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Identifying Dry-season Rice-planting Patterns in Bangladesh Using the Landsat Archive

Aaron M. Shew*
College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA

Aniruddha Ghosh
Environmental Science and Policy, University of California, Davis, CA 95616, USA; anighosh@ucdavis.edu

Abstract/ Executive Summary:

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

Keywords: Bangladesh; Boro rice; Time series; Food security; Landsat; Google Earth Engine

Location:

Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Remote sensing

Objectives:

The objectives of this study are: (i) To demonstrate the use of a harmonic-time-series-based vegetation indices (HTS-VIs) model with Landsat in providing consistent estimates of areas annually planted to boro rice in Bangladesh from 2014–2018; (ii) To analyze regional differences in boro rice phenology and production; and (iii) To compare and contrast the results of the HTS-VIs model with reference sources for boro rice areas in Bangladesh.

Methodology:

Bangladesh (spanned across 88⁰E to 92.5⁰E and 20.5⁰N to 26.6⁰ N) is one of the most densely populated countries on earth, with a population of approximately 160 million people and a land area of about 130,000 square kilometers. There are 8 divisions, 64 districts, and 490 Upazila (subdistricts) in the Bangladeshi administrative hierarchy0. Bangladesh has significantly improved the livelihood of its citizens since its independence in 1971. Life expectancy has increased from below 50 in the 1970s to over 70 in 2013, and most metrics for health and food security, i.e., wasting, stunting, mortality rates for children under 5, etc., have improved0. Approximately 50 percent of people in Bangladesh rely directly on rice production for both income and food, and due to rapid land use and land cover changes in the country0, there are increasing pressures on agriculture as a whole and for rice production specifically. In the coastal region, many farmers face increasingly extreme flood events and salinity intrusion. In the northwest, farmers deal with more frequent and severe droughts0, and the highly productive northeast region experiences flash floods that may lead to food shortages at the national level [42]. Additionally, urbanization and population growth have contributed to decreases in arable land, making agricultural resilience to environmental changes and the adoption As the primary staple food, rice is of utmost importance in Bangladesh and throughout Asia [43]. The landscape and agricultural production systems vary regionally within the country, resulting in temporally and spatially fragmented rice-production areas [25,28]. There are three rice-producing seasons: The primary production season during the monsoon (aman) generally begins in July or August and ends between November and January depending on the region and rice cultivars planted; dry (boro) irrigated rice production follows aman production in some regions, especially in the north and east, with planting generally in December or January and harvest in March or April. Finally, the early summer season (aus) is a short rice season that precedes aman in a few areas. Most arable land with access to sufficient fresh water is planted to paddy rice in the aman season. However, the adoption of boro or aus rice in double- or (seldom) triple-cropping systems is limited due to environmental and climatic factors. Some regions lack freshwater during the dry season for irrigating boro rice, or may even face salinity intrusion in soils as in some coastal regions. Regions that do have adequate freshwater from ground or surface sources may grow a second season of rice, often hybrid or HYV, because of the shorter growing season requirements compared to traditional cultivars (~90–120 days). Second season (and specifically hybrid and HYV) rice production in Bangladesh may contribute significantly to sustainable intensification of agriculture in the coming decades. This is particularly critical for Bangladesh because arable land is decreasing, populations are increasing, and rice consumption per capita is one of the highest in the world.

Source of Information: Remote Sens. 2019, 11, 1235;

Article Link (Online Journal): doi:10.3390/rs11101235 www.mdpi.com/journal/remotesensing

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In this study, we demonstrated the implementation of a harmonic time series (HTS) model with EVI and NDFI (VIs) to identify rice production areas during the boro season in Bangladesh for 2014–2018. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. We found that the HTS-VIs model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other models and governmental estimates but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national estimates by BBS. Moreover, the results relate strongly with the BBS district estimates of rice area with an average R-squared of 0.88. The HTS-VIs model extends previous efforts to map rice in fragmented landscapes, and the results of our study demonstrate its use in a complex environment. In future work, the model may be improved by comparing results with on-the-ground information to further validate and affirm the potential of this rice-mapping technique. As agricultural monitoring via remote sensing becomes more widespread and important in meeting global food security needs, we suggest that models like the HTS-VIs introduced here could improve the accuracy and efficiency with which scientists are able to do so, giving policy-makers and development practitioners an enhanced platform for their work.

Knowledge Management: Journal