A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

Amani, Shima; Shafizadeh-Moghadam, Hossein

doi:10.1016/j.agwat.2023.108324

Cited by 19 publications

(7 citation statements)

References 153 publications

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“…This observation specifies that the meteorological parameter wind speed ( ) significantly contributes to estimating the across the targeted study region. Similar observations were reported by Traore, Wang & Kerh (2010) in the Sudano-Sahelian zone of Burkina Faso, Yang et al (2019) in China, and the literature survey conducted by Amani & Shafizadeh-Moghadam (2023) . Although the complete set of five input parameters provided the highest estimation accuracy for , fairly good performance was achieved with the other three input combinations, , , and .…”

Section: Results Analysissupporting

confidence: 88%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

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Section: Results Analysissupporting

confidence: 88%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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“…The application of ML methods based on the Artificial Neural Networks (ANNs) applied to Remote Sensing (RS) data processing considerably increases the effectiveness of mapping [56,57]. Advanced ML methods enable the landscape dynamics of spatial and temporal trends to be automatically revealed through computer-based algorithms of pattern recognition and data analysis, as reported in existing studies [58][59][60][61][62]. Several ML and AI algorithms exist to analyze and quantify spatial data using analytical and empirical approaches.…”

Section: Theoretical Framework and Motivationmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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Section: Inversion Of Etc From Citrus Orchardmentioning

confidence: 99%

“…Using the modified RSEB model (0.64 RSEB) in a time series, the four trees were divided into a group of nine sets of three different treatments, and the results of the inversion of ETc from citrus fields in the top slope treatment (No. 13 The inversion of ET c of citrus on different slopes was realized by using UAV multispectral remote sensing with the RSEB model and simple corrections. It was evident that the ET c of crops under the same treatment was generally consistent and decreased with time, with some variations in specific values.…”

Section: Inversion Of Etc From Citrus Orchardmentioning

confidence: 99%

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Multispectral Inversion of Citrus Multi-Slope Evapotranspiration by UAV Based on Modified RSEB Model

Zhu,

Zhang,

Duan

et al. 2024

Water

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Evaptotranspiration (ETc) is a crucial link in the farmland water cycle process. To accurately obtain the citrus ETc in different slope positions, the METRIC, RSEB, and FAO Penman–Monteith (P-M) models were constructed based on unmanned aerial vehicle (UAV) multispectral images to invert the ETc values. The ETc of citrus calculated by the P-M model was used as a reference standard, and the accuracy of the ETc inversion was evaluated by the METRIC model and the RSEB model. The results showed that the R2, RMSE, and SE of the METRIC model and the RSEB model were 0.396 and 0.486, 4.940 and 3.010, and 4.570 and 2.090, respectively, indicating a higher accuracy of the RSEB model for inverting the ETc values. Furthermore, the accuracy of the RSEB model could be improved by introducing the optimal correction coefficient (after correction: RMSE = 1.470, SE = 0.003). Based on the modified RSEB model, the ETc values of the citrus in different slope positions were obtained. We also found that the middle slope ETc > the top slope ETc > the bottom slope ETc, indicating that the slope position indeed affected the citrus ETc. This research provides a favorable framework for the ETc inversion, and the results are of theoretical and practical importance to realize crop water conservation.

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A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

Cited by 19 publications

References 153 publications

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Multispectral Inversion of Citrus Multi-Slope Evapotranspiration by UAV Based on Modified RSEB Model

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