production estimation was executed (Takezawa et al, 2007;Hongo et al, 2012;Shikata et al, 2013). One of such works conducted in Bali of Indonesia suggested that Rice Growth Vegetation Index could be a new vegetation index (Nuarsa et al, 2011a).…”