Monitoring of maize lodging using multi-temporal Sentinel-1 SAR data

Shu, Meiyan; Zhou, Longfei; Gu, Xiaohe; Ma, Yuntao; Sun, Qian; Yang, Guijun; Zhou, Chengquan

doi:10.1016/j.asr.2019.09.034

Cited by 55 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study,

and

were found to be sensitive to lodged rice (see Section 4.1 ). Similar conclusions were recorded when the C-band spaceborne SAR was used to monitor wheat and corn lodging [ 16 , 18 , 20 ]. Han et al also used VH and VV + VH as optimal polarization parameters to classify lodged corn with a different lodging degree [ 15 ].…”

Section: Resultssupporting

confidence: 74%

“…Previous investigations have shown that multiple polarimetric features have the ability to distinguish between lodging wheat and sugarcane from healthy crops based on Randarsat-2 fully polarimetric data [ 6 , 13 , 14 ]. By analyzing the sensitivity of polarimetric features extracted from Sentinel-1 to lodged corn, Han and Shu et al [ 15 , 16 ] constructed a model to classify lodged corn with different lodging angles. Chauhan et al [ 17 , 18 ] explored the ability of Radarsat-2 and Sentinel-1 data with multiple incidence angles to classify healthy wheat and lodged wheat with different crop angles of inclination (CAI).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Wang

Shao

et al. 2020

Sensors

View full text Add to dashboard Cite

Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

show abstract

“…In this study,

and

Section: Resultssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Wang

Shao

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A previous study shows that a high or low fertilizer level presents different NDVI mean values [ 45 ]. Moreover, lodging can also be caused by the growth environment, such as severe storms and rains [ 46 , 47 ]. In our experiment, lodging occurred at different stages of maturity for various rice lines and to different degrees, suggesting that precise yield prediction at early time points depends on growth conditions (such as fertilizer level or severe growth environment) and potentially vary between lines.…”

Section: Discussionmentioning

confidence: 99%

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Wang

Kohnen

et al. 2021

Plant Phenomics

View full text Add to dashboard Cite

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

show abstract

“…Our thorough review of the literature and the limited number of existing studies (Gu et al, 2019;Kumpumäki et al, 2018;Shu et al, 2019) have demonstrated that there is no conceptual framework nor methodology for using satellite-based RS images to assess crop lodging. This research addresses that gap.…”

Section: Introduction 11 the Need For Quantifying Crop Lodgingmentioning

confidence: 99%

“…They analysed the temporal backscatter trends of X-band scatterometer data to study lodging-induced changes in wheat. In a recent study, Shu et al (2019) investigated the potential of Sentinel-1 satellite data for quantitative assessment of maize lodging. The results showed that the VH/VV ratio was sensitive to nonlodged maize, while VV backscatter was sensitive to lodged maize.…”

Section: Introductionmentioning

confidence: 99%

Crop lodging detection and susceptibility mapping: a multi-sensor approach

Chauhan¹

View full text Add to dashboard Cite

show abstract

Monitoring of maize lodging using multi-temporal Sentinel-1 SAR data

Cited by 55 publications

References 27 publications

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Crop lodging detection and susceptibility mapping: a multi-sensor approach

Contact Info

Product

Resources

About