Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Kern, Anikó; Barcza, Zoltán; Marjanović, Hrvoje; Árendás, Tamás; Fodor, Nándor; Bonis, Peter A; Bognár, Péter; Lichtenberger, János

doi:10.1016/j.agrformet.2018.06.009

Cited by 170 publications

(78 citation statements)

References 113 publications

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“…Furthermore, we showed that the sun-angle effect can introduce 7% and 10% uncertainty in estimating NDVI max and IntNDVI, and a reduced uncertainty for EVI. This result has important implications as both the VI max and IntVI have been used extensively for estimating vegetation primary productivity (or crop yield and rangeland forage production) [81][82][83][84][85], for land carbon uptake modelling [86,87], and for food security assessment for famine early-warning systems [88,89]. Our findings therefore stress the need to consider proper corrections of the sun-angle effect to achieve more reliable use of vegetation indices in a variety of applications.…”

Section: Sun-angle Effect On Retrievals Of Vegetation Phenology and Pmentioning

confidence: 72%

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Huete

Tran

et al. 2020

Remote Sensing

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Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

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Section: Sun-angle Effect On Retrievals Of Vegetation Phenology and Pmentioning

confidence: 72%

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Huete

Tran

et al. 2020

Remote Sensing

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“…However, the dry grain weight (quantified as the product of time and grain filling rate) increases linearly with the extension of the filling time under a suitable temperature condition [96]. Hence heat stress during the grain filling stage will negatively affect crop yield [97][98][99] through preventing the transfer of photosynthetic products to the grains [100], or damaging photosynthesis in winter wheat leaves and causing faster senescence and shorter maturity [101,102]. In contrast, the low temperature in winter (frost damage) is a crucial stress affecting the growth of winter wheat, which usually causes damage to, or even the death of wheat seedlings, reducing the number of spikelets or seeds, consequently leading to a decreased yield [93].…”

Section: Model Performance For Estimating Yields In Different Time Wimentioning

confidence: 99%

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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“…Climate or environmental index-based methods determine the vulnerability of the studied agricultural area on production factors that are characterized by multidimensional scoring system (Olesen et al, 2011). Statistical models use regression equations to show linkage between yield or yield components and climate variables (Kern et al, 2018;Leng and Huang, 2017;Lobell and Burke, 2010). Niche-based models define the geographical distribution of a crop species and specify the concerning environmental suitability expressed on a scale (0-1) (Estes et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Estimation of agro-ecosystem services using biogeochemical models

Pokovai¹,

Hollós²,

Vidnyánszky-Bottyán³

et al. 2020

Időjárás

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⎯ Agro-ecosystem services are the various benefits (e.g., crop yield) that people freely obtain from the properly functioning agricultural lands. The estimated changes in climatic conditions including increasing temperature, with particular attention to the summer means, together with the expected changes in the temporal precipitation distribution pose enormous challenge to the agriculture. Currently, dynamic system models are most frequently used tools that are capable of estimating the prospective effects of climate change on agro-ecosystems. A deterministic biogeochemical model is presented that is developed by Hungarian scientists within the framework of the AgroMo project. The main goal of the AgroMo project is to develop climate-smart strategies in order to mitigate the effect of potential future hazards in the context of climate change by 1) creating a complex, state-of-the-art experimental platform; 2) producing ten new, 0.1° spatial resolution climate scenarios based on the RCP (Representative Concentration Pathway) 4.5 and RCP8.5 scenarios; 3) developing an integrated assessment and modeling framework that is capable of simulating every major land use types; 4) analyzing/simulating a great number of adaptation strategies that can be used to support decision makers. Based on the preliminary simulation results, climate change will most likely expose significant negative impact on the spring sown crops in Hungary. Although, the yield losses could be avoided with irrigation or could be mitigated with earlier sowing, the role of winter crops is likely to become more significant in Hungary in the future.

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Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Cited by 170 publications

References 113 publications

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

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

Estimation of agro-ecosystem services using biogeochemical models

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