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Research Article Open access CC BY 4.0

Use of Satellite Remote Sensing for Rice Yield Estimation: A Case Study of Polonnaruwa District, Sri Lanka

Thilantha Lakmal Dammalage, Tharshini Shanmugam

Asian Journal of Advances in Agricultural Research · pp. 1–9 · Published 11 Oct 2018

10.9734/AJAAR/2018/43843

Abstract

In Sri Lanka, a conventional method named the Crop-cut survey is currently used to estimate the seasonal rice production. However, it fails to predict rice yield before the harvest as it is conducted during the harvest. Therefore, this study focuses on testing rice yield estimation models based on satellite remote sensing data. Landsat 8 OLI/TIRS images (30m spatial resolution) from Earth Explorer and 8-day composite images (250 m spatial resolution) from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor onboard NASA EOS Terra/Aqua satellite from 2014 to 2017 were used. Cultivated paddy lands were identified by land cover classification, using field-training samples and Landsat 8 OLI/TIRS data. In addition, the temporal change of Normalised Differenced Vegetation Index (NDVI) for paddy and forest were analysed to validate the classification. The observed minimum accuracy of the land cover classification, out of the tested four (4) seasons, was 99.4%, and the minimum Kappa coefficient was 0.9916. The correlation between reference net harvested paddy area and identified paddy cultivated area by Landsat 8 was 0.93. Linear and exponential yield forecasting models developed for Kurunegala District were validated and tested, based on NDVI and EVI2 (Enhanced Vegetation Index) indices obtained through MODIS surface reflectance images of Polonnaruwa District. Both NDVI and EVI2-based models derived after about 80-days of transplanting provide more reliable estimations with compared to national statistical records. Nevertheless, the EVI2-based model provides more reliable estimates than the NDVI-based model with 83.7% average accuracy. Therefore, the rice yield can be successfully estimated for each season before one-month to the harvest time using the EVI2-based model in Polonnaruwa district.

Rice yield Landsat 8 OLI/TIRS MODIS NDVI EVI2

References (10)

  1. 1 Yield Trends Are Insufficient to Double Global Crop Production by 2050 [DOI]
  2. 2 Development of a two-band enhanced vegetation index without a blue band [DOI]
  3. 3 What it will take to Feed 5.0 Billion Rice consumers in 2030 [DOI]
  4. 4 Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation [DOI]
  5. 5 Crop yield estimation by satellite remote sensing [DOI]
  6. 6 Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR [DOI]
  7. 7 Rice yield forecasting models using satellite imagery in Egypt [DOI]
  8. 8 Rice Yield Estimation Using Landsat ETM+ Data and Field Observation [DOI]
  9. 9 Estimating Rice Yield by HJ-1A Satellite Images [DOI]
  10. 10 Mapping paddy rice yield in Zhejiang Province using MODIS spectral index [DOI]

Cited by 2

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W. M. R. K Wanninayaka, R. M. K. T Rathnayaka, E. P. N Udayakumara · 2020 5th International Conference on Information Technology Research (ICITR) · 2020

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