Regional Crop Yield Assessment by Combination of a Crop Growth Model and Phenology Information Derived from MODIS

2011 ◽  
Vol 9 (3) ◽  
pp. 981-989 ◽  
Author(s):  
Wenbo Xu ◽  
Hao Jiang ◽  
Jianxi Huang
Keyword(s):  
Crop Yield ◽  
Growth Model ◽  
Crop Growth ◽  
Crop Growth Model

scholarly journals Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

2021 ◽  
Vol 13 (21) ◽  
pp. 4372
Author(s):  
Yi Xie ◽  
Jianxi Huang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Growth Model ◽  
Wheat Yield ◽  
Crop Growth ◽  
Henan Province ◽  
Crop Growth Model ◽  
Area Index ◽  
Growing Region

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.

scholarly journals May spring seasonal forecasts improve a climate service providing cereal yield predictions?

2021 ◽  
Author(s):  
Maria Nieves Garrido ◽  
J. Ignacio Villarino ◽  
Eroteida Sánchez ◽  
Inmaculada Abia ◽  
Marta Dominguez ◽  
...  
Keyword(s):  
Crop Yield ◽  
Growth Model ◽  
Wheat Yield ◽  
Crop Growth ◽  
Seasonal Forecasts ◽  
Crop Growth Model ◽  
Yield Data ◽  
Winter Climate ◽  
Winter Cereal ◽  
Climate Service

<p>Following the need of winter cereal farmers from the main producing region (Castilla y León) in Spain to estimate crop yield with at least one season of anticipation, we have developed a climate service based essentially on current and historical meteorological observations, on spring seasonal forecasts from ECMWF System 5 and on the crop growth model AquaCrop. Different experiments have been designed to produce both a synthetic yield database serving as observed truth and three different seasonal forecasting strategies. Calculation of objective verification scores for deterministic and probabilistic crop yield forecasts -including an assessment of their potential economic value- in hindcast mode determines the quality of this service and the differences among forecasting strategies. We demonstrate that the three compared strategies show good skill of wheat yield forecasts at the beginning of July, although the meteorological forcing for Aquacrop simulations between 1st April and 30th June is very different for the three compared strategies. The important role of the memory from previous (autumn and winter) climate conditions carried by the crop growth model is analysed and discussed.  A yearly assessment also allows some preliminary estimation of the value and possible benefits of the service for final users. Finally, we conclude that the simulation synthetically producing the observed truth compares rather well –especially the interannual variability- with other yield data based on surveys and experts estimations although it overestimates yield. Users have played a decisive role in co-design and co-development phases of this climate service. They have also actively intervened in the analysis and evaluation of results.</p><p> </p>

scholarly journals Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

2018 ◽  
Vol 11 (7) ◽  
pp. 2789-2812 ◽  
Author(s):  
Werner von Bloh ◽  
Sibyll Schaphoff ◽  
Christoph Müller ◽  
Susanne Rolinski ◽  
Katharina Waha ◽  
...  
Keyword(s):  
Growth Model ◽  
Nitrogen Cycle ◽  
Crop Productivity ◽  
Crop Growth ◽  
Limiting Factors ◽  
Full Detail ◽  
Crop Growth Model ◽  
Maintenance Respiration ◽  
Area Index ◽  
Global Vegetation

Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901–2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

2021 ◽  
Author(s):  
Bingyu Zhao ◽  
Meiling Liu ◽  
Jiianjun Wu ◽  
Xiangnan Liu ◽  
Mengxue Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Growth Model ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Growth Conditions ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Parallel Method ◽  
Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

scholarly journals Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

2014 ◽  
Vol 18 (10) ◽  
pp. 4223-4238 ◽  
Author(s):  
G. M. Tsarouchi ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Coupled Model ◽  
Crop Growth ◽  
Original Model ◽  
Surface Model ◽  
Strong Impact ◽  
Crop Growth Model ◽  
Mean Error ◽  
The Impact

Abstract. Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4–11.6 mm month−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2–3.4 mm month−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

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