Assessment of ENVISAT ASAR data for rice monitoring based on three years experiments

Zhao, Xiaoyan; Yang, Shuangyan; Shen, Suhung; BingBai, Li

doi:10.1109/rsete.2011.5964234

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…Focusing on the modelling approach, researchers used satellite imagery to feed yield crop software models such as ORYZA [ 30 , 31 , 47 , 62 , 104 , 111 , 131 ] or MAPscape-RICE [ 11 , 30 , 31 , 126 ]. ORYZA model ( , accessed on 23 March 2023) is a software belonging to the International Rice Research Institute (IRRI) aiming to model and compare the performance of simulated numbers with in situ observations.…”

Section: Resultsmentioning

confidence: 99%

“…If ML algorithms are gathered for consideration in this classification, supervised ML represents 80% (47) of the solutions and is preferred against unsupervised ML 20% (12). Focusing on the modelling approach, researchers used satellite imagery to feed yield crop software models such as ORYZA [30,31,47,62,104,111,131] or MAPscape-RICE [11,30,31,126]. ORYZA model (https://www.irri.org/oryza, accessed on 23 March 2023) is a software belonging to the International Rice Research Institute (IRRI) aiming to model and compare the performance of simulated numbers with in situ observations.…”

Section: Vegetation Index Formula Literature Number Referencesmentioning

confidence: 99%

“…Falling into the "other" category, often the approach followed used a remote sensingbased method with the application of vegetation indices to get phenology data for detecting paddy rice and using this information to feed a machine learning-based algorithm to map paddy fields or compare accuracies [24,83]. Focusing on the modelling approach, researchers used satellite imagery to feed yield crop software models such as ORYZA [30,31,47,62,104,111,131] or MAPscape-RICE [11,30,31,126]. ORYZA model (https://www.irri.org/oryza, accessed on 23 March 2023) is a software belonging to the International Rice Research Institute (IRRI) aiming to model and compare the performance of simulated numbers with in situ observations.…”

Section: Vegetation Index Formula Literature Number Referencesmentioning

confidence: 99%

See 2 more Smart Citations

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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