Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 30720

Special Issue Editors

Prof. Dr. Jonghan Ko

E-Mail Website
Guest Editor
Applied Plant Science, Chonnam National University, Gwangju 61186, Korea
Interests: crop modelling; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Wenxuan Guo

E-Mail Website
Guest Editor
1. Department of Plant & Soil Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
2. Department of Soil and Crop Sciences, Texas A&M University, TAMU 2124, College Station, TX 77843, USA
Interests: precision agriculture; environmental sciences; remote sensing in agriculture; especially UAV application in precision plant phenotyping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ravinesh Deo

E-Mail Website
Guest Editor
School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Interests: metaheuristic algorithm; deep learning; artificial intelligence in renewable energy; smart electricity grids; energy loads or demand model; energy informatics or economics; green or cleaner energy solutions; energy generation; utilization; conversion; storage; transmission; management; and sustainability; sources such as mechanical; thermal; nuclear; chemical; electromagnetic; magnetic; electricity; solar; bio; hydro; wind; geothermal; tidal and ocean energy; fossil fuels and nuclear resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop simulation models and remote sensing are two distinct methodologies applied to monitor and predict crop growth conditions and agricultural production. Crop models are advantageous for the simulation of temporal variations in crop growth and the prediction of productivity, while they are limited in their ability to project spatial variations in crop growth conditions. Remote sensing with various tools, such as artificial intelligence, is useful for collecting spatial data for deriving information about in crop growth conditions, while it is inadequate for monitoring temporal variations in crop growth. Integrating these two techniques can bring in the strengths of both spatial and temporal crop growth monitoring to enhance crop management and optimize production.

Remote sensing with various sensors on diverse platforms generates big data, which poses serious challenges in data processing, analysis, and assimilation and the effective application of such data in agricultural production. On the other hand, technological development in data fusion, machine learning, and artificial intelligence provide opportunities for the consumption of big data and the derivation of information for optimizing crop production at unprecedented spatial and temporal scales. The objectives of this Special Issue are to compile the latest research on data fusion technologies that apply crop models and remote sensing to the monitoring and retrieval of crop and soil biophysical variables and genetic and phenotypic parameters. We invite authors to submit original research contributions, exhaustive reviews, new crop modelling and remote sensing methodologies, and relevant applications in diverse agricultural environments including the latest developments in agricultural technologies. Specifically, we are interested in papers on the following research topics:

  • Progress in scientific methodologies related to data fusion and data assimilation of remote sensing data in crop modeling;
  • Innovative remote sensing and image analysis tools or methods for enhanced quantification of biophysical and biochemical variables of crops and soils;
  • Application of a holistic system of these approaches.

This Special Issue is designed to foster the initiatives of the United Nations Food and Agricultural Organization (FAO) to resolve food security issues in both developing and developed nations. This issue is of interest to stakeholders in the agricultural policy area for use in climate change adaptation, digital agriculture, and modern farming techniques.

Dr. Jonghan Ko
Dr. Wenxuan Guo
Dr. Ravinesh C Deo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 6825 KiB  
Article
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
by A. A. Masrur Ahmed, Ravinesh C Deo, Nawin Raj, Afshin Ghahramani, Qi Feng, Zhenliang Yin and Linshan Yang
Remote Sens. 2021, 13(4), 554; https://doi.org/10.3390/rs13040554 - 4 Feb 2021
Cited by 40 | Viewed by 6446
Abstract
Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed [...] Read more.
Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

15 pages, 3742 KiB  
Article
Two-Dimensional Simulation of Barley Growth and Yield Using a Model Integrated with Remote-Controlled Aerial Imagery
by Ashifur Rahman Shawon, Jonghan Ko, Seungtaek Jeong, Taehwan Shin, Kyung Do Lee and Sang In Shim
Remote Sens. 2020, 12(22), 3766; https://doi.org/10.3390/rs12223766 - 16 Nov 2020
Cited by 4 | Viewed by 2595
Abstract
It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). [...] Read more.
It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). The objective of this study was to simulate spatiotemporal barley growth and yield based on the development of a crop-modeling system integrated with RAS-based remote sensing images. We performed field experiments to obtain ground truth data and RAS images of crop growth conditions and yields at Chonnam National University (CNU), Gwangju, South Korea in 2018, and at Gyeongsang National University (GNU), Jinju, South Gyeongsang, South Korea in 2018 and 2019. In model calibration, there was no significant difference (p = 0.12) between the simulated barley yields and measured yields, based on a two-sample t-test at CNU in 2018. In model validation, there was no significant difference between simulated yields and measured yields at p = 0.98 and 0.76, according to two-sample t-tests at GNU in 2018 and 2019, respectively. The remote sensing-integrated crop model accurately reproduced geospatial variations in barley yield and growth variables. The results demonstrate that the crop modeling approach is useful for monitoring at-field barley conditions. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

23 pages, 4178 KiB  
Article
Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data
by Alexey Stepanov, Konstantin Dubrovin, Aleksei Sorokin and Tatiana Aseeva
Remote Sens. 2020, 12(12), 1936; https://doi.org/10.3390/rs12121936 - 15 Jun 2020
Cited by 24 | Viewed by 4834
Abstract
Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, [...] Read more.
Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

20 pages, 3631 KiB  
Article
Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling
by Yan Zhao, Andries B Potgieter, Miao Zhang, Bingfang Wu and Graeme L Hammer
Remote Sens. 2020, 12(6), 1024; https://doi.org/10.3390/rs12061024 - 23 Mar 2020
Cited by 96 | Viewed by 8962
Abstract
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at [...] Read more.
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

22 pages, 4152 KiB  
Article
Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield
by Ashifur Rahman Shawon, Jonghan Ko, Bokeun Ha, Seungtaek Jeong, Dong Kwan Kim and Han-Yong Kim
Remote Sens. 2020, 12(3), 410; https://doi.org/10.3390/rs12030410 - 28 Jan 2020
Cited by 13 | Viewed by 3709
Abstract
A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index [...] Read more.
A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index (LAI), above-ground dry mass (AGDM), and yield, utilising the proximally sensed data integration into the modelling procedure. Field experiments were performed at two sites, one in 2017 and 2018 at Chonnam National University, Gwangju, and the other in 2017 at Jonnam Agricultural Research and Extension Services in Naju, Chonnam province, South Korea. The estimated parameters of radiation use efficiency, light extinction coefficient, and specific leaf area were 1.65 g MJ−1, 0.71, and 0.017 m2 g−1, respectively. Simulated LAI and AGDM values agreed with the measured values with significant model efficiencies in both calibration and validation, meaning that the proximal sensing data were effectively integrated into the crop model. The RSCM reproduced soybean yields in significant agreement with the measured yields in the model assessment. The study results demonstrate that the well-calibrated RSCM-soybean scheme can reproduce soybean growth and yield using simple input requirement and proximal sensing data. RSCM-soybean is easy to use and applicable to various soybean monitoring projects. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

17 pages, 4299 KiB  
Article
Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity
by Van Cuong Nguyen, Seungtaek Jeong, Jonghan Ko, Chi Tim Ng and Jongmin Yeom
Remote Sens. 2019, 11(18), 2131; https://doi.org/10.3390/rs11182131 - 13 Sep 2019
Cited by 17 | Viewed by 3270
Abstract
Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and [...] Read more.
Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions. Full article
(This article belongs to the Special Issue Smart Data Assimilations, Crop Modelling and Remote Sensing in Agriculture Monitoring)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop