Mapping within-field variability in wheat yield and biomass using remote sensing vegetation indices

Campos, Isidro; González-Gómez, Laura; Villodre, Julio; Calera, María; Campoy, Jaime; Jiménez, Nuria; Plaza, Carmen; Sánchez-Prieto, Sergio; Calera, Alfonso

doi:10.1007/s11119-018-9596-z

Cited by 41 publications

(35 citation statements)

References 39 publications

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“…The detection of within-field variability by satellite or remotely piloted aircraft system (RPAS) images can be very useful for precision irrigation because this approach can provide specific information about irrigation, such as water application uniformity, areas with irrigation deficits or surface runoff areas near the outer boundary of the central pivot systems. For Campos et al [62], the variable application of agronomic inputs (e.g., water using variable-rate irrigation by central pivot) is an obstacle to precision agriculture, and variability maps can be essential for overcoming these obstacles because they provide accurate information about the real demand of each field zone.…”

Section: Plos Onementioning

confidence: 99%

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

et al. 2020

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Accurate information about the spatiotemporal variability of actual crop evapotranspiration (ETa), crop coefficient (K c) and water productivity (WP) is crucial for water efficient management in the agriculture. The Earth Engine Evapotranspiration Flux (EEFlux) application has become a popular approach for providing spatiotemporal information on ETa and Kc worldwide. The aim of this study was to quantify the variability of water consumption (ETa) and the K c for an irrigated commercial planting of soybeans based on the EEFlux application in the western region of the state of Bahia, Brazil. The water productivity (WP) for the fields was also obtained. Six cloud-free images from Landsat 7 and 8 satellites, acquired during the 2016/17 soybean growing season were used and processed on the EEFlux platform. The ETa from EEFlux was compared to that of the modified FAO (MFAO) approach using the following statistical metrics: Willmot's index of agreement (d-index), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE). The K c from EEFlux was compared to the K c used in the soybean field (K c FAO-based) and to the K c values obtained in different scientific studies using the d-index. A similar procedure was performed for WP. Our results reveal that EEFlux is able to provide accurate information about the variability of ETa and the K c of soybean fields. The comparison between ETa EEFlux and ETa MFAO showed good agreement based on the d-index, with values of 0.85, 0.83 and 0.89 for central pivots 1, 2 and 3, respectively. However, EEFlux tends to slightly underestimate ETa. The K c EEFlux showed good accordance with the K c values considered in this study, except in phase II, where a larger difference was observed; the average WP of the three fields (1.14 kg m-3) was higher than that in the majority of the previous studies, which is a strong indicator of the efficient use of water in the studied soybean fields. The study showed that EEFlux, an innovative and free tool for access spatiotemporal variability of ETa and Kc at global scale is very efficient to estimate the ETa and Kc on different growth stages of soybean crop.

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Section: Plos Onementioning

confidence: 99%

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

et al. 2020

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“…Isidro Campos et al [51] examined the abilities of meteorological data and RS for mapping the variability's of biomass or yield in cultivated wheat. A common easy design based on water productivity was obtained by integrating a time series of RS-centric VI.…”

Section: Review On Crop Condition Monitoring Systemsmentioning

confidence: 99%

Crop Detection and Classification using Remote Sensing Images

2019

IJITEE

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crop type identification timely and accurately is one of the applications of remote sensing (RS). It assists the people to regulate the variations in the costs of the food grains. RS images are utmost beneficial for agricultural productions. Recent research methodologies focuses mainly on the crops classification using satellite RS image. This paper proffers the survey on crop detection and classification utilizing RS images. This paper also highlights the latest studies regarding the implementation of crop detection and classification techniques like, review on disparate methodologies for crop recognition and classification (different classifiers are used to detect the crop), review on crop conditions monitoring system, and review on identification of yield estimation , crop region, and also crop growth. At last, the performances of the state-of-art methods are contrasted centered on the Kappa coefficient metrics and overall accuracy. Here, accuracy is the notable metric in the crop identification system.

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“…This has been of particular relevance in the objective of tracking progress towards the United Nations' Sustainable Development Goals. In response to such challenges, novel approaches to complement the on-the-field data collection that is resource-consuming and often subject to potential errors are gaining significance and international support (e.g., using remote sensing for measuring agricultural yield [23] or monitoring deforestation [24]).…”

Section: Introductionmentioning

confidence: 99%

Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region

Falchetta

Noussan

2019

Energies

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Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales.

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Mapping within-field variability in wheat yield and biomass using remote sensing vegetation indices

Cited by 41 publications

References 39 publications

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

Crop Detection and Classification using Remote Sensing Images

Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region

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