Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Du, Meiqi; Huang, Jingfeng; Wei, Pengliang; Yang, Lingbo; Chai, Douglas; Peng, Dailiang; Sha, Jinming; Sun, Weiwei; Huang, Ran

doi:10.3390/agronomy12071583

Cited by 12 publications

(8 citation statements)

References 67 publications

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“…From 2017 there is a spike in the number of published articles, with a drop in 2021. The years 2020 [ 5 , 8 , 9 , 26 , 43 , 47 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] and 2022 (until 20 October) [ 2 , 6 , 18 , 28 , 39 , 42 , 44 , 49 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ] showed a higher number of articles published, 20 per year, which represents 16% of the total, 32% combined.…”

Section: Resultsmentioning

confidence: 99%

“…In most cases, authors also calculated the user’s accuracy (UA), which is the accuracy from the point of view of the map user, and the producer’s accuracy (PA), which is the map accuracy from the map maker’s point of view. This technique was chosen by the authors when they had digital data as reference and to validate with an existing official land cover map [ 2 , 4 , 10 , 11 , 15 , 22 , 23 , 30 , 56 , 65 , 85 , 86 , 98 , 99 , 103 , 119 , 135 ] and ground-truth data acquired [ 2 , 14 , 22 , 25 , 73 , 77 , 81 , 83 , 84 , 87 , 95 , 101 , 105 , 112 , 113 , 117 ] or to sample using photointerpretation of a very high-resolution image (e.g., Google Earth imagery) [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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“…To be specific, semantic segmentation technology [20] is capable of analyzing the deep semantic information of images and conducting pixel-level supervised classification [21] quickly, which has been favored by many scholars. For instance, Du et al [22] extracted rice from Arkansas using a semantic segmentation model U-net based on timeseries Landsat imagery and the Cropland Data Layer (CDL). Rice could be identified in the heading stage with an overall accuracy of 0.86.…”

Section: Introductionmentioning

confidence: 99%

Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images

et al. 2022

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The Hetao Plain and Xing’an League are the major cultivated areas and main grain-producing areas in Inner Mongolia, and their crop planting structure significantly affects the grain output and economic development in Northern China. Timely and accurate identification, extraction, and analysis of typical crops in Xing’an League and Hetao Plain can provide scientific guidance and decision support for crop planting structure research and food security in ecological barrier areas in Northern China. The pixel samples and the neighborhood information were fused to generate a spectral spatial dataset based on single-phase Sentinel-2 images. Skcnn_Tabnet, a typical crop remote sensing classification model, was built at the pixel scale by adding the channel attention mechanism, and the corn, sunflower, and rice in the Hetao Plain were quickly identified and studied. The results of this study suggest that the model exhibits high crop recognition ability, and the overall accuracy of the three crops is 0.9270, which is 0.1121, 0.1004, and 0.0874 higher than the Deeplabv3+, UNet, and RF methods, respectively. This study confirms the feasibility of the deep learning model in the application research of large-scale crop classification and mapping and provides a technical reference for achieving the automatic national crop census.

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“…Synergy of cloud computing and deep learning. The deep learning algorithms that have emerged in the last decade have improved rice extraction on complex surfaces and in fragmented landscapes by building a moderate number of neuronal computation nodes and multi-layer operational hierarchies with higher classification accuracy compared to traditional machine learning algorithms [91,92]. However, more complex model structures would also require better hardware performance, longer training times, and a larger number of data labels [93].…”

mentioning

confidence: 99%

High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data

Huang

You²,

Liu³

et al. 2023

Remote Sensing

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Rice has always been one of the major food sources for human beings, and the monitoring and planning of cultivation areas to maintain food security and achieve sustainable development is critical for this crop. Traditional manual ground survey methods have been recognized as being laborious, while remote-sensing technology can perform the accurate mapping of paddy rice due to its unique data acquisition capabilities. The recently emerged Google Earth Engine (GEE) cloud-computing platform was found to be capable of storing and computing the resources required for the rapid processing of massive quantities of remote-sensing data, thereby revolutionizing traditional analysis patterns and offering unique advantages for large-scale crop mapping. Since the phenology of paddy rice depends on local climatic conditions, and considering the vast expanse of China with its outstanding geospatial heterogeneity, a zoning strategy was proposed in this study to separate the monsoon climate zone of China into two regions based on the Qinling Mountain–Huaihe River Line (Q-H Line), while discrepant basic data and algorithms have been adopted to separately map mid-season rice nationwide. For the northern regions, optical indices have been calculated based on Sentinel-2 images, growth spectral profiles have been constructed to identify phenological periods, and rice was mapped using One-Class Support Vector Machine (OCSVM); for the southern regions, microwave sequences have been constructed based on Sentinel-1 images, and rice was mapped using Random Forest (RF). By applying this methodological system, mid-season rice at 10 m spatial resolution was mapped on the GEE for the entire Chinese monsoon region in 2021. According to the accuracy evaluation coefficients and publicly released local statistical yearbook data, the relative error of the mapped areas in each province was limited to 10%, and the overall accuracy exceeded 85%. The results could indicate that mid-season rice can be mapped more accurately and efficiently on a China-wide scale with relatively few samples based on the proposed zoning strategy and mapping methods. By adjusting the parameters, the time interval for mapping could also be further extended. The powerful cloud-computing competence of the GEE platform was used to map rice on a large spatial scale, and the results can help governments to ascertain the distribution of mid-season rice across the country in a short-term period, which would be well suited to meeting the increasingly efficient and fine-grained decision-making and management requirements.

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Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Cited by 12 publications

References 67 publications

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images

High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data

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