Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data

Pereira, Luís S.; Paredes, Paula; Melton, F. S.; Johnson, Lee F.; Wang, T.; López-Urrea, R.; Cancela, J.J.; Allen, Richard G.

doi:10.1016/j.agwat.2020.106197

Cited by 72 publications

(66 citation statements)

References 106 publications

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“…This study incorporated numerous spectral features computed using the blue, green, red, rededge and near infrared bands (e.g., indices available at [57][58][59][60]), structural features (e.g., available at [44,61]) and canopy fraction cover [62], as well as a number of custom/adopted features (e.g., cummulative NDVI, canopy width, canopy area and canopy volume above cordon). A correlation analysis of each spectral and structural feature was performed with respect to the ground measured k c for all the timepoints combined.…”

Section: Spectral and Structural Feature Selectionmentioning

confidence: 99%

Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

2021

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Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (kc). The ET-driven irrigation decisions for grapevines rely on the sampling of several kc values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate kc at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model kc using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with kc throughout the season (p ≤ 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post véraison—the onset of ripening in grapes. Combining structural and spectral information improved the model’s performance. Among the investigated predictive models, the random forest predicted kc with the highest accuracy (R2: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of kc within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of kc can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.

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Section: Spectral and Structural Feature Selectionmentioning

confidence: 99%

Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

2021

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“…This approach is similar to Hughes et al (2013), which also incorporated stochasticity in terms of irrigation requirements distributed in time, in Dutta et al (2017), and in HYPE, the reference hydrological model for Sweden (SMHI, 2020c). Johnson et al (2016) analysed the differences between irrigation following standard practices and irrigation based on decision support models that incorporate information on ET, such as the NASA SIMS model (Melton et al, 2012;Pereira et al, 2020). Based on two years of field data collected for head lettuce and broccoli using a randomized block design, the authors found that standard practices lead to an increase in water consumption between 26 and 40% for lettuce and between 39 and 51% for broccoli, as compared to ET-based irrigation management strategies based on data from SIMS.…”

Section: Irrigation Management Scenariosmentioning

confidence: 99%

“…For this reason, researchers and institutions have been active in developing decision support systems based on simulation and optimization models, or satellite data observations, or hybrid approaches to provide guidelines on optimal irrigation and nutrient applications and thus inform smart farming management. The NASA Satellite Irrigation Management Support (SIMS) system (Melton et al, 2012;Pereira et al, 2020) integrates Earth observation data from Landsat and MODIS with meteorological observations to generate daily maps of evapotranspiration (ET) and 8-day map of crop coefficients for millions of hectares of farmland in the western United States (US). OpenET (OpenET, 2021) has implemented six satellite-based ET models on the Google Earth Engine platform (Gorelick et al, 2017) to make daily, monthly and annual ET data for the western US easily available at field scales (30m x 30m per pixel) via a web-based UI and application programming interface (API).…”

Section: Introductionmentioning

confidence: 99%

Towards an operational irrigation management system for Sweden with a water-food-energy nexus perspective

Campana¹,

Lastanao²,

Zainali³

et al. 2021

Preprint

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The 2018 drought in Sweden has triggered questions about climate adaptation and mitigation measures, especially in the agricultural sector, which suffered the most. This study applies a water-food-energy nexus modelling framework to evaluate drought impacts on irrigation and agriculture in Sweden using 2018 and 2019 as case studies. A previous water-food-energy nexus model was updated to facilitate an investigation of the benefits of data-driven irrigation scheduling as compared to existing irrigation guidelines. Moreover, the benefits of assimilating earth observation data in the crop model have been explored. The assimilation of leaf area index data from the Copernicus Global Land Service significantly improve the crop yield estimation as compared to default crop model parameters. The results show that the irrigation water productivities of the proposed model are measurably improved compared to conventional irrigation guidelines both in 2018 and in 2019. This is mostly due to the advantage of the proposed model in providing ETc-driven guidelines by using spatially-explicit data generated by mesoscale models from the Swedish Meteorological and Hydrological Institute. During a normal year, the proposed irrigation management system leads to significant water savings as compared to conventional irrigation guidelines. The modelling results show that temperature stress during the 2018 drought played a key role in the crop yield reduction, with yield reductions of up to 35%. In the context of the water-food-energy nexus, this motivates the implementation of new technologies to reduce water and temperature stress to mitigate likely negative effects of climate change. A visualization platform is developed using an open-source package for Google Earth® to help farmers and water and energy management agencies to better understand the connections between water and energy use and food production. This can be significant, especially during the occurrence of extreme events, but also to adapt to the negative effects of climate changes.

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“…Many approaches exist for estimating ET 0 [10][11][12][13][14], including those based on a single meteorological variable, such as air temperature [15], solar radiation [16], or mass transfer [17] using pan evaporation. However, the aforementioned methods are case-study specific and therefore are not generalized.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Wang

et al. 2021

Agriculture

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To estimate the irrigation volume required for agriculture and improve water resources utilization efficiency, it is essential to obtain an estimate of reference evapotranspiration (ET0) and its components (e.g., reference transpiration, T0 and reference soil evaporation, E0). This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and its associated components to obtain a reference-based SPAC model of reference evapotranspiration (R-SPAC), and it applied the model to an agricultural ecosystem. Model simulations of mean hourly ET0 were benchmarked against those of the Penman-Monteith method by the Food and Agriculture Organization (FAO-PM) throughout the growing season. The resulting good correlation obtained (R2 = 0.96, agreement index, I = 0.98, root-mean-square deviation (RMSD) = 0.05 mm h−1) validated the accuracy of the R-SPAC model. Sensitivity analysis was used to explore uncertainties and errors for ET0, T0, and E0 caused by input variables. The results showed that net radiation and shortwave radiation at the study site were the main drivers of ET0 for both the FAO-PM and R-SPAC models. The study showed that the proposed R-SPAC model can be used for predicting ET0 and for exploring interactions between climate, crop type, and soil in determining evapotranspiration under various future environment conditions.

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Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data

Cited by 72 publications

References 106 publications

Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

Towards an operational irrigation management system for Sweden with a water-food-energy nexus perspective

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

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