Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Abstract

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.

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

“…On the contrary, Landsat represents the series of satellites that allows the larger temporal archive since the early 1980s and hence the larger time span for rs+pheno studies; while Sentinel-2 (launched in 2015) is the emerging technology in rs+pheno research, thanks to its high spatial resolution (10 m) and high temporal resolution (every 5 days). Based on such satellites, several ways of integrating the proposed remote sensing VIs into phenological modeling analysis have been suggested through time, e.g., for studying anomalies in spring and autumn phenology [48,49], analyzing the effects of warmer temperatures on the incidence and severity of frost damage [50,51], exploring the role of phenology in fire dynamics [52,53], combining multi-resolution products [54,55], smoothing satellite time-series data [56,57], integrating remotely sensed phenology data and climate [58,59]. While NDVI and EVI represent the most agreed VIs in rs+pheno studies, LAI and FAPAR, although largely used as inputs in crop modeling studies [60][61][62], remain only partially adopted.…”

“…The attention towards the phenology metrics detection and extraction also represented the main topic for both 1999-2008 and 2009-2018, with the majority of the studies focusing on harmonic analysis [52,57,63]. The start of the season (SOS) represents the metric most investigated; its study has increased through time to such an extent that it became a major, common topic and, hence, lost its relevance.…”

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

“…Mapping methods using dense time-series imagery are standard for analyzing rice distribution and yield (Shew and Ghosh 2019;Mosleh, Hassan, and Chowdhury 2015). Two problems persist with singledate optical imagery of rice areas: (1) frequent cloud cover and (2) highly dynamic plant cover throughout the growing season.…”

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

“…Many methods for mapping rice have been developed based on different remote sensing data, including (1) machine learning classifiers (e.g., random forest and support vector machines), (2) phenology-based classifiers, (3) rule-based algorithms, and (4) the time-series algorithm approach (Dong et al, 2016b;Bazzi et al, 2019;Dong et al, 2016a;Dong and Xiao, 2016;Luo et al, 2020b, a;Nelson et al, 2014;Phung et al, 2020;Minasny et al, 2019;Shew and Ghosh, 2019;Xiao et al, 2006;Zhan et al, 2021). Satellite image sources include MODIS, Landsat, Sentinel, RADARSAT, and PALSAR (Dong and Xiao, 2016;Shao et al, 2001;Singha et al, 2019;Zhou et al, 2016).…”

“…The International Rice Research Institute (IRRI) extracted the distribution of paddy rice for Asia (Nelson and Gumma, 2015). However, the paddy rice maps generated using MODIS data contain a large number of mixed pixels caused by the coarse spatial resolution (500 m) (Dong et al, 2015(Dong et al, , 2016bShew and Ghosh, 2019), particularly in hilly areas (Liu et al, 2019). The mixed land cover types within MODIS pixels can affect the accuracy of the rice map (Sun et al, 2009).…”