Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data

Filgueiras, Roberto; Almeida, Thomé Simpliciano; Mantovani, Everardo Chartuni; Dias, Santos Henrique Brant; Filho, Elpı́dio Inácio Fernandes; Cunha, Fernando França da; Venâncio, Luan Peroni

doi:10.1016/j.agwat.2020.106346

Cited by 49 publications

(26 citation statements)

References 57 publications

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“…Leaf nodes contain multivariate linear regression models that allow for a certain degree of extrapolation. Compared to other regression tree or random forest models, Cubist has high interpretability and performs similarly well in many remote sensing and Earth system science studies (Filgueiras et al 2020 ; Kumar et al 2021 ).…”

Section: Methodsmentioning

confidence: 98%

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

et al. 2022

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Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

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Section: Methodsmentioning

confidence: 98%

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

et al. 2022

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“…On the Other hand, Filgueiras et al (2020) tested six regression algorithms to predict evapotranspiration and soil water content from remote sensing images. Among them, Random Forest had the best performance for soil water prediction and the Cubist algorithm for evapotranspiration.…”

Section: Regression Algorithmsmentioning

confidence: 99%

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Alvim¹,

Guimarães

Sousa

et al. 2022

Eng. Agríc.

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A literature review on artificial intelligence in irrigation management was performed, using the Systematic Literature Review (SLR) method with explicit search criteria. More than 45,000 complete titles in 130 reference bases were consulted at once. A total of 38 primary studies were selected, which formed the basis of this review. The findings showed increasing use of Artificial Neural Networks (ANN) fed with climate and soil sensor data for irrigation management solutions. ANNs have been the most popular choice for solutions that require machine learning techniques. Fuzzy-logic-based technologies stood out in Decision Support Systems (SSD). Hybrid neuro-fuzzy approaches manage the best aspects contained in each of the two techniques (ANN and fuzzy logic). Moreover, autonomous wireless and networked sensors have been the most often used. Good chances of developing solutions for irrigation management point to the growing application of ANN-based machine learning, Support Vector Machine (SVM), and Random Forests techniques, using wireless sensor networks and computer vision with remote sensing images.

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“…In most cases, the proposed resolution methods are developed with small experimental plots, and because of this, they are given for a particular climatic, geographical and soil type context. Thus they may not be applied directly in commercial agricultural fields [32]. Thus, it is still necessary to develop simple models that are executable in the practice of irrigated agriculture and at the same time are developed for large areas of agricultural crops [32].…”

Section: Introductionmentioning

confidence: 99%

“…Thus they may not be applied directly in commercial agricultural fields [32]. Thus, it is still necessary to develop simple models that are executable in the practice of irrigated agriculture and at the same time are developed for large areas of agricultural crops [32].…”

Section: Introductionmentioning

confidence: 99%

Short term soil moisture forecasts for potato crop farming: A machine learning approach

Dubois

Teytaud

Vérel

2021

Computers and Electronics in Agriculture

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Agricultural decision-making is crucial for future yields. In the context of smart farming, grower combine information from sensors located close to their crops with agronomic models to help them to better understand their crops. Irrigation management is therefore based on extrapolation of data and/or agronomic model responses. This problem can be seen as a learning task for which machine learning techniques have proven their relevance in many and diverse applications. In this paper we place ourselves in the context of potato farming, a crop for which irrigation plays a crucial role. We model the problem of soil water potential prediction as a learning problem solved by supervised leaning algorithms. The problem appears to be difficult since there are several potential inputs, and several outputs to predict. Experiments are conducted on several scenarios with data acquired during 3 years. We demonstrate the possibility of applying feature selection method to automatically design models with features relevant to the problem at hand while having good performances. We have also demonstrated the relevance of the machine learning for this kind of problem, since the methods are able to correctly predict the next water potential values.

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Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data

Cited by 49 publications

References 57 publications

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Short term soil moisture forecasts for potato crop farming: A machine learning approach

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