Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. © 2016 by the authors

Evapotranspiration Modeling and Forecasting for Efficient Management of Irrigation Command Areas

It has become very crucial to manage water resources to meet the needs of the growing population. In irrigation command areas, and in order to build a better plan to manage service delivery from canals and reservoirs, it is important to build appropriate knowledge of water needs on a field basis. There is often a lag between the order and delivery of water to the field. Knowledge of the crop water requirement at the field level helps the decision maker to make the right choices leading to more efficient handling of the available water. The purpose of this study was to develop methodologies and tools that allow better management of irrigation water and water delivery systems, such as machine learning models that can be used as tools for decision support systems of water management. To achieve better modeling and prediction, wavelet decompositions were explored for their ability to give information about time and frequency changes in the data. Remote sensing approaches were also used for their ability to quantify water requirements at the spatial level. Therefore, this dissertation explored the use of the above-mentioned data tools and techniques to address water management problems. The framework of this dissertation consisted of three components that provide tools to support irrigation system operational decisions. In general, the results for each of the methods developed were satisfactory, relevant, and encouraging. They provided significant potential for improving decision making for real-time applications in irrigation command areas and better management of the water resources

Discussion of Assessment of Reference Evapotranspiration by the Hargreaves Method in the Bekaa Valley, Lebanon by Roula Bachour, Wynn R. Walker, Alfonso F. Torres-Rua, and Mac McKee

Martí Pérez, PC.; Royuela, A.; González Altozano, P. (2015). Discussion of Assessment of Reference Evapotranspiration by the Hargreaves Method in the Bekaa Valley, Lebanon by Roula Bachour, Wynn R. Walker, Alfonso F. Torres-Rua, and Mac McKee. Journal of Irrigation and Drainage Engineering. 141(6). doi:10.1061/(ASCE)IR.1943-4774.0000646S141

Automated evapotranspiration retrieval model with missing soil-related datasets: The proposal of SEBALI

International audiencePrecision Agriculture (PA) has been booming lately in alignment with the proposal of several surface energy balance algorithms. The Surface Energy Balance Algorithm for Land (SEBAL) remains one of the most validated and implemented systems worldwide. This model enables the estimation of Evapotranspiration (ET) in different vegetation settings. In Lebanon, winter cereals, including wheat, are arguably the most important crop types as they enter directly into the Lebanese diet. Yet, no recent studies were produced to estimate their water consumption, particularly with the pressing global warming trend. In this paper, a developed version of the open source SEBAL python script (i.e. Py-SEBAL), surnamed SEBAL-Improved or SEBALI, was proposed to estimate evapotranspiration for winter cereals (i.e. Wheat, Barley, Triticale) in the Bekaa plain of Lebanon with missing soil-related datasets. Main enhancements of SEBALI over py-SEBAL concern the ability to choose a random shape for the study site, the selection of Hot/Cold pixels over agricultural areas only, thus better selection process, as well as the usage of atmospherically corrected satellite images. More importantly, ET rates could be assessed in regions lacking soil-related datasets, due to the usage of the Water stress (Ws) factor. Between November 2017 and June 2018, results show that the highest ET values were in May (i.e. flowering and grain filling growth stage), with seasonal ET values significantly varying (e.g. more than 600 mm in some regions) irrespective of the spatial location of the parcels. These results were explained by the different agricultural practices, and, to a lower extent, the varied precipitations within the study area. These outputs coupled with the produced ET cereal trend could assist decision makers as well as farmers and land owners to forecast their water consumptions and to increase their yields while conserving water resources, thus enhancing the water usage efficiency. The proposed surface energy balance algorithm (i.e. SEBALI) could be portable to other climatic regions, particularly when soil-related datasets are lacking

Estimation of Spatially Distributed Evapotranspiration using Remote Sensing and a Relevance Vector Machine

With the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient (EE) of 0.84 and a root-mean-squared error (RMSE) of 0.5mmday−10.5  mm day−1. This methodology lays the groundwork for estimating ET at a spatial scale for the days when a satellite image is not available. It could also be used to forecast daily spatial ET if the vegetation indexes model inputs are extrapolated in time and the reference ET is forecasted accurately. Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)IR.1943-4774.000075

Wavelet-Multivariate Relevance Vector Machine Hybrid Model for Forecasting Daily Evapotranspiration

Evapotranspiration (ET) is one of the main components of the hydrological cycle. It is a complex process driven mainly by weather parameters, and as such, is characterized by high non-linearity and non-stationarity. This paper introduces a methodology combining wavelet multiresolution analysis with a machine learning algorithm, the multivariate relevance vector machine (MVRVM), in order to predict 16 days of future daily reference evapotranspiration (ETo). This methodology lays the ground for forecasting the spatial distribution of ET using Landsat satellite imagery, hence the choice of 16 days, which corresponds with the Landsat overpass cycle. An accurate prediction of daily ETo is needed to improve the management of irrigation schedules as well as the operations of water supply facilities like canals and reservoirs. In this paper, various wavelet decompositions were performed and combined with MVRVM to develop hybrid models to predict ETo over a 16-days period. These models were compared to a MVRVM model, and models accuracy and robustness were evaluated. The addition of 10 days of forecasted air temperature as additional inputs to the forecasting models was also investigated. The results of the wavelet-MVRVM hybrid modeling methodology showed that a reliable forecast of ETo up to 16 days ahead is possible