Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm

Wang, Hongquan; Magagi, Ramata; Goı̈ta, Kalifa; Trudel, Mélanie; McNairn, Heather; Powers, Jarrett

doi:10.1016/j.rse.2019.111234

Cited by 72 publications

(46 citation statements)

References 24 publications

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“…Finally, micro/nanosatellites can provide data in several spectral bands (which are sensitive to the crop structure, leaf pigment, and water content), and they enable meter-levelresolution images for estimating crop phenotyping traits at a very large scale [24], [79], [87], [88], [144], [193], [236], [239]. Micro/nanosatellites include optical and synthetic aperture radar (SAR) satellites.…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

“…Compared with optical satellites, SAR satellites have some advantages for estimating crop phenotyping traits at high plant densities because SAR sensors use longer wavelengths, can penetrate crop canopies, and are not influenced by the presence of clouds and haze [88], [242]. For example, the backscatter and polarimetric decompositions of PolSAR are very sensitive to crop morphological structure changes [87]. SAR satellites have also been used to monitor crop phenology, yield, lodging, and so on through machine learning methods and polarimetric parameters [87], [143], [242].…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

“…For example, the backscatter and polarimetric decompositions of PolSAR are very sensitive to crop morphological structure changes [87]. SAR satellites have also been used to monitor crop phenology, yield, lodging, and so on through machine learning methods and polarimetric parameters [87], [143], [242]. More detail about the application of aerial phenotyping platforms is provided in the "Application of Ground and Aerial Phenotyping Platforms" section.…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

“…PolSAR is very sensitive to crop morphological structure changes [87]. RADARSAT-2 and TerraSAR-X provide opportunities to explore the potential of these data for crop SOS and EOS estimation.…”

Section: Phenological Parametersmentioning

confidence: 99%

“…Furthermore, they harnessed the three polarimetric parameters (entropy, anisotropy, and alpha angle) from polarimetric RADARSAT-2 data and hierarchical decision trees to estimate rice phenological parameters [89]. Wang et al [87] applied the multitemporal RADARSAT-2 dataset, SAR polarimetric decompositions, and an RF algorithm to monitor canola, maize, soybean, and wheat phenology based on crop phenological parameters. With the fast development of UAVs and sensors, near-real-time crop phenology retrieval is feasible.…”

Section: Phenological Parametersmentioning

confidence: 99%

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High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Jin

Zarco‐Tejada

Schmidhalter

et al. 2021

IEEE Geosci. Remote Sens. Mag.

195

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 0274-6638/20©2020IEEE IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MONTH 2020 C rop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields. BACKGROUND Worldwide demand for food will increase through 2050 and beyond due to the increasing global human population. This represents a huge challenge to crop researchers and agricultural policymakers because current yield gain rates will not be sufficient for the demands of population growth, while climate change will make the difficulty even greater. Today's DNA sequencing, marker-assisted breeding, transgenic technology, genome-wide association study (GWAS) approaches, and quantitative trait loci (QTL) identification have been applied, to a limited extent, to improve crop yields [1]-[4]. While it is now relatively easy to select for monogenic traits, current genome sequence datasets have not been sufficiently mined for more genetically complex (multigenic) performance characteristics, at least in part because of the lack of crop phenotypic information collected from real-world field situations. Furthermore, traditional crop growth analysis often involves destructive sampling that is time-consuming and prone to measurement error. At High-Throughput Estimation of Crop Traits A review of ground and aerial phenotyping platforms

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