An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI

Lai, Y. R.; Pringle, M. J.; Kopittke, Peter M.; Menzies, Neal W.; Orton, Thomas G.; Dang, Yash P.

doi:10.1016/j.jag.2018.07.013

Cited by 66 publications

(48 citation statements)

References 30 publications

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“…Generally, the heading period of winter wheat in the study is around in April and the grain filling period in May. Remote sensing vegetation indices (e.g., NDVI and EVI) can reflect the physiological characteristics of crops in different growth stages [87,88]. The close correlations between vegetation indexes and crop yields, especially during the flowering and grain filling stages [78,89,90], have strengthened the important role of later time window for yield prediction.…”

Section: Model Performance For Estimating Yields In Different Time Wimentioning

confidence: 97%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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Section: Model Performance For Estimating Yields In Different Time Wimentioning

confidence: 97%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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“…These empirical models rely on the correlation between spectral bands (and their combinations) and biophysical properties of the crops, such as Leaf Area Index (LAI), which are themselves related to final yields 7 . A large range of VIs has been tested, with mixed results, in different regions and for different crops including the well-known Normalised Difference Vegetation Index [8][9][10][11] . Capitalising on the ability to retrieve biophysical variables from satellite data 12,13 , some attempts have empirically correlate biophysical variables to yield 14,15 .…”

Section: High Temporal Resolution Of Leaf Area Data Improves Empiricamentioning

confidence: 99%

High temporal resolution of leaf area data improves empirical estimation of grain yield

Waldner¹,

Horan²,

Chen³

et al. 2019

Preprint

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Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.

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“…These time series allow yields to be estimated and production mapped in most cases at the end of the agricultural season and at spatial scales ranging from the agricultural region (by using medium-resolution images to cover areas of several tens of km 2 ) to the plot scale (by averaging information acquired at high spatial resolution over several hectares) [8][9][10][11][12]. The methods used are then diverse, ranging from simple empirical relationships (based on vegetation indices derived from reflectance [13][14][15]) to the assimilation of biophysical parameters (such as the leaf area index or the fraction of absorbed photosynthetically active radiation, derived from optical images by inversion of a radiative transfer model) in agro-meteorological models [16][17][18], or the use of different statistical algorithms (e.g., artificial neural network, partial least squares regression, support vector machine or random forest [19][20][21][22]). The latter approach has the advantage of obtaining high performance, particularly in a multi-factorial context, but it is conditioned by the availability of data (particularly access to field truths), which often constrains the possibilities of implementation (i.e., difficulty of independent calibration and validation procedures) and limits the representativeness of the algorithms.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

et al. 2020

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The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

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An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI

Cited by 66 publications

References 30 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

High temporal resolution of leaf area data improves empirical estimation of grain yield

Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

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