Dataset Information

High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.

ABSTRACT: Knowledge of where, when, and how much paddy rice is planted is crucial information for understating of regional food security, freshwater use, climate change, and transmission of avian influenza virus. We developed seasonal paddy rice maps at high resolution (10?m) for Bangladesh and Northeast India, typical cloud-prone regions in South Asia, using cloud-free Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite, the Random Forest classifier, and the Google Earth Engine (GEE) cloud computing platform. The maps were provided for all the three distinct rice growing seasons of the region: Boro, Aus and Aman. The paddy rice maps were evaluated against the independent validation samples, and compared with the existing products from the International Rice Research Institute (IRRI) and the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The generated paddy rice maps were spatially consistent with the compared maps and had a satisfactory accuracy over 90%. This study showed the potential of Sentinel-1 data and GEE on large scale paddy rice mapping in cloud-prone regions like tropical Asia.

SUBMITTER: Singha M

PROVIDER: S-EPMC6472375 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.

Singha Mrinal M Dong Jinwei J Zhang Geli G Xiao Xiangming X

Scientific data 20190411 1

Knowledge of where, when, and how much paddy rice is planted is crucial information for understating of regional food security, freshwater use, climate change, and transmission of avian influenza virus. We developed seasonal paddy rice maps at high resolution (10 m) for Bangladesh and Northeast India, typical cloud-prone regions in South Asia, using cloud-free Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite, the Random Forest classifier, and the Google Earth Engine (GEE) cloud co ...[more]

PMID: 30976017

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Project description:Rice yields are largely influenced by variability in weather. Here, we demonstrate the effect of weather variables viz., maximum and minimum temperatures, rainfall, morning and evening relative humidity, bright sunshine hours on the yield of rice cv. Swarna, grown across five rice ecologies of India through field experiments during kharif (wet) season (Jun-Sept.). Critical thresholds of weather elements were identified for achieving above average, average and below average yield for each ecology. The investigation could determine how different weather elements individually and collectively affect rice yield in different rice ecosystems of India. While a sudden increase in minimum temperature by 8-10 °C (> 30 °C) during reproductive period resulted in 40-50 per cent yield reduction at Mohanpur, a sudden decrease (< 20 °C) caused yield decline at Dapoli. The higher yields may be attributed to a significant difference in bright sunshine hours between reproductive phases of above-average and below-average yield years (ranging from 2.8 to 7.8 hours during P5 stages and 1.7 to 5.1 during P4 stages). Rice cultivar Swarna performed differently at various sowing dates in a location as well as across locations (6650 kg ha-1 at Dapoli to 1101 kg ha-1 at Samastipur). It was also found that across all locations, the above average yield could be associated with higher range of maximum temperature compared to that of below average yield. Principal component analysis explained 77 per cent of cumulative variance among the variables at first growth stage, whereas 70 per cent at second growth stage followed by 74 per cent and 66 per cent at subsequent growth stages. We found that coastal locations, in contrast to inland ones, could maximize the yield potential of the cultivar Swarna, due to the longer duration of days between panicle initiation to physiological maturity. We anticipate that the location-specific thresholds of weather factors will encourage rice production techniques that are climate resilient.

Dataset Information

High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.

Publications

High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.

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