Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery

Brinkhoff, James; Houborg, Rasmus; Dunn, Brian

doi:10.1016/j.agwat.2022.107907

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…Weather data included observations up to January 13, followed by GFS forecasts for 14 days, then the average of historical observations after the forecast window. The ponding prediction also used a logistic regression model, with remote sensing features (Brinkhoff et al., 2022), and predicted a ponding date of December 15. The probability of having reached PI and flowering are shown.…”

Section: Resultsmentioning

confidence: 99%

“…This combined (from most accurate to least accurate): Observations of past weather from the SILO dataset, 14 day forecasts of up‐coming weather, from the Global Forecast System data produced by NOAA and delivered in Google Earth Engine (Gorelick et al., 2017), Historical SILO data to fill in dates beyond the forecast, created by averaging 2000–2022 data per date. These fused weather data were input to the phenology prediction models, along with sowing methods and sowing dates provided by growers, and ponding dates predicted using remote sensing using the method described by Brinkhoff et al. (2022). This method classified each date as pre‐ or post‐ponding using a time‐series of Sentinel‐2 satellite reflectance data, which are sensitive to rice field ponding status.…”

Section: Methodsmentioning

confidence: 99%

“…These fused weather data were input to the phenology prediction models, along with sowing methods and sowing dates provided by growers, and ponding dates predicted using remote sensing using the method described by Brinkhoff et al (2022). This method classified each date as pre-or postponding using a time-series of Sentinel-2 satellite reflectance data, which are sensitive to rice field ponding status.…”

Section: • Degree Daysmentioning

confidence: 99%

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Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

et al. 2023

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Crop phenology modeling often involves determining variety‐specific growing degree day thresholds, or parameterizing mechanistic crop models. In this work, we used machine learning methods to develop models that provide daily predictions of the probability that rice (Oryza sativa) crops had reached the panicle initiation and flowering growth stages. These per‐date classifications were summarized into per‐paddock growth stage transition dates, which were then compared with field‐sampled reference data, encompassing 15 rice varieties, 10 years, and 380 sites. Leave‐one‐year‐out cross validation was used to provide realistic estimates of model errors. Compared with more complex and computationally intensive algorithms, logistic regression produced competitive results (mean cross‐season validation RMSE 3.9 and 5.2 days for panicle initiation and flowering, respectively). Logistic regression had additional advantages: providing confidence of growth stage predictions at each date (as it is a probabilistic algorithm), and straightforward explainability (as model parameters directly indicated how the various input variables contributed to growth stage predictions). Input variables included accumulated weather, rice variety, and sowing methods. The models were applied to forecasting phenology transition dates of the rice crops planted throughout the Murray and Murrumbidgee valleys. In addition, recommendations for optimal sowing dates were developed, using simulations involving more than 40 years of weather data, with the goal of minimizing the risk of cold‐temperatures during the microspore growth phase, which can severely degrade yield in temperate rice growing regions.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: • Degree Daysmentioning

confidence: 99%

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Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

et al. 2023

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“…GEE pre-processed the data with the Sentinel-1 Toolbox to remove thermal noise, radiometric calibration, orbital correction, and terrain correction (https://developers.google.com/earthengine/guides/sentinel1). Sentinel-2 (referred to Sentinel 2A/B) Level-2A (Surface Reflectance, SR) images have many artefacts and overcorrected and therefore were substituted with Sentinel-2 Level-1C (Top-of-Atmosphere Reflectance, TOA) (Brinkhoff et al, 2022;Fatchurchman et al, 2022). More information on the Sentinel-2 MSI product can be found in the User Guides on the ESA website (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi, accessed on 7 September 2023).…”

Section: Sentinel-1 and Sentinel-2 Time-series Datamentioning

confidence: 99%

SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data

Ginting,

Rudiyanto,

Mohd Shah

et al. 2024

Preprint

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Abstract. The Southeast Asia region has a vast expanse with diverse tropical climates, making it a prominent centre of rice cultivation, contributing to about 20 % of the world’s rice production and contributes 29 % of global rice methane emissions. As a staple food for many countries, accurate and up-to-date information on the rice harvested area is crucial for addressing food security issues, predicting rice yield and methane emissions, and formulating effective government policies. This paper presents the first detailed study of rice cropping intensity and harvested areas throughout Southeast Asia. Current remote sensing products have not been able to produce up-to-date cropping intensity information due to the variability of local cultivation practices. To address this problem, we integrated Sentinel-1A and Sentinel-2A/B time series data from 2020 to 2021 and developed a local unsupervised classification with phenological labelling (LUCK-PALM) method. We implemented the system on the Google Earth Engine (GEE) cloud-based platform to produce geospatial products of rice cropping intensity and harvested area at a spatial resolution of 10 m called SEA-Rice-Ci10s. The results show that Southeast Asia's total rice growing area in 2020–2021 was 28.5 Mha, with 51 % single cropping, 47 % double cropping, and 2 % triple cropping. These were equivalent to 42.9 Mha of annual harvested area, consisting of Thailand (11.2 Mha), Indonesia (8.4 Mha), Myanmar (8.4 Mha), Vietnam (6.3 Mha), Cambodia (3.9 Mha), the Philippines (3.3 Mha), Laos (0.8 Mha), Malaysia (< 0.5 Mha), and Timor-Leste (0.01 Mha). We compared our rice maps to agricultural statistics data at the district and province levels and existing rice maps for some Southeast Asian countries. The results demonstrate that our map agreed well with countries’ statistics with a linear coefficient of determination (R2) from 0.85 to 0.97. Compared to existing products, our map can resolve small paddy fields of about 0.2 ha in the hilly areas. This information will be useful in addressing food security challenges and improving estimates of methane emissions from rice cultivation. The 10 m paddy rice cropping intensity map for Southeast Asia, SEA-Rice-Ci10, is available on the GEE App (https://rudiyanto.users.earthengine.app/view/seariceci2021), the Climate TRACE platform (https://climatetrace.org/) and the Zenodo repository (https://doi.org/10.5281/zenodo.10707621) (Frisa Irawan et al., 2024).

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“…Incorporating Sentinel 2 data in this study would have enhanced the frequency of satellite imagery [30], potentially benefiting the two-week clustering method. However, Sentinel data became available in Australia only in December 2018 [65], precluding the utilisation of crop production data from the 2017/18 and 2018/19 seasons. The absence of these data years would have considerably diminished the dataset available for model development.…”

Section: Limitationsmentioning

confidence: 99%

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

Clarke,

Yates,

Blanchard

et al. 2024

Remote Sensing

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Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield (HRY), is less explored. This research collated crop-level HRY data across four seasons (2017/18–2020/21) from Australia’s rice-growing region. Models were developed using the XGBoost algorithm trained at varying time steps up to 16 weeks pre-harvest. The study compared the accuracy of models trained on datasets with climate data alone or paired with vegetative indices using two- and four-week aggregations. The results suggest that model accuracy increases as the harvest date approaches. The dataset combining climate and vegetative indices aggregated over two weeks surpassed industry benchmarks early in the season, achieving the highest accuracy two weeks before harvest (LCCC = 0.65; RMSE = 6.43). The analysis revealed that HRY correlates strongly with agroclimatic conditions nearer harvest, with the significance of vegetative indices-based features increasing as the season progresses. These features, indicative of crop and grain maturity, could aid growers in determining optimal harvest timing. This investigation offers valuable insights into grain quality forecasting, presenting a model adaptable to other regions with accessible climate and satellite data, consequently enhancing farm- and industry-level decision-making.

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Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery

Cited by 6 publications

References 41 publications

Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

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