Abstract
This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).
Highlights
The contention to achieve food security and the sustainable use of agricultural resources becomes vital due to the growth in the human population [1]
The capability of the STARFM is investigated by fusing both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to create the synthetic normalized difference vegetation index (NDVI) time series, having a 30 m spatial and daily temporal resolution with an overall R2 of 0.618 and root mean square error (RMSE) of 0.10
The measured biomass is used to compare the performance of the crop growth models (CGMs) based on the accuracy, simplicity, and reliability of the predicted biomass using the STARFM and MODIS NDVI time series
Summary
The contention to achieve food security and the sustainable use of agricultural resources becomes vital due to the growth in the human population [1]. The World Summit on Food Security states that the community expects to reach a margin of 10 billion by 2050, and this will force intensive agriculture demand, along with the biggest challenge of climate change [2,3] In this context, achieving sustainability in agriculture might slow down the negative impacts on the quantity and quality of soil and water resources, land degradation, greenhouse emissions, or biodiversity [4]. Efficient methodologies that are both able to monitor crop conditions and changing weather conditions near real-time are essential [1] These methodologies can be applied to predict crop production, e.g., for optimizing the management strategies in agriculture and increasing sustainability [6]. These crop predictions are essential to obtain the economic returns, but are, at the same time, highly valuable to efficiently evaluate food production insufficiency and to ensure food security in the agricultural regions of the world [7]
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