ArticleFrontiers in plant science2022
Field-scale rice yield estimation based on UAV-based MiniSAR data with Ku band and modified water-cloud model of panicle layer at panicle stage.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.PloS one · 2024Article
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Abstract
Scientific and accurate estimation of rice yield is of great significance to food security protection and agricultural economic development. Due to the weak penetration of high frequency microwave band, most of the backscattering comes from the rice canopy, and the backscattering coefficient is highly correlated with panicle weight, which provides a basis for inversion of wet biomass of rice ear. To solve the problem of rice yield estimation at the field scale, based on the traditional water cloud model, a modified water-cloud model based on panicle layer and the radar data with Ku band was constructed to estimate rice yield at panicle stage. The wet weight of rice ear scattering model and grain number per rice ear scattering model were constructed at field scale for rice yield estimation. In this paper, the functional area of grain production in Xiashe Village, Xin'an Town, Deqing County, Zhejiang Province, China was taken as the study area. For the first time, the MiniSAR radar system carried by DJI M600 UAV was used in September 2019 to obtain the SAR data with Ku band under polarization HH of the study area as the data source. Then the rice yield was estimated by using the newly constructed modified water-cloud model based on panicle layer. The field investigation was carried out simultaneously for verification. The study results show: the accuracies of the inversion results of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel B were 95.03% and 94.15%; and the accuracies of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel C+D+E were over 91.8%. In addition, different growth stages had effects on yield estimation accuracy. For rice at fully mature, the yield estimation accuracies of wet weight of ear and grain number per ear were basically similar, both exceeding 94%. For rice at grouting stage, the yield estimation accuracy of wet weight of ear was 92.7%, better than that of grain number per ear. It was proved that it can effectively estimate rice yield using the modified water-cloud model based on panicle layer constructed in this paper at panicle stage at field scale.
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