Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing

In recent years, the unmanned aerial vehicle (UAV) remote sensing system has been rapidly developed and applied in accurate estimation of crop parameters and yield at farm scale. To develop the major contribution of UAV multispectral images in predicting winter wheat leaf area index (LAI), chlorophyll content (called soil and plant analyzer development [SPAD]), and yield under different water treatments (low water level, medium water level, and high water level), vegetation indices (VIs) originating from UAV multispectral images were used during key winter wheat growth stages. The estimation performances of the models (linear regression, quadratic polynomial regression, and exponential and multiple linear regression models) on the basis of VIs were compared to get the optimal prediction method of crop parameters and yield. Results showed that LAI and SPAD derived from VIs both had high correlations compared with measured data, with determination coefficients of 0.911 and 0.812 (multivariable regression [MLR] model, normalized difference VI [NDVI], soil adjusted VI [SAVI], enhanced VI [EVI], and difference VI [DVI]), 0.899 and 0.87 (quadratic polynomial regression, NDVI), and 0.749 and 0.829 (quadratic polynomial regression, NDVI) under low, medium, and high water levels, respectively. The LAI and SPAD derived from VIs had better potential in estimating winter wheat yield by using multivariable linear regressions, compared to the estimation yield based on VIs directly derived from UAV multispectral images alone by using linear regression, quadratic polynomial regression, and exponential models. When crop parameters (LAI and SPAD) in the flowering period were adopted to estimate yield by using multiple linear regressions, a high correlation of 0.807 was found, while the accuracy was over 87%. Importing LAI and SPAD obtained from UAV multispectral imagery based on VIs into the yield estimation model could significantly enhance the estimation performance. This study indicates that the multivariable linear regression could accurately estimate winter wheat LAI, SPAD, and yield under different water treatments, which has a certain reference value for the popularization and application of UAV remote sensing in precision agriculture.

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Quadratic Polynomial Regression Using Serial Observation Processing: Implementation within DART

Monthly Weather Review ◽

10.1175/mwr-d-17-0089.1 ◽

2017 ◽

Vol 145 (11) ◽

pp. 4467-4479 ◽

Cited By ~ 1

Author(s):

Daniel Hodyss ◽

Jeffrey L. Anderson ◽

Nancy Collins ◽

William F. Campbell ◽

Patrick A. Reinecke

Keyword(s):

Kalman Filter ◽

Linear Regression ◽

Data Assimilation ◽

Polynomial Regression ◽

Quadratic Polynomial ◽

State Estimate ◽

Serial Data ◽

Serial Observation ◽

Hierarchy Of Models

It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.

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Winter wheat nutrition diagnosis under different N treatments based on multispectral images and remote sensing

10.1117/12.866213 ◽

2010 ◽

Cited By ~ 1

Author(s):

Ruijiao Zhao ◽

Minzan Li ◽

Shuqiang Li ◽

Yongjun Ding

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Multispectral Images ◽

Nutrition Diagnosis

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Modeling water level using downstream river water level observations and machine learning methods

E3S Web of Conferences ◽

10.1051/e3sconf/202016301009 ◽

2020 ◽

Vol 163 ◽

pp. 01009

Author(s):

Mikhail Sarafanov ◽

Eduard Kazakov ◽

Yulia Borisova

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Water Level ◽

Statistical Models ◽

Polynomial Regression ◽

Water Levels ◽

Learning Methods ◽

Machine Learning Methods ◽

Temperature And Precipitation ◽

Gauging Station

The article presents the results of the development of a model for calculating levels at one gauging station using the levels at another. To link the levels at two gauging stations, the data on levels, temperature and precipitation were used. The use of machine learning methods to solve the problem of predicting water levels made it possible to achieve an accuracy of about 6 cm. At the same time, traditional statistical models (linear regression, polynomial regression) have 14-16 cm error.

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Detecting the Cool Island Effect of Urban Parks in Wuhan: A City on Rivers

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010132 ◽

2020 ◽

Vol 18 (1) ◽

pp. 132

Author(s):

Qijiao Xie ◽

Jing Li

Keyword(s):

Remote Sensing ◽

Negative Impact ◽

Remote Sensing Data ◽

Urban Parks ◽

High Water ◽

Water Bodies ◽

Green Spaces ◽

Heat Island ◽

Island Effect ◽

Urban Heat

As a nature-based solution, development of urban blue-green spaces is widely accepted for mitigating the urban heat island (UHI) effect. It is of great significance to determine the main driving factors of the park cool island (PCI) effect for optimizing park layout and achieving a maximum cooling benefit of urban parks. However, there have been obviously controversial conclusions in previous studies due to varied case contexts. This study was conducted in Wuhan, a city with high water coverage, which has significant differences in context with the previous case cities. The PCI intensity and its correlation with park characteristics were investigated based on remote sensing data. The results indicated that 36 out of 40 urban parks expressed a PCI effect, with a PCI intensity of 0.08~7.29 °C. As expected, larger parks with enough width had stronger PCI intensity. An increased density of hardened elements in a park could significantly weaken PCI effect. Noticeably, in this study, water bodies in a park contributed the most to the PCI effect of urban parks, while the vegetated areas showed a negative impact on the PCI intensity. It implied that in a context with higher water coverage, the cooling effect of vegetation was weakened or even masked by water bodies, due to the interaction effect of different variables on PCI intensity.

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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽

10.3390/s21061994 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1994

Author(s):

Qian Ma ◽

Wenting Han ◽

Shenjin Huang ◽

Shide Dong ◽

Guang Li ◽

...

Keyword(s):

Remote Sensing ◽

Confusion Matrix ◽

Object Oriented ◽

Low Complexity ◽

Classification Model ◽

Recursive Feature Elimination ◽

Support Vector ◽

Multispectral Images ◽

High Complexity ◽

Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

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Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic

Agricultural Water Management ◽

10.1016/j.agwat.2021.107064 ◽

2021 ◽

Vol 256 ◽

pp. 107064

Author(s):

František Jurečka ◽

Milan Fischer ◽

Petr Hlavinka ◽

Jan Balek ◽

Daniela Semerádová ◽

...

Keyword(s):

Remote Sensing ◽

Czech Republic ◽

Water Balance ◽

Winter Wheat ◽

Spring Barley ◽

The Czech Republic

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Lake and inland dunes as interconnected Systems: The story of Lake Tresssee and an adjacent dune field (Schleswig-Holstein, North Germany)

The Holocene ◽

10.1177/0959683620981684 ◽

2020 ◽

pp. 095968362098168

Author(s):

Christian Stolz ◽

Magdalena Suchora ◽

Irena A Pidek ◽

Alexander Fülling

Keyword(s):

Water Level ◽

Bronze Age ◽

Environmental Changes ◽

High Water ◽

Alluvial Fan ◽

Water Levels ◽

Lake Area ◽

Dune Field ◽

The North ◽

Sand Drift

The specific aim of the study was to investigate how four adjacent geomorphological systems – a lake, a dune field, a small alluvial fan and a slope system – responded to the same impacts. Lake Tresssee is a shallow lake in the North of Germany (Schleswig-Holstein). During the Holocene, the lake’s water surface declined drastically, predominately as a consequence of human impact. The adjacent inland dune field shows several traces of former sand drift events. Using 30 new radiocarbon ages and the results of 16 OSL samples, this study aims to create a new timeline tracing the interaction between lake and dunes, as well, as how both the lake and the dunes reacted to environmental changes. The water level of the lake is presumed to have peaked during the period before the Younger Dryas (YD; start at 10.73 ka BC). After the Boreal period (OSL age 8050 ± 690 BC) the level must have undergone fluctuations triggered by climatic events and the first human influences. The last demonstrable high water level was during the Late Bronze Age (1003–844 cal. BC). The first to the 9th century AD saw slightly shrinking water levels, and more significant ones thereafter. In the 19th century, the lake area was artificially reduced to a minimum by the human population. In the dunes, a total of seven different phases of sand drift were demonstrated for the last 13,000 years. It is one of the most precisely dated inland-dune chronologies of Central Europe. The small alluvial fan took shape mainly between the 13th and 17th centuries AD. After 1700 cal. BC (Middle Bronze Age), and again during the sixth and seventh centuries AD, we find enhanced slope activity with the formation of Holocene colluvia.

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