Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Sun, Chunling; Zhang, Hong; Ge, Jike; Wang, Chao; Li, Liutong; Xu, Lu

doi:10.3390/rs14133213

Cited by 19 publications

(11 citation statements)

References 51 publications

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“…Rice is one of the essential staple food crops globally and in China ( Ali et al., 2020 ), and is cultivated in tropical and subtropical environments ( Sun et al., 2022 ). In southern China, rice is cultivated twice a year where light and heat resources are generally enough, i.e., early rice and late rice.…”

Section: Introductionmentioning

confidence: 99%

Effects of various seed priming on morphological, physiological, and biochemical traits of rice under chilling stress

Zhang

Zhang²,

Gao³

et al. 2023

Front. Plant Sci.

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Introduction/BackgroundDirect-seeded rice is exceptionally vulnerable to chilling stress, especially at the seed germination and seedling growth stages in the early season of the double cropping system.MethodsTherefore, we conducted two experiments to evaluate the role of various seed primings and their different concentrations of plant growth regulators [experiment 1—abscisic acid (ABA), gibberellin (GA3), salicylic acid (SA), brassinolide (BR), paclobutrazol, uniconazole (UN), melatonin (MT), and jasmonic acid (JA)] and osmopriming substances (chitosan, polyethylene glycol 6000 (PEG6000), and CaCl2) and experiment 2—GA, BR (two best), CaCl2 (worst), and control (CK)] on rice seedlings under low temperature condition.ResultsResults showed that the maximum germination rate of 98% was recorded in GA3 (10 mgL−1) and BR (0.3 mgL−1) among treatments. Compared to CK, root and shoot length were improved in ABA (0.5 mgL−1) and GA3 (100 mgL−1) by 64% and 68%, respectively. At the same time, root and shoot weights (fresh and dry) were enhanced in Paclobutrazol (300 mgL−1) and GA3 among treatments. Furthermore, the average root volume, average root diameter, and total root surface area were increased by 27%, 38%, and 33% in Paclobutrazol (300 mgL−1), Paclobutrazol (200 mgL−1) and JA (1 mgL−1) treatments, respectively compared to CK. In the second experiment, a respective increase of 26%, 19%, 38%, and 59% was noted in SOD, POD, CAT, and APX enzyme activities in GA treatment compared to CK. Similarly, proline, soluble sugar, soluble protein, and GA content were also improved by 42%, 25.74%, 27%, and 19%, respectively, in GA treatment compared to CK. However, a respective reduction of 21% and 18% was noted in MDA and ABA content in GA treatment compared to CK. Our finding highlighted that better germination of primed-rice seedlings was associated with fresh and dry weights of the roots and shoots and the average root volume of the seedlings.DiscussionOur results suggested that GA3 (10 mg L−1) and BR (0.3 mg L−1) seed priming prevent rice seedlings from chilling-induced oxidative stress by regulating antioxidant enzyme activities and maintaining ABA, GA, MDA, soluble sugar, and protein content. However, further studies (transcriptome and proteome) are needed to explore the molecular mechanisms involved in seed priming-induced chilling tolerance under field conditions.

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Section: Introductionmentioning

confidence: 99%

Effects of various seed priming on morphological, physiological, and biochemical traits of rice under chilling stress

Zhang

Zhang²,

Gao³

et al. 2023

Front. Plant Sci.

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“…LSTMs were previously demonstrated in several remote sensing applications, particularly land cover mapping and crop monitoring [26][27][28][29][30][31][32][33][34]. However, to date, the use of LSTMs in forest attribute prediction utilising Earth Observation (EO) data was limited if at all reported with SAR image time series.…”

Section: Introductionmentioning

confidence: 99%

Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series

et al. 2022

Remote Sensing

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Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.

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“…As described in many previous studies (Singha et al, 2019;Chang et al, 2020;Crisóstomo De Castro Filho et al, 2020;Sun et al, 2022a), VH polarization was more sensitive to the flooding period of rice than VV polarization and has been more widely used for rice area extraction. Therefore, Sentinel-1 VH polarization time-series data were selected in this study.…”

Section: Feature Extractionmentioning

confidence: 72%

“…Optical data are easily obscured by clouds, which poses a challenge for rice area extraction in humid and subhumid climates with abundant water resources such as Southeast Asia (Liu et al, 2019;Sun et al, 2021). Compared with traditional optical remote sensing, synthetic-aperture radar (SAR) is an active microwave radar with the advantages of all day and all weather, is weather-independent, can penetrate clouds and is very sensitive to the geometric structure and dielectric properties of crops (Huang et al, 2017;Orynbaikyzy et al, 2019;Sun et al, 2022a). In recent years, free SAR data represented by Sentinel-1 data have been widely used in rice mapping over large regions.…”

mentioning

confidence: 99%

Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data

Sun

Zhang

et al. 2023

Earth Syst. Sci. Data

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Abstract. Over 90 % of the world's rice is produced in the Asia–Pacific region. Synthetic-aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. The complexity of rice cultivation patterns in tropical and subtropical regions makes it difficult to construct a representative data-relevant rice crop model, increasing the difficulty in extracting rice distributions from SAR data. To address this problem, a rice area mapping method for large regional tropical or subtropical areas based on time-series Sentinel-1 SAR data is proposed in this study. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatiotemporal statistical features with good generalization ability was selected and then input into the U-Net semantic segmentation model, combined with WorldCover data to reduce false alarms, finally the 20 m resolution rice area map of five countries in mainland Southeast Asia in 2019 was obtained. The proposed method achieved an accuracy of 92.20 % on the validation sample set, and the good agreement was obtained when comparing our rice area map with statistical data and other rice area maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice area mapping with complex cropping patterns and the reliability of the generated rice area maps. The 20 m annual paddy rice area map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun et al., 2022b).

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Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Cited by 19 publications

References 51 publications

Effects of various seed priming on morphological, physiological, and biochemical traits of rice under chilling stress

Effects of various seed priming on morphological, physiological, and biochemical traits of rice under chilling stress

Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series

Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data

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