Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

Durgun, Yetkin Özüm; Gobin, Anne; Gilliams, Sven; Duveiller, Grégory; Tychon, Bernard

doi:10.3390/rs8030170

Cited by 11 publications

(8 citation statements)

References 102 publications

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“…This is in contrast to studies in other regions, where peak NDVI has been shown to capture wheat yield variability well [3,21]. In the present study, maxNDVI occurred on average on 24 [22]. The observed maxNDVI occurred on average close to winter wheat anthesis.…”

Section: Discussioncontrasting

confidence: 99%

“…From these results we concluded that modelling winter wheat yield based on NDVI using an empirical model is environmentally dependent. The environmental dependency of wheat yield models based on fAPAR, another vegetation index, was already demonstrated by [23,24]. In rain-fed regions with high yields in Europe, the correlations between fAPAR and yield were low, whereas in regions where yield is water-limited, high correlations between fAPAR and yield were found [23].…”

Section: Discussionmentioning

confidence: 88%

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Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 88%

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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“…In addition, remotely sensed VIs are globally available, allowing us to monitor crop yield at the field level as well as at the global level. Several studies have already demonstrated the possibility to use VIs to monitor yield at different geographical locations and at different scales i.e., from the field to global level [12][13][14][15][16][17]. One of the VIs that is often and successfully used to estimate crop yield is the Normalized Difference Vegetation Index (NDVI) [14,18,19].…”

Section: Introductionmentioning

confidence: 99%

Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

et al. 2020

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Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

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“…1) Statistical estimation model using satellite remote sensing spectral information [6], which is obtained by establishing the relationship between vegetation index and crop yield. This type of model is commonly empirical, fundamentally simple, and lack of biological basis, making them hard to be extended to other areas [7]- [9].…”

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Rice Yield Estimation Based on an NPP Model With a Changing Harvest Index

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Paddy rice is one of the most widely planted crops in Asia. Yield estimation of paddy rice is crucial to food security. Gross primary productivity or net primary productivity (NPP) models are some of the most commonly used methods for crop yield estimation as they have a theoretical basis and are simple to use. The harvest index (HI) of paddy rice, one of the input parameters in yield estimation models, has been increasingly used with the improvement of paddy rice cultivars over the past four decades. However, in many previous researches, this parameter was arbitrarily determined to fit the observed yield data values, without considering its changing trend. In the present study, we developed an NPP-based rice yield estimation model with HI being a changing parameter. The accuracy of the proposed method is tested over the Jiangsu Province, Southeast China. The results showed that the rice HI increases linearly in study area. Compared with yield estimation with a fixed HI, yield estimations using a changing HI are greatly improved, with the average estimation accuracy greater than 96%, and the relative errors within ±5%. The results proved that the NPP-based yield estimation model with a changing HI can be a promising alternative for rice yield estimation. Index Terms-Gross primary productivity (GPP), harvest index (HI), net primary productivity (NPP), paddy rice yield estimation, rectangular hyperbolic model. I. INTRODUCTION T HE global human population is estimated to reach 9.7 billion by the year 2050 [1] and this makes the availability of the rice grain extremely important for food security. A range of factors, including sharp increase of population, decline in arable area, water pollution and water resource shortage, environmental pollution and global climate change, significantly affects agricultural production and threatens grain security [2]. Grain security has become a tremendous challenge in many countries and regions. Paddy rice is one of the most important and widely planted crops in the world, especially in Asia [3], providing an half of mankind's calories. More than 65% Chinese Manuscript

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Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

Cited by 11 publications

References 102 publications

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

Rice Yield Estimation Based on an NPP Model With a Changing Harvest Index

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