Comparison of Penman–Monteith and non-linear energy balance approaches for estimating leaf wetness duration and apple scab infection

Leca, Alexandre; Parisi, Luciana; Lacointe, André; Saudreau, Marc

doi:10.1016/j.agrformet.2011.04.010

Cited by 16 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…energy limited) (Rana & Katerji, 1998). As Penman–Monteith is a linearized approximate solution, problems may occur in extreme conditions and errors in the soil evaporative term (Leca, Parisi, Lacointe, & Saudreau, 2011). Majozi et al (2017b) noted that Penman–Monteith methods need to include a SMC constraint.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of WaPOR V2 evapotranspiration products across Africa

Blatchford

Mannaerts

Njuki

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of WaPOR V2 evapotranspiration products across Africa

Blatchford

Mannaerts

Njuki

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…energy limited) (Rana & Katerji, 1998). As Penman-Monteith is a linearized approximate solution, problems may occur in extreme conditions and errors in the soil evaporative term (Leca, Parisi, Lacointe, & Saudreau, 2011). Majozi et al (2017b) For water-stressed crops, crop resistance errors can attribute to the large error in ETa estimations, while for tall crops, the VPD can have a large influence on the error (Rana & Katerji, 1998).…”

Section: Why Is Wapor Overestimating When Etia Is Low?mentioning

confidence: 99%

Untitled

2020

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Moreover, the absence of a standard protocol for the measurement of LWD leads to inconsistency in its observations from different observational networks. To overcome these limitations, LWD prediction models using standard meteorological variables such as air temperature (T air ), wind speed (WS), relative humidity (RH), and shortwave radiation have been suggested as alternatives for in-situ measurement [9][10][11][12]. These models utilize physical mechanisms and empirical relationships to predict LW using meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

et al. 2020

View full text Add to dashboard Cite

Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea.

show abstract

Comparison of Penman–Monteith and non-linear energy balance approaches for estimating leaf wetness duration and apple scab infection

Cited by 16 publications

References 24 publications

Evaluation of WaPOR V2 evapotranspiration products across Africa

Evaluation of WaPOR V2 evapotranspiration products across Africa

Untitled

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

Contact Info

Product

Resources

About