Page 377 - Special Topic Session (STS) - Volume 4

STS2319 Lakshman N. R. et al.

Measuring rice yield from space: The case of Thai

Binh Province, Viet Nam
1
Lakshman Nagraj Rao , Kaiyu Guan , Ngo The Hien , Zhan Li
5
2,3
4
1 Asian Development Bank, Manila, Philippines.
2 Department of Natural Resources and Environmental Sciences, University of Illinois at
Urbana Champaign, Urbana, IL 61801, USA.
3 National Center for Supercomputing Applications, University of Illinois at Urbana
Champaign, Urbana, IL 61801, USA.
4 Center for Informatics and Statistics, Ministry of Agriculture and Rural Development, Viet
Nam.
5 School for the Environment, University of Massachusetts Boston, MA 02125, USA.

Abstract
Despite a growing interest in using satellite data to estimate paddy rice yield
in Southeast Asia, significant cloud coverage has led to a scarcity of usable
optical data for such analysis. In this paper, we study the feasibility of using
surface reflectance fusion data which integrates Landsat and Moderate
Resolution Imaging Spectroradiometer (MODIS) images to circumvent the
cloud cover problem and estimate yield in Thai Binh Province, Viet Nam during
the second growing season of 2015. Our findings indicate that although
Landsat–MODIS fusion data are not necessarily beneficial for paddy rice
mapping when compared with only using Landsat data, fusion data allows us
to estimate the peak value of various vegetation indices and derive the best
empirical relationship between these indices and yield data from the field.

Keywords
Crop cutting; yield, fusion; paddy area; remote sensing

1. Introduction
Traditionally, crop area and yield are estimated using administrative data
or sample surveys (Asian Development Bank 2016). However, measurement
related concerns persist in both cases as data collection officers, farmers, and
others involved in the process may have the tendency to systematically over
or underestimate production and area in their assigned areas (Dillon and Rao,
2018). An alternative to using administrative data or conducting surveys is the
application of satellite remote sensing techniques, which has been ongoing
for the past several decades with some progress achieved for paddy rice
(Kuenzer and Knauer 2013; Mosleh, Hassan, and Chowdhury 2015).
From a methodological perspective, substantial progress has been made
on remote sensing techniques to identify rice areas. However, estimating rice
yield in the remote sensing context is still at a very nascent stage. There are
several challenges associated with satellite-based crop yield estimation. First,
there is a lack of reliable ground-truth crop yield data for model calibration

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