Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga

Miranda, Rodrigo de Queiroga; Galvíncio, Josiclêda Domiciano; Moura, Magna Soelma Beserra de; Jones, Charles; Srinivasan, Raghavan

doi:10.1155/2017/9314801

Cited by 27 publications

(17 citation statements)

References 37 publications

(58 reference statements)

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“…The MOD16A2 Net Evapotranspiration data product [27] is based on the retrieval of key parameters of the Penman-Monteith evapotranspiration formula from MODIS data [28]. A reliability study of this data product in dry, heterogenous forests against ground measurements yielded a strong correlation of r 2 = 0.82 [29], indicating the high overall reliability of the product.…”

Section: Satellite-derived Datamentioning

confidence: 99%

Machine Learning Regression Model for Predicting Honey Harvests

et al. 2020

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Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarivores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, Corymbia calophylla (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services.

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Section: Satellite-derived Datamentioning

confidence: 99%

Machine Learning Regression Model for Predicting Honey Harvests

et al. 2020

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“…Several institutions in the world using MODIS global evapotranspiration for providing evapotranspiration data, i.e., The European Space Agency (ESA), US Geological Survey (USGS), and Department of Civil Engineering -Indian Institute of Science. Some studies conducted by Mu et al (2011); Kim et al (2012); Shekar and Nandagiri (2016); Miranda et al (2017); and Aguilar et al (2018) show that MODIS global evapotranspiration product can estimate actual evapotranspiration with reasonable accuracy. Furthermore, Jiang et al (2004), Courault et al (2005), and Kalma et al (2008) show that deviation of MODIS global evapotranspiration is between 10% -to 30% compared to measurement and observation.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of MODIS Global Evapotranspiration Product as Satellite-Based Evapotranspiration Data for Supporting Precision Agriculture in West Papua - Indonesia

Faisol¹,

Indarto

Novita

et al. 2021

j Trop. Soils

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Precision Agriculture has been a significant issue since the middle of the 1980s. Evapotranspiration is one of the main parameters in precision agriculture to analyze real water needs in the agriculture area and managing water resources. Traditionally evapotranspiration estimates by directly measured methods, i.e., lysimeter, pan-evaporation, eddy covariance, Bowen ratio, soil water, and climate data analysis. These methods are expensive techniques with low spatial representativeness. The utilization of remote sensing technology is expected to be an alternative solution for providing evapotranspiration data with a cost-effective and high spatial representative. This research aims to evaluate the MODIS global evapotranspiration as satellite-based evapotranspiration in estimating evapotranspiration in West Papua. Four (4) statistical parameters, i.e., mean error (ME), root means square error (RMSE), relative bias (RB), and mean bias factor (MBF), are using for evaluation. The research showed that MODIS global evapotranspiration was overestimated in estimating evapotranspiration in West Papua. However, MODIS global evapotranspiration has an acceptable accuracy in estimating evapotranspiration in West Papua indicated by ME = 0.66 mm/day, RMSE = 0.94 mm/day, RB = 0.27, and MBF = 0.81. Therefore, MODIS global evapotranspiration can be used as an alternative solution for providing evapotranspiration data in West Papua with a cost-effective.

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“…Few well-documented cases have been developed to date at the scales comparable to those of this study [26,27], but there is still a lack of detailed groundwater flow models exploring the impact of evapotranspiration on the hydrogeological water budget in different climates. Recently, the Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration products have proven to be reliable and highly useful tools, because they are free downloadable datasets and consequently relatively easy to use by researchers and governments stakeholders [28]. They have been successfully used for multi-calibration purposes in several surface models [29,30], providing crucial information, particularly in ungauged basins.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact of Evapotranspiration at the Aquifer Scale via Groundwater Modelling and MODIS Data

Colombani

Gaiolini

Busico

et al. 2021

Water

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In shallow alluvial aquifers characterized by coarse sediments, the evapotranspiration rates from groundwater are often not accounted for due to their low capillarity. Nevertheless, this assumption can lead to errors in the hydrogeological balance estimation. To quantify such impacts, a numerical flow model using MODFLOW was set up for the Tronto river alluvial aquifer (Italy). Different estimates of evapotranspiration rates were retrieved from the online Moderate Resolution Imaging Spectroradiometer (MODIS) database and used as input values. The numerical model was calibrated against piezometric heads collected in two snapshots (mid-January 2007 and mid-June 2007) in monitoring wells distributed along the whole alluvial aquifer. The model performance was excellent, with all the statistical parameters indicating very good agreement between calculated and observed heads. The model validation was performed using baseflow data of the Tronto river compared with the calculated aquifer–river exchanges in both of the simulated periods. Then, a series of numerical scenarios indicated that, although the model performance did not vary appreciably regardless of whether it included evapotranspiration from groundwater, the aquifer–river exchanges were influenced significantly. This study showed that evapotranspiration from shallow groundwater accounts for up to 21% of the hydrogeological balance at the aquifer scale and that baseflow observations are pivotal in quantifying the evapotranspiration impact.

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Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga

Cited by 27 publications

References 37 publications

Machine Learning Regression Model for Predicting Honey Harvests

Machine Learning Regression Model for Predicting Honey Harvests

An Evaluation of MODIS Global Evapotranspiration Product as Satellite-Based Evapotranspiration Data for Supporting Precision Agriculture in West Papua - Indonesia

Quantifying the Impact of Evapotranspiration at the Aquifer Scale via Groundwater Modelling and MODIS Data

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