scholarly journals Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

Sensors ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 81 ◽  
Author(s):  
Tongtong Wang ◽  
Zhiqiang Xiao ◽  
Zhigang Liu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Performance Evaluation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Learning Methods ◽  
Area Index ◽  
Machine Learning Methods ◽  
Reflectance Data

scholarly journals Leaf Area Index Estimation Model for UAV Image Hyperspectral Data Based on Wavelength Variable Selection and Machine Learning Methods

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Tao Cheng ◽  
Wei Guo ◽  
Xin Xu ◽  
Xinming Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Learning Methods ◽  
Successive Projections Algorithm ◽  
Area Index ◽  
Machine Learning Methods ◽  
Successive Projections

Abstract Background In order to accurately estimate leaf area index (LAI) of winter wheat by using unmanned aerial vehicle (UAV) hyperspectral imagery.Methods The UAV hyperspectral imaging data, alternating slice-wise diagonalization (ASD) spectral data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments.The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.Results Our results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information.The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. Our results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

scholarly journals Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Juanjuan Zhang ◽  
Tao Cheng ◽  
Wei Guo ◽  
Xin Xu ◽  
Hongbo Qiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Learning Methods ◽  
Successive Projections Algorithm ◽  
Area Index ◽  
Machine Learning Methods ◽  
Successive Projections

Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

scholarly journals Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2517 ◽  
Author(s):  
Huaan Jin ◽  
Weixing Xu ◽  
Ainong Li ◽  
Xinyao Xie ◽  
Zhengjian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Area Index ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Modis Lai ◽  
Temporal Profiles ◽  
Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

scholarly journals Sequential Method with Incremental Analysis Update to Retrieve Leaf Area Index from Time Series MODIS Reflectance Data

2014 ◽  
Vol 6 (10) ◽  
pp. 9194-9212 ◽  
Author(s):  
Jingyi Jiang ◽  
Zhiqiang Xiao ◽  
Jindi Wang ◽  
Jinling Song
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Incremental Analysis ◽  
Sequential Method ◽  
Area Index ◽  
Reflectance Data

Time series 30m resolution leaf area index estimation and vegetation change monitoring in Saihanba, China

2020 ◽  
Author(s):  
Hongmin Zhou ◽  
Guodong Zhang ◽  
Changjing Wang ◽  
Jindi Wang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dynamic Model ◽  
Support Vector ◽  
Growth Monitoring ◽  
Area Index ◽  
Modis Lai ◽  
Reflectance Data

<p>Leaf area index (LAI) is one of the most important biophysical variables for regulating the physiological processes of vegetation canopy. Time series high-resolution LAI data is critical for vegetation growth monitoring, surface process simulation and global change research. However, there are no high-resolution LAI data products that are continuous in time and space. In this paper, we use MODIS LAI products and Landsat surface reflectance data to generate time series high-resolution LAI datasets from 2000 to 2018 in Saihanba based on the ensemble kalman filter, and uses time-series LAI data to monitor surface vegetation changes according to the Prophet model. Firstly, the multi-step Savitzky–Golay filtering algorithm is used to smooth the MODIS LAI data, and the upper envelope of time series LAI is generated. A dynamic model is constructed according to the trend of LAI upper envelope to provide the short-range forecast of LAI. Then the ground measured LAI data and the corresponding Landsat reflectance data are used to train a Back Propagation neural network. High-resolution LAI data from BP model is used to update the dynamic model in real time to generate high-resolution time series LAI data based on EnKF. Finally, the time series LAI data is used as the input of Prophet deep learning model to obtain the LAI time series prediction values of a certain year. Comparing the prediction results with the LAI of current year, the correlation coefficient and the root mean square error distribution maps can be obtained, a support vector machine method is used to classify the disturbed pixels and the normal pixels. The LAI time series estimation has a high accuracy of R²larger than 0.90, and RMSE less than 0.54. The disturbance monitoring results indicate that vegetation in 2009, 2010, 2013, 2014, 2015, 2017 is seriously disturbed, Variation of meteorological conditions and deforest contributes heavily to the disturbance.</p>

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Spatiotemporal Patterns ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Machine Learning Methods ◽  
Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

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