Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data

Cai, Yaotong; Lin, Hui; Zhang, Meng

doi:10.1016/j.asr.2019.08.042

Cited by 111 publications

(63 citation statements)

References 38 publications

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“…Additionally, in complex landscapes Sentinel-2 imagery was able to map sugarcane fields in China with over 90% of accuracy [50]. Regarding classification techniques, beside pixel based, object based has showed to be an accurate classification approach using Sentinel-2 imagery [51]. In this sense, object-based dynamic time warping was demonstrated to be more efficient in classifying crops than the pixel-based approach when using multitemporal Sentinel-2 imagery [52], nonetheless random forest seems to be more efficient for crop type classification when crop spectral variability is high [53].…”

Section: Sentinel-2 For Precision Agriculturementioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

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Section: Sentinel-2 For Precision Agriculturementioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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“…One of the issues or challenges in monitoring paddy rice growth using machine learning algorithms is to determine the optimum features combination. With optimum features combination, the overall accuracy of the classification results can be improved [115]. For instance, the optimum features combination can be achieved by using the robust adaptive spatial temporal fusion model (RASTFM) [116].…”

Section: ) Monitoring Paddy Rice Growthmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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“…It has been widely used in regional and large area rice mapping. For example, Cai Y et al used the random forest (RF) method based on Sentinel-2 MSI images, time-series NDVI, and phenological data to map rice with the overall accuracy and kappa coefficient were higher than 95% and 0.93, respectively [29]. Rad A M et al presented a new automatic rule-based method combining crop phenology-time normalized vegetation index and time series of Sentinel-2 imagery with the kappa coefficient of 0.70 [30].…”

Section: Introductionmentioning

confidence: 99%

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

et al. 2021

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Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

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Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data

Cited by 111 publications

References 38 publications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

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