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STS2319 Lakshman N. R. et al.
and testing at regional scales. Field-level crop cutting data is usually costly and
labor-intensive, and district-level crop statistics are either not easily accessible
or of low quality in developing countries (Asian Development Bank 2016).
Second, satellite data with both high temporal and spatial resolutions are
limited in terms of availability and cost. Given that the majority of paddy rice
fields in Southeast Asia are smallholder farms, there is a need for high spatial
resolution data down to 10–30 meters (m), and high-frequency time series
data during the peak growing season to develop an advanced crop yield
algorithm (Lobell et al. 2015, Sibley et al. 2014).
The objective of this paper is to build a prototype to map paddy rice fields
and estimate crop yield in Thai Binh, using two satellite data sources - Landsat,
and MODIS, alongside field data collected through crop-cutting activities
during the rainy season of 2015. This study contributes to the growing
literature on yield estimation using remote sensing techniques in its innovative
employment of data fusion of Landsat– MODIS for crop yield estimation,
which makes it possible to obtain high resolution data in both space than the
individual sources themselves, which is critical for estimating rice area and
yields in settings where smallholder farms are prevalent.

2. Methodology
A. Study Area
The study area includes the province of Thai Binh, located in northeastern
coastal Viet Nam. Thai Binh is a key paddy rice production area in the Red
River Delta region which is the second largest paddy rice-producing region in
Viet Nam. Paddy rice is grown twice a year – during summer (mid-June to early
October) and winter (mid-December to late May). Thai Binh has one key rainy
season which starts in May and ends in October. Our study focuses on the
summer growing season of 2015.

B. Landsat–MODIS Fusion
To overcome the challenge of availability of satellite data with high spatial
and temporal resolution, we fuse the surface reflectance data from Landsat
(16-day, 30 m) and MODIS (daily, 250–500 m) to generate a product that has
both high spatial and high temporal resolution. We employ a mature Landsat–
MODIS fusion algorithm, the Spatial and Temporal Adaptive Reflectance
Fusion Model (STARFM) (Gao et al. 2006). STARFM model blends Landsat and
MODIS data to generate synthetic daily surface reflectance products at
Landsat spatial resolution based on a deterministic weighting function
computed by spectral similarity, temporal difference, and spatial distance. The
algorithm requires Landsat and MODIS pair images for the same date with
clear-day quality. This posed several challenges for our study (Chen 2011).
First, no single completely clear Landsat scene was available in the study area
due to cloud contamination and the SLC-off problem, which limited the

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