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Special Issue "Earth Observations and Crop Models for Sustainable Agricultural Management: Part II"

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 July 2021).

Special Issue Editors

Dr. Zhongxin Chen
E-Mail Website
Guest Editor
Senior IT Officer, IT Service Division (CSI), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
Interests: remote sensing applications in agriculture; data assimilation; agro-geoinformatics
Special Issues and Collections in MDPI journals
Prof. Dr. Jianxi Huang
E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: crop type mapping; crop yield forecasting; data assimilation; crop growth models
Special Issues and Collections in MDPI journals
Prof. Dr. Guijun Yang
E-Mail Website
Guest Editor
National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, 11 Middle Road, Haidian District, Beijing 100097, China
Interests: remote sensing; agronomic modelling; UAV-based sensors; precision farming
Special Issues and Collections in MDPI journals
Prof. Dr. Shibo Fang
E-Mail Website
Guest Editor
Institute of Eco-environment and Agro-Meteorology (IEAM), Chinese Academy of Meteorology Science, No. 46, Zhongguancun South Street, Haidian District, Beijing, China
Interests: soil moisture remote sensing; SAR remote sensing in Agriculture; Crop stress; crop growth observation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Because the 1st edition of this Special Issue (available at https://0-www-mdpi-com.brum.beds.ac.uk/journal/remotesensing/special_issues/crop_rs) was so successful, we are pleased to continue the series in a 2nd edition. We introduce this volume here.

Modern agricultural management strongly requires intensive and extensive information from earth observation and spatially explicit models (SEMs). Thanks to the rapid development of earth observation systems and data processing technologies, the quantity and quality of the available information for agriculture have improved substantially in the past decade. On the other hand, crop models have contributed greatly to agricultural management and research. Both process-based and statistical crop models often require wide-spectrum data input, and inadequate data input will limit the performance and thus the applications of crop models. Many innovative research works have been committed to incorporating earth observations into crop models to facilitate agricultural management, but there are still gaps to be met for sustainable and profitable agricultural management.

To better understand the challenges and opportunities to integrate earth observation with crop modelling technologies, this Special Issue invites contributions on: (i) innovative EO methods to derive crop parameters; (ii) novel spatially-explicit crop models towards a better understanding of agricultural production system and ecosystems; and (iii) remote sensing data assimilation with crop models. Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following:

  • EO quantitative inversion of crop and relevant environmental parameters
  • Calibration and verification of various of EO datasets including Sentinel and Gaofen imagery
  • Multi-sensor and multi-system EO data fusion
  • EO for monitoring crop growth, health and yield
  • EO for pests and diseases
  • Spatially-explicit crop model development, implementation, and validation
  • Data assimilation algorithms, system and uncertainty

Dr. Zhongxin Chen
Dr. Jianxi Huang
Prof. Guijun Yang
Prof. Shibo Fang
Prof. Zhenhong Li
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 papers will be 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 2400 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.

Keywords

  • Multispectral imagery
  • Hyperspectral imagery
  • SAR processing
  • Thermal infrared imagery
  • Lidar
  • UAV/GAV sensors
  • Quantitative remote sensing
  • Data fusion
  • Data assimilations
  • Crop modelling
  • Crop growth and health
  • Pest and diseases
  • Yield mapping and prediction

Published Papers (5 papers)

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Research

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Article
Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery
Remote Sens. 2021, 13(14), 2755; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142755 - 13 Jul 2021
Viewed by 507
Abstract
The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making [...] Read more.
The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops. Full article
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Article
Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data
Remote Sens. 2021, 13(8), 1536; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081536 - 15 Apr 2021
Viewed by 439
Abstract
To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, [...] Read more.
To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method. Full article
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Article
Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield
Remote Sens. 2021, 13(6), 1094; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061094 - 13 Mar 2021
Cited by 3 | Viewed by 849
Abstract
In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach [...] Read more.
In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated. Full article
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Article
Exploring the Optical Properties of Leaf Photosynthetic and Photo-Protective Pigments In Vivo Based on the Separation of Spectral Overlapping
Remote Sens. 2020, 12(21), 3615; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213615 - 03 Nov 2020
Viewed by 673
Abstract
The in vivo features of the absorption of leaf photosynthetic and photo-protective pigments are closely linked to the leaf spectrum in the 400–800 nm regions. However, this information is difficult to obtain because the overlapping leaf pigments can mask the contribution of individual [...] Read more.
The in vivo features of the absorption of leaf photosynthetic and photo-protective pigments are closely linked to the leaf spectrum in the 400–800 nm regions. However, this information is difficult to obtain because the overlapping leaf pigments can mask the contribution of individual pigments to the leaf spectrum. Here, to limit the masking phenomenon between these pigments, the separation technology for leaf spectral overlapping was employed in the PROSPECT model with the ZJU dataset. The main results of this study include the following aspects: (1) the absorption coefficients of separated chlorophyll a and b, carotenoids and anthocyanins in the leaf in vivo display the physical principles of forming an absorption spectrum similar to those in an organic solution; (2) the differences in the position of each absorption peak of pigments between the leaf in vivo and in an organic solution can be described by a spectral displacement parameter; and (3) the overlapping characteristics between the separated pigments in the leaf in vivo are clearly drawn by a range of absorption feature (RAF) parameter. Moreover, the absorption coefficients of the separated pigments were successfully applied in leaf spectral modeling and pigment retrieval. The results show that the separated multiple pigment absorption coefficients from the leaf spectrum in vivo are effective and provide a framework for future refinements in describing leaf optical properties. Full article
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Letter
Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China
Remote Sens. 2020, 12(21), 3539; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213539 - 28 Oct 2020
Cited by 2 | Viewed by 869
Abstract
Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery [...] Read more.
Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data. Full article
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