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Remote Sensing
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Disruptive Trends of Earth Observation in Precision Farming
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Disruptive Trends of Earth Observation in Precision Farming

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 January 2022) | Viewed by 16574

Special Issue Editors

Dr. Thomas Alexandridis

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Guest Editor
Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: agronomic applications of earth observation; remote sensing; digital image processing; geoinformatics; spectroscopy; precision agriculture; UAV; crops; irrigation; soil
Special Issues, Collections and Topics in MDPI journals
Dr. Tomáš Řezník

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Guest Editor
Laboratory on Geoinformatics and Cartography, Faculty of Science, Masaryk University, Brno, Czech Republic
Interests: environmental analyses and modelling; spatial data infrastructures; geoinformation law; Semantic Web; Web cartography and geoinformatics; soil
Prof. Dr. Abdul M. Mouazen

E-Mail Website
Guest Editor
Precision Soil and Crop Engineering (Precision Scoring), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
Interests: proximal soil sensing; soil and water management; soil dynamics; tillage; traction; compaction; mechanical weeding; soil remediation and management and precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guo Huadong

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Guest Editor
International Center of Big Data for Sustainable Development Goals, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing science; radar for Earth observation; digital Earth; big Earth data; big data for SDGs

Special Issue Information

Dear Colleagues,

Recent technological advances and novel ideas has lead towards the development of new sensors (miniaturized hyperspectral, miniaturized lidar, dedicated online soil sensors), new satellite concepts (nano-satellite constellations, micro-satellite missions, on-board processing, educational satellites), new airborne platforms (low cost UAVs, Medium Altitude Long Endurance UAVs, High Altitude Pseudo Satellites), new digital image processing concepts (cloud computing, Software as a Service, Open Data Cube, real time processing, multiple data fusion, emerging algorithms), big data availability (PB of data from Copernicus, Google Earth Engine, Internet of Things, crowdsourcing 'farmer as a sensor') and new business models (vertical integration of Earth observation services, freely available high-resolution products).

In view of these developments, disruptive trends have emerged to provide valuable applications in site-specific management of agricultural resources for improved decision making and eventually optimizing agricultural production. These are essential steps towards sustainable application of precision agriculture and its promotion to new markets.

Capitalizing on the above disruptive trends, the aim of this special issue is to collect state-of-the-art research in the world of precision agriculture applications of Earth Observation. Submissions on the following topics are invited (but not limited to), as long as they present innovative methods and approaches, or novel applications of existing tools in precision agriculture applications:

  • miniaturized hyperspectral, miniaturized lidar, dedicated online sensors,
  • nano-satellite constellations, micro-satellite missions,
  • low flight UAVs, Medium Altitude Long Endurance UAVs,
  • sensors for proximal sensing at field level,
  • GPS/GNSS,
  • big data, IoT, crowdsourcing (farmer as a sensor),
  • emerging algorithms, cloud computing, real time processing, on-board processing, multiple data fusion,
  • spatio-temporal analysis of time series of agricultural parameters,
  • analysis of hyperspectral data cubes,
  • remote and proximal data assimilation in crop growth models,
  • mapping and predicting crop yield and grassland biomass,
  • mapping soil fertility parameters,
  • monitoring soil moisture content,
  • determining fertilization applications in the spatial domain,
  • monitoring crop growth,
  • monitoring water use and irrigation requirements at within-field scales,
  • mapping and early detection of crop diseases, weed and insect infestations for site-specific management,
  • crop damage assessment (frost, droughts, hail),
  • site-specific applications and management of agricultural resources.

Dr. Thomas Alexandridis
Dr. Tomáš Řezník
Prof. Dr. Abdul M. Mouazen
Prof. Guo Huadong
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 (4 papers)

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Research

22 pages, 49786 KiB  
Article
Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter
by Nikolaos Karapetsas, Thomas K. Alexandridis, George Bilas, Muhammad Abdul Munnaf, Angela P. Guerrero, Maria Calera, Anna Osann, Anne Gobin, Tomáš Rezník, Dimitrios Moshou and Abdul Mounem Mouazen
Remote Sens. 2022, 14(7), 1639; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071639 - 29 Mar 2022
Cited by 2 | Viewed by 2494
Abstract
Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years [...] Read more.
Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets. Full article
(This article belongs to the Special Issue Disruptive Trends of Earth Observation in Precision Farming)
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22 pages, 6731 KiB  
Article
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery
by Amarasingam Narmilan, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe and Buddhika Rasanjana Kulasekara
Remote Sens. 2022, 14(5), 1140; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051140 - 25 Feb 2022
Cited by 39 | Viewed by 6654
Abstract
The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane [...] Read more.
The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content. Full article
(This article belongs to the Special Issue Disruptive Trends of Earth Observation in Precision Farming)
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19 pages, 4792 KiB  
Article
Simplified Priestley–Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity
by Yunjun Yao, Zhenhua Di, Zijing Xie, Zhiqiang Xiao, Kun Jia, Xiaotong Zhang, Ke Shang, Junming Yang, Xiangyi Bei, Xiaozheng Guo and Ruiyang Yu
Remote Sens. 2021, 13(5), 902; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050902 - 27 Feb 2021
Cited by 6 | Viewed by 2335
Abstract
An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation [...] Read more.
An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types. Full article
(This article belongs to the Special Issue Disruptive Trends of Earth Observation in Precision Farming)
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21 pages, 8896 KiB  
Article
Winter Wheat Green-up Date Variation and its Diverse Response on the Hydrothermal Conditions over the North China Plain, Using MODIS Time-Series Data
by Linghui Guo, Jiangbo Gao, Chengyuan Hao, Linlin Zhang, Shaohong Wu and Xiangming Xiao
Remote Sens. 2019, 11(13), 1593; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131593 - 04 Jul 2019
Cited by 12 | Viewed by 3095
Abstract
Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason [...] Read more.
Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (Tm), mean maximum (Tmax) and minimum temperature (Tmin) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the Tmax and Tmin effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both Tmax and Tmin should be considered to improve the performance of spring phenology models. Full article
(This article belongs to the Special Issue Disruptive Trends of Earth Observation in Precision Farming)
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