Special Issue "Earth Observation and GIScience for Agricultural Applications"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

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
Prof. Dr. Cidália Costa Fonte
E-Mail Website1 Website2
Guest Editor
Department of Mathematics, University of Coimbra, INESC Coimbra, Largo D. Dinis, 3001 – 501 Coimbra, Portugal
Interests: geospatial data quality; land use/land cover mapping; remote sensing; crowdsourced data; spatial analysis
Dr. Giuseppe Pulighe
E-Mail Website1 Website2
Guest Editor
CREA Research Centre for Agricultural Policies and Bioeconomy, Via Po 14, 00198 Rome, Italy
Interests: watershed modeling; bioenergy development; urban agriculture; land use/land cover; geoinformation; remote sensing; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,


Current and projected scenarios for global agricultural systems call for new approaches for sustainable use of environmental resources and decision-making. Recent technological advancements, such as earth observation, geospatial technologies, unmanned aerial vehicles (UAVs), and the Internet of things (IoT), among others, have boosted our knowledge about agricultural systems and interactions at natural, social, and economic levels. These have contributed to collecting, analyzing, monitoring, and simulating the use of resources in the agricultural domain and provided decision-making tools for sustainable intensification, production systems and supply chains, and natural resource management, among others.

We are facing the data deluge era with free and open data policies for several satellite platforms and geospatial data originates from a wealth of human and electronic sensors along with a growing set of powerful tools for data management and processing. We are at the right time to harness the geospatial components for deploying products and services to serve different stakeholders in agricultural monitoring and supporting a climate-resilient, climate-neutral, and environment-friendly agriculture.

EO data and GIScience will be pivotal in ensuring the implementation of EU and global policies (EU’s Green Deal, the Area Monitoring System of the Common Agricultural Policy and national agricultural statistics).

This Special Issue will explore relevant applications of geospatial data and techniques for agriculture and sustainable use of resources.

We cordially invite original research, reviews, and contributions on topics including but not limited to the following:

  • Remote, proximal, ground, and human sensing data collection to support application in agriculture;
  • GIS-based decision support systems for managing resources in agriculture and carrying out scenario simulations;
  • Geospatial analysis for measuring climate change impacts on cultivated lands;
  • Integration of geodata for assessing the relationships between environment and agriculture;
  • Administrative and statistical data for monitoring agricultural activities;
  • Land cover and land use mapping for agricultural land;
  • Geospatial big data for agricultural applications;
  • Novel tools and techniques for data collection in agriculture.


Dr. Eng. Flavio Lupia
Prof. Dr. Jamal Jokar Arsanjani
Prof. Dr. Cidália Costa Fonte
Dr. Giuseppe Pulighe
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1400 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

  • Geographic information systems
  • Earth observation
  • Sustainable agriculture
  • Geospatial analysis
  • Spatial and spatiotemporal data acquisition
  • Geospatial big data
  • Food security and production
  • Crop modeling
  • Biophysical parameter retrieval
  • Agricultural monitoring and reporting
  • Land cover and land use mapping

Published Papers (7 papers)

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Research

Article
Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study
ISPRS Int. J. Geo-Inf. 2021, 10(11), 792; https://doi.org/10.3390/ijgi10110792 - 20 Nov 2021
Viewed by 250
Abstract
Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this [...] Read more.
Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the following research questions: (a) how will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs)?: (b) do machine learning models perform well enough in predicting changes of the groundwater in Denmark? If so, which ML model outperforms for forecasting these changes? Three ML algorithms were used in R: artificial neural networks (ANN), support vector machine (SVM) and random forest (RF). The ML models were trained with time-series data of groundwater levels taken at wells in the Hovedstaden region, for the period 1990–2018. Several independent variables were used to train the models, including different soil parameters, topographical features and climatic variables for the time period and region selected. Results show that the RF model outperformed the other two, resulting in a higher R-squared and lower mean absolute error (MAE). The future prediction maps for the different scenarios show little variation in the water table. Nevertheless, predictions show that it will rise slightly, mostly in the order of 0–0.25 m, especially during winter. The proposed approach in this study can be used to visualize areas where the water levels are expected to change, as well as to gain insights about how big the changes will be. The approaches and models developed with this paper could be replicated and applied to other study areas, allowing for the possibility to extend this model to a national level, improving the prevention and adaptation plans in Denmark and providing a more global overview of future water level predictions to more efficiently handle future climate change scenarios. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Risk Assessment of Different Maize (Zea mays L.) Lodging Types in the Northeast and the North China Plain Based on a Joint Probability Distribution Model
ISPRS Int. J. Geo-Inf. 2021, 10(11), 723; https://doi.org/10.3390/ijgi10110723 - 26 Oct 2021
Viewed by 290
Abstract
Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and [...] Read more.
Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia
ISPRS Int. J. Geo-Inf. 2021, 10(10), 645; https://doi.org/10.3390/ijgi10100645 - 25 Sep 2021
Viewed by 466
Abstract
Evaluation of the vegetation and agricultural-management factor (C-factor) is an important task, the solution of which affects the correct assessment of the intensity of soil erosion. For the vast area of the European part of Russia (EPR), this task is particularly relevant since [...] Read more.
Evaluation of the vegetation and agricultural-management factor (C-factor) is an important task, the solution of which affects the correct assessment of the intensity of soil erosion. For the vast area of the European part of Russia (EPR), this task is particularly relevant since no products allow taking into account the C-factor. An approach based on automated interpretation of the main crop groups based on MODIS satellite imaging data from Terra and Aqua satellites with the LSTM machine-learning method was used to achieve this goal. The accuracy of crop group recognition compared to the open data of the Federal State Statistics Service of Russia was 94%. The resulting crop maps were used to calculate the C-factor for each month of a particular year from 2014 to 2019. After that, summaries were made at the regional and landscape levels. The average C-factor value for the EPR was 0.401, for the forest landscape zone 0.262, for the forest-steppe zone 0.362, and for the steppe zone 0.454. The obtained results are in good correlation with the results of previous field studies and provide up-to-date (based on 2014–2019 data) estimates of C-factor for rainfall erosion (monthly, annual) with high spatial detail (250 m). Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine
ISPRS Int. J. Geo-Inf. 2021, 10(9), 587; https://doi.org/10.3390/ijgi10090587 - 02 Sep 2021
Cited by 1 | Viewed by 597
Abstract
Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we [...] Read more.
Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Usage of Airborne Hyperspectral Imaging Data for Identifying Spatial Variability of Soil Nitrogen Content
ISPRS Int. J. Geo-Inf. 2021, 10(6), 355; https://doi.org/10.3390/ijgi10060355 - 21 May 2021
Cited by 2 | Viewed by 731
Abstract
Soil is a significant natural resource composed of organic and inorganic material. Nitrogen, one of the essential elements, is traditionally measured using laboratory methods. The development of hyperspectral imaging enables the cost-effective acquisition of both spectral and spatial information for detecting physical, chemical, [...] Read more.
Soil is a significant natural resource composed of organic and inorganic material. Nitrogen, one of the essential elements, is traditionally measured using laboratory methods. The development of hyperspectral imaging enables the cost-effective acquisition of both spectral and spatial information for detecting physical, chemical, and biological attributes of the soil samples. The presented work evaluates the suitability of airborne hyperspectral imaging for determining soil nitrogen content and producing a soil nitrogen map on a pixel-wise basis. The measurement of spatial variability of the soil nitrogen content was taken at two fields located at Rudice, in northeast Brno, Czech Republic, using laboratory methods and a handheld spectrometer. The soil reflectance was also recorded using airborne-mounted imaging spectroscopy sensors. A partial least squares regression was used to develop a model for the calibration of the data collected with a portable spectrometer and to predict the total nitrogen in the soils based on hyperspectral images from airborne sensors. The determination factor for the PLSR model presented in this paper reached an R2 of 0.44. The model’s performance could be improved by using a handheld spectrometer with a wider spectral range, using the same acquisition period for field data collection and hyperspectral imaging, and enlarging the sample size. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
ISPRS Int. J. Geo-Inf. 2021, 10(4), 239; https://doi.org/10.3390/ijgi10040239 - 07 Apr 2021
Viewed by 630
Abstract
Strawberries (Fragaria × ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout [...] Read more.
Strawberries (Fragaria × ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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Article
Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia
ISPRS Int. J. Geo-Inf. 2020, 9(11), 663; https://doi.org/10.3390/ijgi9110663 - 04 Nov 2020
Viewed by 1010
Abstract
The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns [...] Read more.
The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields. Full article
(This article belongs to the Special Issue Earth Observation and GIScience for Agricultural Applications)
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