Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields

Campos, Isidro; González-Gómez, Laura; Villodre, Julio; González-Piqueras, José; Suyker, Andrew E.; Calera, Alfonso

doi:10.1016/j.fcr.2017.11.025

Cited by 39 publications

(31 citation statements)

References 72 publications

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“…Remote sensing (RS) measurements can provide timely information on plant growth and development, responses to dynamic weather conditions and management practices and, therefore, the final crop yield potentials [33]. Based on crop-specific spectral reflectance features, crop yields can be predicted by constructing remote-sensing models that incorporate multiple vegetation indices [68][69][70].…”

Section: The Major Elements and Potential Utilization Of The Establismentioning

confidence: 99%

“…Hyperspectral imaging has become a common method used to predict crop traits and yields [24,25]. Hyperspectral remote-sensing data acquired from the ground [26][27][28], unmanned aerial vehicles [10,[29][30][31], airborne platforms [32] and satellite platforms [33] can capture crop canopy spectra in narrow bands and thereby provide information on the biophysical/biochemical composition of the canopy. Low-altitude and flexible unmanned aerial vehicles (UAV) provide an important, affordable and low-cost approach to quantify the components of crop phenotyping [34,35] and precision agriculture [36,37].…”

Section: Introductionmentioning

confidence: 99%

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Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

et al. 2019

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Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

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Section: The Major Elements and Potential Utilization Of The Establismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

et al. 2019

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“…The aNDVI, as proposed by Campos et al [25] and González-Gómez [26], was preferred to map the within-field variability instead of only using indices of a specific date (e.g., just before tasselling) because, according to our experience, the crop development stage in different parts of the plot is not uniform. It can vary according to soil properties, irrigation rates, and other factors.…”

Section: Methodological Approachmentioning

confidence: 99%

“…There are other multispectral broad-band vegetation indices available for use in PA that can be calculated from Sentinel-2 bands (for example, normalized difference index bands B04 and B05 (NDI45) [34], and the red edge inflection point [35], among others). However, although several limitations have been recognised, it is one of the most frequently used index used to estimate spatial patterns in crop biomass and/or potential crop yield [1,25]. In addition, at present it starts to be widely used in PA services for end users.…”

Section: Methodological Approachmentioning

confidence: 99%

“…For example, Maresma et al [19,24] investigated the correlation of different multispectral vegetation indices with the N status at V12 (12 leaves with visible leaf collar) just before tasselling with a single date image. However, other authors point out that good estimator of biomass and yield can be derived from the use of temporal series of remote-sensing images to characterize dynamics of crop canopy throughout the growing and development cycle [25,26]. From that, the accumulation of vegetation index values (e.g., the normalized difference vegetation index, NDVI) from the beginning of the biomass accumulation to grain maturity could be a good alternative to yield maps to estimate the within-field variability to finally delineate potential zones for different management practices.…”

Section: Introductionmentioning

confidence: 99%

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Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

2018

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Abstract:One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer's expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers' expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.

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Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Mustafiz

Noguchi

Ahamed

2022

New Frontiers in Regional Science: Asian Perspectives

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Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields

Cited by 39 publications

References 72 publications

Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

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