Special Issue "Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture"

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: 1 April 2022.

Special Issue Editors

Dr. Abid Ali
E-Mail Website
Guest Editor
Department of Agricultural and Food Sciences-DISTAL, University of Bologna, Viale Fanin 44, North Wing, 40127 Bologna, Italy
Interests: foliar application of nutrients; organic farming; quantum GIS; ArcGIS; landsat vegetation indices; ECa directed to soil sampling technique; geostatistical analysis; precision agriculture; remote sensing
Dr. Flavio Lupia
E-Mail Website1 Website2
Guest Editor
CREA Research Centre for Agricultural Policies and Bioeconomy, Via Po 14, 00198 Rome, Italy
Interests: geospatial information; precision agriculture; remote sensing; administrative and statistical data for agriculture; open data
Special Issues, Collections and Topics in MDPI journals
Dr. Michał Stępień
E-Mail Website
Guest Editor
ZSR (Complex of Agricultural Schools) in Grzybno, Grzybno 48, 63-112 Brodnica, Poland
Interests: management zones; site specific tillage and input management; precision agriculture application on smaller farms; soil science; land evaluation; fertilization; tropical agriculture; organic farming; agricultural machinery
Dr. Bahattin Akdemir
E-Mail Website
Guest Editor
Tekirdag Namik Kemal University, Faculty of Agriculture, Department of Biosystems Engineering, 59030 Tekirdag, Turkey
Interests: precision agriculture; precision horticulture; variable rate application; spatial variability; yield mapping; sensors in agriculture; automatic steering; agricultural machinery; tractors
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, Collections and Topics in MDPI journals
Dr. Dariusz Gozdowski
E-Mail Website
Guest Editor
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences—SGGW, 159 Nowoursynowska St., 02-776 Warsaw, Poland
Interests: statisticsbio; statistics; GIS; geostatistics; precision agriculture

Special Issue Information

Dear Colleagues,

The current global food and agriculture system is facing major global challenges including climate change, population growth, environmental degradation biodiversity loss and natural resources depletion. It is recognized that agricultural digitalization might be one of the approaches that can help to counterbalance the current situation with the help of remote sensing and other technologies producing a huge amount of relevant data at parcel, farm and regional levels.

Today, we can model the crop yield performances, quality of the agricultural product and environmental effects of agricultural input usage in the spatio-temporal dimension by exploiting the remotely and proximal collectable data and by analyzing the relationships among crop, soil, weather and farm management practices. Several approaches are being developed to allow the precise management of farm resources as function of the within-field variability enacting a relevant leap compared to the traditional agricultural practices. However, the implementation of precision agriculture practices faces the challenge due to the diversity of factors which impact the crop yields and quality such as size of agricultural lands and variability of topography, soil, moisture and microclimatic conditions etc.

In this special issue; we focus on the state-of-art research on digital agriculture enabled by integrating remote, proximal and ground sensing technologies with crop, soil and weather data in search of a sustainable use of farm inputs. Innovative approaches are solicited on measurement, management/integration and use of data established by technologies for better understanding and managing the within-field variability and its relationship with remote, proximally and ground-sensed data. We invite you to submit reviews, case studies, or research articles for that focus on scientific methods, technological tools, and innovative statistical analyses, to capture the current advancements and fostering an open discussion on the future perspectives on the smart exploitation of spatial data integration in agriculture.

Dr. Abid Ali
Dr. Flavio Lupia
Dr. Michał Stępień
Dr. Bahattin Akdemir
Dr. Zhongxin Chen
Dr. Dariusz Gozdowski
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 2500 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

  • Spatio-temporal soil/crop/product quality variability
  • Spectral vegetation indices
  • Proximal soil and crop sensing
  • Soil spatial variability
  • Weather data and irrigation
  • Geostatistical analysis of soil and crop variability
  • Site-specific crop management
  • Precision farming
  • Digital agriculture

Published Papers (1 paper)

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Research

Article
Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy
Remote Sens. 2021, 13(19), 4000; https://doi.org/10.3390/rs13194000 - 06 Oct 2021
Viewed by 617
Abstract
Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting [...] Read more.
Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture. Full article
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