Mapping rice greenhouse gas emissions in the Red River Delta, Vietnam

Torbick, Nathan; Salas, William; Chowdhury, Diya; Ingraham, Pete; Trịnh, Mai Văn

doi:10.1080/17583004.2016.1275816

Cited by 21 publications

(31 citation statements)

References 44 publications

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“…As Sentinel-1A and B both become operational, the user community will have open access, operational C-band SAR at 6-day frequency for some priority regions. The proliferation of operational, moderate resolution (<30 m) and temporally frequent SAR will also lead to the next generation of Monitoring, Reporting, and Verification (MRV) tools that can help address climate change and agroforestry greenhouse emissions policies (e.g., [28]). The overarching goal of this research application was to develop and apply a rice monitoring framework using multiscale, moderate resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

et al. 2017

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Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km 2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R 2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.

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Section: Introductionmentioning

confidence: 99%

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

et al. 2017

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“…Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used to investigate regional CH 4 emissions based on empirical models by extracting wetland area and land surface temperature data. 30 Torbick et al 31 fused different sources of satellite products to generate information on field management to use as input parameters for the DNDC (DeNitrification-DeComposition) model to estimate paddy GHG emissions. Hayashida et al 32 successfully analyzed Asian paddy emission patterns with a scanning imaging absorption spectrometer for atmospheric cartography, demonstrating the potential to estimate agricultural GHG emissions through hyperspectral data.…”

Section: Whichmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of methane emissions based on crop yield and remote sensing data in a paddy field

Shi

Zhang

et al. 2020

Greenhouse Gases

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Quantifying agricultural greenhouse gas (GHG) emissions is important for addressing global warming. In this regard, empirical models were constructed to evaluate the feasibility of using rice yield and canopy spectral properties for estimating paddy cumulative methane (CH4) emissions (CCE). A field experiment with shading treatments was conducted in 2017. A static chamber‐gas chromatography was used to measure CH4 fluxes during the growing season. Canopy hyperspectral reflectance was measured and then used to calculate the multispectral normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the enhanced vegetation index (EVI). The results show that CH4 emissions were positively correlated to rice yield and biomass, indicating that higher biomass provided more substrates for CH4 generation. O2 availability maybe a main factor in the CCE differences under shading treatments. RVI and EVI showed a stronger positive relationship to CH4 emissions than NDVI. The empirical model including yield and EVI‐JS (EVI of jointing‐booting stage) as input variables performed better (adj‐R2 = 0.85, root mean square error [RMSE] = 12.26 kg ha−1) than the model that included only yield (adj‐R2 = 0.5, RMSE = 22.5 kg ha−1). This study suggests that it is feasible to apply remote sensing in paddy CH4 estimation, providing a referable attempt for future regional agricultural GHG emissions quantification. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…This environment may produce strongly reductive conditions in paddy soils, causing anaerobic soil archaea to emit substantial amounts of CH 4 . Although the importance of agricultural mitigation has been affirmed by the Vietnamese government, which aims to reduce GHG emissions by 20% and promote water management with increased drainage periods, integrated research and decision support tools are still needed to quantify emission baselines and evaluate the mitigation potential with a scientifically robust, transparent, and scalable approach [12].…”

Section: Introductionmentioning

confidence: 99%

“…To regionally assess the CH 4 emissions from rice paddies, Tier 1 methods [13] for calculating emissions with emission factors (kg CH 4 ha −1 day −1 ) and scaling factors (e.g., differences in water regimes during the cultivation period, differences in water regimes in the pre-season before the cultivation period, organic matter (rice straw) application rate/timing, and soil types) have been widely employed [12,14,15]. Additionally, the development of country-specific emission factors is ongoing for a Tier 2 method based on ground flux measurement data (e.g., the specific emission factor in the Philippines [16]).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing

et al. 2018

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In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas.

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Mapping rice greenhouse gas emissions in the Red River Delta, Vietnam

Cited by 21 publications

References 44 publications

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

Estimation of methane emissions based on crop yield and remote sensing data in a paddy field

Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing

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