Estimation of crop evapotranspiration from MODIS data by combining random forest and trapezoidal models

2022 ◽  
Vol 259 ◽  
pp. 107249
Author(s):  
Pengyu Hao ◽  
Liping Di ◽  
Liying Guo
Keyword(s):  
Random Forest ◽  
Crop Evapotranspiration ◽  
Modis Data

scholarly journals Splitting and Length of Years for Improving Tree-Based Models to Predict Reference Crop Evapotranspiration in the Humid Regions of China

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3478
Author(s):  
Xiaoqiang Liu ◽  
Lifeng Wu ◽  
Fucang Zhang ◽  
Guomin Huang ◽  
Fulai Yan ◽  
...  
Keyword(s):  
Random Forest ◽  
Southern China ◽  
Irrigation Scheduling ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Crop Evapotranspiration ◽  
Data Splitting ◽  
Reference Crop Evapotranspiration ◽  
Extreme Gradient Boosting ◽  
Reference Crop

To improve the accuracy of estimating reference crop evapotranspiration for the efficient management of water resources and the optimal design of irrigation scheduling, the drawback of the traditional FAO-56 Penman–Monteith method requiring complete meteorological input variables needs to be overcome. This study evaluates the effects of using five data splitting strategies and three different time lengths of input datasets on predicting ET0. The random forest (RF) and extreme gradient boosting (XGB) models coupled with a K-fold cross-validation approach were applied to accomplish this objective. The results showed that the accuracy of the RF (R2 = 0.862, RMSE = 0.528, MAE = 0.383, NSE = 0.854) was overall better than that of XGB (R2 = 0.867, RMSE = 0.517, MAE = 0.377, NSE = 0.860) in different input parameters. Both the RF and XGB models with the combination of Tmax, Tmin, and Rs as inputs provided better accuracy on daily ET0 estimation than the corresponding models with other input combinations. Among all the data splitting strategies, S5 (with a 9:1 proportion) showed the optimal performance. Compared with the length of 30 years, the estimation accuracy of the 50-year length with limited data was reduced, while the length of meteorological data of 10 years improved the accuracy in southern China. Nevertheless, the performance of the 10-year data was the worst among the three time spans when considering the independent test. Therefore, to improve the daily ET0 predicting performance of the tree-based models in humid regions of China, the random forest model with datasets of 30 years and the 9:1 data splitting strategy is recommended.

scholarly journals Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA

2015 ◽  
Vol 7 (5) ◽  
pp. 5347-5369 ◽  
Author(s):  
Pengyu Hao ◽  
Yulin Zhan ◽  
Li Wang ◽  
Zheng Niu ◽  
Muhammad Shakir
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Random Forest ◽  
Modis Data ◽  
Early Crop ◽  
Crop Classification ◽  
Selection Of

scholarly journals Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 175
Author(s):  
Hiroki Mizuochi ◽  
Yoshihiro Iijima ◽  
Hirohiko Nagano ◽  
Ayumi Kotani ◽  
Tetsuya Hiyama
Keyword(s):  
Surface Water ◽  
Random Forest ◽  
Water Monitoring ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Spatiotemporal Resolution ◽  
Heterogeneous Distribution ◽  
Modis Data ◽  
Adversarial Networks ◽  
The Impact

Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Land cover classification for the monitoring of winter grain crops within the Kyiv oblast and Mykolaiv oblast (Ukraine) with the use of MODIS data

2009 ◽  
Vol 15 (5) ◽  
pp. 16-23
Author(s):  
O.I. Sakhatsky ◽  
◽  
G.M. Zholobak ◽  
A.A. Makarova ◽  
O.A. Apostolov ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Grain Crops ◽  
Modis Data

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio
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