Spatialization of Crop Growth Simulation Model Using Remote Sensing

Biswal, Anima; Chakraborty, Abhishek; Murthy, C. S.

doi:10.1007/978-981-15-6864-0_4

Cited by 2 publications

(2 citation statements)

References 222 publications

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“…Although models cannot be used instead of experimental investigation, however the main advantage of the crop modelling is time and cost saving (García-Gutiérrez et al, 2021). In this context, various crop growth models have been developed including DSSAT, CropSyst, CROPWAT, FAO, APSIM and EPIC (Jones et al, 2003;Keating et al, 2003;Stockle et al, 2003;Maniruzzaman et al, 2015;Carlson et al, 2016;Jones et al, 2017;Razzaghi et al, 2017;Holzworth et al, 2018;Anar et al, 2019;Xu et al, 2019;Biswal et al, 2021). In general, these models analyse the results of the agricultural experimental data to simulate crop development, growth, yield, water and nutrient uptake in different agro-ecological environments (Pohanková et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

et al. 2020

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Water consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.

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Section: Introductionmentioning

confidence: 99%

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

et al. 2020

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“…Temporal monitoring on a daily scale, not yet possible at reasonably affordable costs through EO data with high spatial resolution, can instead be obtained by using crop growth models, as proven by the vast literature available on this subject, e.g., refs. [7][8][9][10]. The use of crop growth models also allows the estimation of variables that are not directly observable through sensors, thus offering a more complete and efficient monitoring of crops.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

et al. 2021

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The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.

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Spatialization of Crop Growth Simulation Model Using Remote Sensing

Cited by 2 publications

References 222 publications

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

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