Zhu, Wanxue

[["dc.bibliographiccitation.artnumber","108582"],["dc.bibliographiccitation.journal","Field Crops Research"],["dc.bibliographiccitation.volume","284"],["dc.contributor.author","Zhu, Wanxue"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Sun, Zhigang"],["dc.contributor.author","Li, Jing"],["dc.contributor.author","Yu, Danyang"],["dc.contributor.author","Siebert, Stefan"],["dc.date.accessioned","2022-07-20T16:36:02Z"],["dc.date.available","2022-07-20T16:36:02Z"],["dc.date.issued","2022-08"],["dc.description.abstract","Unmanned aerial vehicle (UAV) remote sensing and machine learning have emerged as a practical approach with ultra-high temporal and spatial resolutions to overcome the limitations of ground-based sampling for continuous crop monitoring. However, little is known on the suitability of distinct sensing indices for different crop management and distinct crop development phases. In this study, we assessed the potential of the UAV-based modeling to monitor field-scale crop growth under different water and nutrient supply considering distinct phenological phases of maize. UAV multispectral observations were deployed over two long-term experimental sites in three growing seasons. Calibration and validation of the random forest model took place at the Nutrient Balance Experimental Site (NBES) and the Water Nitrogen Crop Relation Site (WNCR), respectively. Leaf area index, leaf chlorophyll concentration, and aboveground dry matter were measured at the jointing, heading, and grain filling phases of maize in 2018–2020. Our results revealed that the suitability of sensing indicators differed at distinct maize phenological phases. Overall, red edge, red edge reflectance ratio, and chlorophyll index green are the most appropriate UAV indicators for estimating maize growth variables. The random forest model developed and calibrated at NBES with nutrient supply detected the signal of nitrogen × irrigation interactions at the other experimental site (WNCR) in different development phases and years very well, suggesting that random forest models developed by UAV images of same spatial and spectral attributes could be transferred across sites with the same cultivar while different irrigation and fertilizer management. We conclude that the selected number of UAV detected indicators processed with a random forest model could be used for robustly estimating environment × management (fertilizer and irrigation) interactions on maize growth variables."],["dc.identifier.doi","10.1016/j.fcr.2022.108582"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112529"],["dc.language.iso","en"],["dc.relation.doi","10.1016/j.fcr.2022.108582"],["dc.relation.issn","0378-4290"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","23"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","14"],["dc.contributor.affiliation","Peng, Jinbang; 1Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"],["dc.contributor.affiliation","Rezaei, Ehsan Eyshi; 4Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany"],["dc.contributor.affiliation","Zhu, Wanxue; 5Department of Crop Sciences, University of Göttingen, Von-Siebold-Str. 8, 37075 Göttingen, Germany"],["dc.contributor.affiliation","Wang, Dongliang; 6Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"],["dc.contributor.affiliation","Li, He; 7State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"],["dc.contributor.affiliation","Yang, Bin; 3Shandong Dongying Institute of Geographic Sciences, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Dongying 257000, China"],["dc.contributor.affiliation","Sun, Zhigang; 1Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"],["dc.contributor.author","Peng, Jinbang"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Zhu, Wanxue"],["dc.contributor.author","Wang, Dongliang"],["dc.contributor.author","Li, He"],["dc.contributor.author","Yang, Bin"],["dc.contributor.author","Sun, Zhigang"],["dc.date.accessioned","2022-12-07T15:55:14Z"],["dc.date.available","2022-12-07T15:55:14Z"],["dc.date.issued","2022-11-23"],["dc.date.updated","2022-12-07T15:13:54Z"],["dc.description.abstract","Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers."],["dc.description.sponsorship","Strategic Priority Research Program of the Chinese Academy of Sciences"],["dc.description.sponsorship","National Key Research and Development Program of China"],["dc.description.sponsorship","National Natural Science Foundation of China"],["dc.description.sponsorship","Program of Yellow River Delta Scholars"],["dc.identifier.doi","10.3390/rs14235923"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118487"],["dc.language.iso","en"],["dc.relation.eissn","2072-4292"],["dc.rights","CC BY 4.0"],["dc.title","Plant Density Estimation Using UAV Imagery and Deep Learning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4716"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Zhu, Wanxue"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Yang, Ting"],["dc.contributor.author","Li, Binbin"],["dc.contributor.author","Gong, Huarui"],["dc.contributor.author","Lyu, Yun"],["dc.contributor.author","Peng, Jinbang"],["dc.contributor.author","Sun, Zhigang"],["dc.contributor.editor","Zhu, Wenquan"],["dc.date.accessioned","2022-01-11T14:07:54Z"],["dc.date.available","2022-01-11T14:07:54Z"],["dc.date.issued","2021"],["dc.description.abstract","Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes."],["dc.description.abstract","Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes."],["dc.identifier.doi","10.3390/rs13224716"],["dc.identifier.pii","rs13224716"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97887"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]