Assessing the Spatial Variability of Alfalfa Yield Using Satellite Imagery and Ground-Based Data

Kayad, Ahmed; Al‐Gaadi, Khalid A.; Tola, ElKamil; Madugundu, Rangaswamy; Zeyada, Ahmed M.; Kalaitzidis, Chariton

doi:10.1371/journal.pone.0157166

Cited by 40 publications

(29 citation statements)

References 26 publications

(32 reference statements)

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“…The present review highlighted that, to date, studies on EWE effects on agricultural crops using remote and proximal sensing technologies are lacking, as compared to other methodologies, i.e., process-based modeling, field ecophysiological measurements and simulation scenarios. Remote and proximal sensing methodologies are playing an increasingly important role in the agriculture sector [49,50], and their application to the topic of EWE will need to be enlarged. Furthermore, as already stressed by other authors [51][52][53] this manuscript showed that there is a serious concern for food security and economic losses due to EWE.…”

Section: Discussionmentioning

confidence: 99%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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Despite the increase of publications focusing on the consequences of extreme weather events (EWE) for the agricultural sector, a specific review of EWE related to agriculture is missing. This work aimed at assessing the interrelation between EWE and agriculture through a systematic quantitative review of current scientific literature. The review analysed 19 major cropping systems (cereals, legumes, viticulture, horticulture and pastures) across five continents. Documents were extracted from the Scopus database and examined with a text mining tool to appraise the trend of publications across the years, the specific EWE-related issues examined and the research gaps addressed. The results highlighted that food security and economic losses due to the EWE represent a major interest of the scientific community. Implementation of remote sensing and imagery techniques for monitoring and detecting the effects of EWE is still underdeveloped. Large research gaps still lie in the areas concerning the effects of EWE on major cash crops (grapevine and tomato) and the agronomic dynamics of EWE in developing countries. Current knowledge on the physiological dynamics regulating the responses of main crops to EWE appears to be well established, while more research is urgently needed in the fields of mitigation measures and governance systems.

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Section: Discussionmentioning

confidence: 99%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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“…In the study region, the alfalfa crop is usually cultivated for 2 years, with, on average, a cutting each 30-35 days during summer periods and every 45-60 days during winter periods. The mean alfalfa hay yield for each cut is estimated to be 2.0-3.0 kg ha −1 during winter and 4.0-5.0 kg ha −1 during spring or summer periods (Kayad et al, 2016).…”

Section: Study Areamentioning

confidence: 99%

Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images

Madugundu

Al‐Gaadi

Tola

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Accurate estimation of evapotranspiration (ET) is essential for hydrological modeling and efficient crop water management in hyper-arid climates. In this study, we applied the METRIC algorithm on Landsat-8 images, acquired from June to October 2013, for the mapping of ET of a 50 ha center-pivot irrigated alfalfa field in the eastern region of Saudi Arabia. The METRIC-estimated energy balance components and ET were evaluated against the data provided by an eddy covariance (EC) flux tower installed in the field. Results indicated that the METRIC algorithm provided accurate ET estimates over the study area, with RMSE values of 0.13 and 4.15 mm d −1 . The METRIC algorithm was observed to perform better in full canopy conditions compared to partial canopy conditions. On average, the METRIC algorithm overestimated the hourly ET by 6.6 % in comparison to the EC measurements; however, the daily ET was underestimated by 4.2 %.

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“…Currently, the availability of crop yield monitoring systems mounted to harvesters can provide yield maps but obviously only at the end of the season. Therefore, the rapid development in RS and the need for crop yield monitoring and prediction attracts the attention of many researchers to investigate within-field variability through satellite and aerial RS data [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…A large number of VIs have been developed to describe crop growth and subsequently yield. Some well-known ones are the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), Green Atmospherically Resistant Vegetation Index (GARVI), and Normalized Difference red edge (NDRE) [16,[28][29][30][31][32]. Specific modifications by differential weighting of some bands (in particular of the near-infrared range) have been applied to provide specific indices for crop yield monitoring, like the Wide Dynamic Range Vegetation Index (WDRVI) [33,34] and the Green Chlorophyll Vegetation Index (GCVI) [35].…”

Section: Introductionmentioning

confidence: 99%

“…Specific modifications by differential weighting of some bands (in particular of the near-infrared range) have been applied to provide specific indices for crop yield monitoring, like the Wide Dynamic Range Vegetation Index (WDRVI) [33,34] and the Green Chlorophyll Vegetation Index (GCVI) [35]. Selecting the most suitable VI and crop age for yield assessment is an important step in crop yield empirical model development [16].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Kayad

Sozzi

Gatto³

et al. 2019

Remote Sensing

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Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

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Assessing the Spatial Variability of Alfalfa Yield Using Satellite Imagery and Ground-Based Data

Cited by 40 publications

References 26 publications

Extreme Weather Events in Agriculture: A Systematic Review

Extreme Weather Events in Agriculture: A Systematic Review

Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

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