SCHEDULING IRRIGATION IN HETEROGENEOUS CONTAINER NURSERY CROPS

Sensor-based management of container nursery crops irrigated with fresh or saline water

Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region. The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.

Crop coefficient, water requirements, yield and water use efficiency of sugarcane growth in Brazil

Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty

According to 230 flux site observations, intra-day and diurnal ground heat flux (G) accounted for 19.2 % and 28.8 % of the corresponding net radiation, respectively. This indicates that G plays an important role in remote sensing (RS) evapotranspiration (ET) models. The accuracy of the five G simulation methods in the surface energy balance-based RS ET models was evaluated using half-hourly observations. The linear coefficient (LC) method and the methods embedded with the normalized difference vegetation index (NDVI) were able to accurately simulate a half-hourly G series at most sites. The mean and median Nash-Sutcliffe efficiency (NSE) values of all sites were generally higher than 0.50 in each half-hour period. However, the two methods embedded by fractional vegetation coverage showed poor performance in most half-hour periods, except the sunrise and sunset periods, with the mean and median NSE values of all sites below 0.20. The accuracy of each method varied significantly at different sites and at half-hour intervals. The highest accuracy was exhibited during sunrise periods (6:00–7:00), followed by sunset periods (17:00–18:00). There were 92 % (211/230) sites with an NSE of the LC method greater than 0.50 at 6:30. It showed a slightly higher accuracy during night periods than during daytime periods. The lowest accuracy was observed at noon periods (10:00–15:30). The sites with an NSE exceeding 0.50 only accounted for 51 % (118/230) and 43 % (100/230) at 10:30 and 13:30, respectively. The accuracy of Northern Hemisphere sites was generally higher than that of Southern Hemisphere sites. In general, the highest and lowest accuracies were observed at the high- and low-latitude sites, respectively. The performance of the LC method and the methods embedded with NDVI were generally satisfactory at the Eurasian and North American sites, with the NSE values of most sites exceeding 0.70. Conversely, it exhibited relatively poor performance at the African, South American, and Oceanian sites, especially the African sites. Both the temporal and spatial distributions of the accuracy of the G simulation were positively correlated with the correlation between G and the net radiation. Although the G simulation methods accurately simulated the G series at most sites and time periods, their performance was poor at some sites and time periods. The application of RS ET datasets covering these sites requires caution. The optimal parameter value for each method varied greatly at different sites. This indicates that the fixed parameter values in the G simulation methods are not appropriate. Further improvement of G simulations at these sites and time periods is recommended for the RS ET modelers.

Understanding crop water requirements (CWR) in semi-arid region is essential for better irrigation practices, scheduling and efficient use of water since the water supply through rainfall is limited. This paper estimated the crop reference and actual evapotranspiration (Eto and ETc) respectively and the irrigation water requirement of rice (Oryza sativa L.) in Benin’s sub-basin of Niger River (BSBNR) of west Africa, using CROPWAT model. The long recorded climatic data, crop and soil data from 1942 to 2012 were computed with the Cropwat model which is based on the United Nations’ Food and Agriculture Organization (FAO) paper number 56 (FAO56). The Penman-Monteith method was used to estimate ETo. Crop coefficients (Kc) from the phenomenological stages of rice were applied to adjust and estimate the actual evapotranspiration ETc through a water balance of the irrigation water requirements (IR). The results showed the BSBNR annual reference evapotranspiration (ETo) was estimated at 1 967 mm. The lowest monthly value of ETo of 123 mm, was observed in August month, middle of the rainy season while the highest value 210 mm was observed in March within dry season. The crop evapotranspiration ETc and the crop irrigation requirements were estimated at 651 mm and 383 mm, respectively in rainy season and 920 mm and 1 148 mm, respectively within a dry season. Irrigation projects of these seasons can then be scheduled for water use efficiency based on these findings. Keywords: crop evapotranspiration, irrigation water requirement, rice, CROPWAT, model, Benin DOI: 10.3965/j.ijabe.20150802.1290

Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments.

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as Irrigation Principles. Soil and Water Conservation Engineera technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5 °C was found to be more appropriate than the literature value of 1.0 °C, but consideration of crop growth stage would further improve scheduling.

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest

Abstract. We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.

Evapotranspiration (ET) is a typical biological environmental process to influence leaf temperature, crop water requirement, and greenhouse microclimate. Affecting factors of ET include air temperature, air relative humidity, wind speed, solar radiation, longwave radiation, soil moisture, CO2 concentration, and crop state. In this study, two ET models of indoor cultivation commonly adopted in literature were selected to evaluate the effect of the performance of sensors on the model uncertainty. The method of the International Organization for Standardization, Guides to the expression of Uncertainty in Measurement (ISO GUM) was adopted. The result indicated that the performance of leaf area index (LAI) and solar radiation (Is) sensors were primary sources of uncertainty. The uncertainty of ET models due to sensor performance needs to be considered. To ensure the predictive ability for applying the ET model for crops irrigation management and greenhouse environmental control, the improvements in the measurement of environmental variables for calculating ET would be of particular importance. The method of this study can be used for evaluating the uncertainty of ET models that calculate ET based on environmental variables measured by meteorological sensors or the remote sensing technique.

Core Ideas Increased bias in ET due to various data sources where generally utilized standard weather data poses similar biases. Increased understanding of the complex relationship among the various weather input and vegetation indices. Results showed local weather station data closely represented site condition indicating the difficulties and challenges in ET modeling using other commonly available and widely adopted data sources. ABSTRACTComplex interactions among meteorological data and vegetation indices are incompletely understood in relation to evapotranspiration (ET) calculations for larger spatial domains with higher spatial and temporal resolution. Objectives of this study were to evaluate contributions of inputs to uncertainty in ET calculations and to enhance understanding of interactions among weather data, vegetative indices, and resistances utilized in biophysical ET model. We evaluated individual and combined effects of weather variables and vegetation indices using BAITSSS (Backward‐Averaged Iterative Two‐Source Surface temperature and energy balance Solution). Local weather station (LWS) data at a lysimeter site were obtained for irrigated corn (Zea mays L.) during the growing season (May to September, 2016) at Bushland, Texas. Gridded meteorological data were obtained from North American Land Data Assimilation System (NLDAS) (∼ 12.5 km) and remotely‐sensed vegetation indices (Landsat 30 m). Standard weather station (SWS) data were obtained from a grass reference near lysimeter site. The r2 and RMSE of ET simulated using LWS data and measured vegetation indices were 0.90 and 0.85 mm for daily ET, and 0.90 and 0.10 mm, for hourly ET, compared to lysimeter ET (less than 4% cumulative error). However, r2 and RMSE were 0.74 and 1.64 mm for daily ET, and 0.81 and 0.14 mm for hourly ET using gridded data, with positive bias (∼ 25% from NLDAS data). Simulated ET from SWS data exhibited similar behavior to gridded data with increased ET up to 21%. Results quantify difficulties in ET modeling using commonly available and widely adopted data sources.

Hydrological models have become an important tool for the efficient management of water resources. However, selection of appropriate models for evapotranspiration (ET) computations in river basins remains challenging to watershed managers, especially in data-scarce regions. The performance of Soil and Water Assessment Tool (SWAT)-based model for the prediction of potential and actual evapotranspiration (PET and AET) of OgunOshun river basin, Nigeria was investigated. Spatial and meteorological data was applied in setting up Mapwindow SWAT model. The three existing methods: Penman-Monteith, Priestly-Taylor (radiation-based) and Hargreaves (temperature-based), available in SWAT-were applied for the evaluation of PET and AET using soil, topographic, land-use and meteorological data as input parameters. The model results show a good correlation between the simulated and observed dataset as shown by Nasch-Sucliffe efficiency and coefficient of determination values. For the 30 year-simulation period, the predicted average PET values for PenmanMonteith, Priestley-Taylor and Hargreaves methods were 1791.516, 1684.597 and 1724.563 mm with corresponding standard deviation values of 89.322, 53.824 and 77.867 mm, respectively. The analysis indicated that Penman-Monteith and Hargreaves methods yielded almost the same results, while Priestly-Taylor method slightly differs, which establishes that it is not very suitable for arid/semi-arid regions. The study could be beneficial to watershed managers in addressing climate-related problems and for sustainable water resource management. KEYWORDS: SWAT, Potential evapotranspiration, Actual evapotranspiration, Watershed.

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.

Evaporation is the process whereby liquid water is converted to water vapor and removed from the evaporating surface. In hydrological practice, the estimation of evaporation can be achieved by direct or indirect methods. Direct method is based on the field measurements. Evaporation pan have also been used and compared to estimate evaporation by researchers (Choudhury, 1999; McKenzie and Craig, 2001; Vallet-Coulomb et al., 2001). The Class A evaporation pan is one of the most widely used instruments for the measurements of evaporation from a free water surface. The pan evaporation (PE) is widely used to estimate the evaporation of lakes and reservoirs (Finch, 2001). Many researchers have tried to estimate the evaporation through the indirect methods using the climatic variables, but some of these techniques require the data which cannot be easily obtained (Rosenberry et al., 2007). Evapotranspiration (ET) is the sum of volume of water used by vegetation, evaporated from the soil, and intercepted precipitation (Singh, 1988). ET plays an important role in our environment at global, regional, and local scales. ET is observed using a lysimeter directly or can be calculated using the water balance method or the climatic variables indirectly. Because the measurements of ET using a lysimeter directly, however, requires much unnecessary time and needs correct and careful experience, it is not always possible in field measurements. Thus, an empirical approach based on the climatic variables is generally used to calculate ET (Penman, 1948; Allen et al., 1989). In the early 1970s, the Food and Agricultural Organization of the United Nations (FAO), Rome, developed practical procedures to calculate the crop water requirements (Doorenbos & Pruitt, 1977), which have become the widely accepted standard for irrigation studies. A common practice for estimating ET from a well-watered agricultural crop is to calculate the reference crop ET such as the grass reference ET (ETo) or the alfalfa reference ET (ETr) from a standard surface and to apply an appropriate empirical crop coefficient, which accounts for the difference between the standard surface and the crop ET. Recently, the outstanding results using the neural networks model in the fields of PE and ET modeling have been obtained (Bruton et al., 2000; Sudheer et al., 2003; Trajkovic et al., 2003; Trajkovic, 2005; Keskin and Terzi, 2006; Kisi, 2006; Kisi, 2007; Zanetti et al., 2007; Jain et al., 2008; Kumar et al., 2008; Landeras et al., 2008; Kisi, 2009; Kumar et al., 2009; Tabari et al.,