Feature-based algorithm for large-scale rice phenology detection based on satellite images

Xin, Zhao; Nishina, Kazuya; Akitsu, Tomoko; Jiang, Liguang; Masutomi, Yuji; Nasahara, Kenlo Nishida

doi:10.1016/j.agrformet.2022.109283

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…Further information regarding regional variations in rice is described below. Our results confirm the report from the Monsoon Rice Calendar and RICA (Mishra et al, 2021;Zhao et al, 2023).…”

Section: Spatial Distribution Of Paddy Rice In Southeast Asiasupporting

confidence: 92%

“…MODIS imagery data with excellent temporal resolution (i.e., 1 to 2 days if clouds do not impact observations) have been used for mapping of rice cropping intensity for a larger extent. However, the coarse spatial resolution leads to high uncertainty (Laborte et al, 2017;Mishra et al, 2021;Zhao et al, 2023). In this study, we successfully produced a more detailed map across Southeast Asia (Fig.…”

Section: Comparison With National Agricultural Statisticsmentioning

confidence: 96%

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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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“…Further information regarding regional variations in rice is described below. Our results confirm the report from the Monsoon Rice Calendar and RICA (Mishra et al, 2021;Zhao et al, 2023).…”

Section: Spatial Distribution Of Paddy Rice In Southeast Asiasupporting

confidence: 92%

Section: Comparison With National Agricultural Statisticsmentioning

confidence: 96%

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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“…The main reason for the error was that there were different varieties of the same fruit trees grown in the study area, and there was a slight difference in the phenological period. Secondly, sparse woodlands and thickets were more similar to the spectrum of fruit trees, which made it easy to confuse them [61]. In the case of litchi fruit trees, there were early-and late-maturing litchi species, and the time window for the identification of the characteristics of litchi fruit trees in this study was in May and early June.…”

Section: Discussionmentioning

confidence: 85%

A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

Han,

Wang,

et al. 2024

Agronomy

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The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.

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“…With the availability of frequent, high‐resolution satellite imagery, many studies have proposed methods to predict rice phenology using remote sensing data (Boschetti et al., 2017; Zhao et al., 2023). However these mostly use a time‐series of vegetation indices to predict generic land surface phenology (e.g., start‐of‐season, peak‐of‐season, end‐of‐season) rather than actual physiological growth stages (e.g., panicle initiation, flowering).…”

Section: Discussionmentioning

confidence: 99%

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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Feature-based algorithm for large-scale rice phenology detection based on satellite images

Cited by 11 publications

References 56 publications

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

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

A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

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

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