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Progresses in Agro-Geoinformatics

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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 (15 October 2022) | Viewed by 21271

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Special Issue Editors

Dr. Feng Gao

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Guest Editor

Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Interests: multi-sensor data fusion; crop phenology; biophysical parameter retrieval; time series analysis; near-real-time mapping
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Dr. Berk Üstündağ

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Guest Editor

Department of Computer Engineering, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
Interests: data fusion; artificial intelligence; signal procession; global optimization; cognitive communication; agricultural information systems

Dr. Liying Guo

E-Mail Website
Guest Editor

Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: agro-geoinformatic technology; sustainable development; land use and land cover change; public health; agricultural applications
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Dr. Yahui Di

E-Mail Website
Guest Editor

Center for Spatial Information Science and Systems, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Interests: artificial intelligence; crop mapping; crop monitoring and forecasting; crop yield modeling; data mining; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is the foundation of human society. In order to make sound decisions regarding agriculture, and ensure food security and agricultural sustainability, timely and accurate information on agricultural activities must be obtained, as well as events and phenomena that impact agricultural productivity, such as the type of crop planting and acreages, crop condition and progress, yield estimation and prediction, and agricultural-related natural disasters. In the recent decades, agro-geoinformatics has greatly enhanced our ability to collect, analyze, visualize, and model remote-sensing-based data for monitoring agricultural activities, productivity, events, and phenomena, deriving novel scientific knowledge and innovation in agriculture, and supporting informed agricultural decision making. Recent progress in digital technologies, such as sensor/sensing data, cloud computing, data fusion, system modeling, deep learning, artificial intelligence, social media, and cyber-infrastructure, has provided the agro-geoinformatics industry with new tools and methods for facilitating and promoting new innovative approaches in agriculture. This Special Issue will enable papers exploring the progress in innovative agro-geoinformatics research and applications to be published.

Papers that are suitable for the Special Issue must address relevant topics in agro-geoinformatics, and include sound implementation and validation procedures. We welcome submissions that describe state-of-the-art approaches in the progression of agro-geoinformatics, including, but not limited to, the following:

Research on agro-Geoinformatics theory, methodology and practice;
Geospatial information for stratification and sampling;
Data fusion, calibration, validation, and ground truths with geoinformatics;
Crop mapping, condition monitoring, acreage estimation and yield modeling;
Cropland evapotranspiration, soil moisture, and drought monitoring and assessment;
Water resource planning and management, irrigation and water usage;
Remote sensing-based agricultural disaster monitoring, assessment, mitigation and emergency response;
Global climate and environment change, and its impacts on agriculture sustainability and food security;
Remote sensing monitoring and modeling on agricultural greenhouse gases;
Agricultural environment and public health;
Wireless and mobile GIS for agricultural field work;
Agro-geoinformatics in government agricultural policy making and decision support;
Agro-geoinformatics education and outreach.
Crop/plant disease detection, monitoring and assessment;
Cloud computing and big data in agriculture-related applications;
Agricultural land use and land cover change;
Agricultural deep learning and artificial intelligence technology;
Spatial data uncertainty analysis;
Agro-geoinformatic system;
Agro-geoinformatic applications;

Dr. Feng Gao
Dr. Berk Üstündağ
Dr. Liying Guo
Dr. Yahui Di
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.

Keywords

Agro-geoinformatics
Agriculture
Remote sensing
Data processing
Assessment
Monitoring
Modeling
Spatio-temporal analysis

Published Papers (8 papers)

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Research

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21 pages, 6273 KiB

Open AccessArticle

Sea Surface Salinity Inversion Model for Changjiang Estuary and Adjoining Sea Area with SMAP and MODIS Data Based on Machine Learning and Preliminary Application

by Xiaoyu Zhang, Mingfei Wu, Wencong Han, Lei Bi, Yongheng Shang and Yingchun Yang

Remote Sens. 2022, 14(21), 5358; https://doi.org/10.3390/rs14215358 - 26 Oct 2022

Viewed by 1435

Abstract

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (<30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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20 pages, 39360 KiB

Open AccessArticle

Facilitating Typhoon-Triggered Flood Disaster-Ready Information Delivery Using SDI Services Approach—A Case Study in Hainan

by Lei Hu, Zhe Fang, Mingda Zhang, Liangcun Jiang and Peng Yue

Remote Sens. 2022, 14(8), 1832; https://doi.org/10.3390/rs14081832 - 11 Apr 2022

Cited by 6 | Viewed by 2648

Abstract

Natural disaster response and assessment are key elements of natural hazard monitoring and risk management. Currently, the existing systems are not able to meet the specific needs of many regional stakeholders worldwide; traditional approaches with field surveys are labor-intensive, time-consuming, and expensive, especially for severe disasters that affect a large geographic area. Recent studies have demonstrated that Earth observation (EO) data and technologies provide powerful support for the natural disaster emergency response. However, challenges still exist in support of the entire disaster lifecycle—preparedness, response, and recovery—which build the gaps between the disaster Spatial Data Infrastructure (SDI) already-in-place requirements and the EO capabilities. In order to tackle some of the above challenges, this paper demonstrates how to facilitate typhoon-triggered flood disaster-ready information delivery using an SDI services approach, and proposes a web-based remote sensing disaster decision support system to facilitate natural disaster response and impact assessment, which implements on-demand disaster resource acquisition, on-the-fly analysis, automatic thematic mapping, and decision report release. The system has been implemented with open specifications to facilitate interoperability. The typhoons and floods in Hainan Province, China, are used as typical scenarios to verify the system’s applicability and effectiveness. The system improves the automation level of the natural disaster emergency response service, and provides technical support for regional remote-sensing-based disaster mitigation in China. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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14 pages, 4158 KiB

Open AccessArticle

Desert Locust Cropland Damage Differentiated from Drought, with Multi-Source Remote Sensing in Ethiopia

by Woubet G. Alemu and Christopher S. R. Neigh

Remote Sens. 2022, 14(7), 1723; https://doi.org/10.3390/rs14071723 - 02 Apr 2022

Cited by 3 | Viewed by 3135

Abstract

In 2020, Ethiopia had the worst desert locust outbreak in 25 years, leading to food insecurity. Locust research has typically focused on predicting the paths and breeding grounds based on ground surveys and remote sensing of outbreak factors. In this study, we hypothesized that it is possible to detect desert locust cropland damage through the analysis of fine-scale (5–10 m) resolution satellite remote sensing datasets. We performed our analysis on 121 swarm point locations on croplands derived from the Food and Agriculture Organization (FAO) of the United Nations, and 94 ‘non-affected’ random cropland sample points generated for this study that are distributed within 20–25 km from the ‘center’ of swarm affected sample locations. Integrated Drought Condition Indices (IDCIs) and Vegetation Health Indices (VHIs) calculated for the affected sample locations for 2000–2020 were strongly correlated (R² > 0.90) with that of the corresponding non-affected group of sample sites. Drought indices were strongly correlated with the evaluation Standardized Precipitation Evapotranspiration Indices (SPEIs), and showed that 2020 was the wettest year since 2000. In 2020, the NDVI and backscatter coefficient of cropland phenologies from the affected versus non-affected cropland sample sites showed a slightly wider, but significant gap in March (short growing season) and August-October (long growing season). Thus, slightly wider gaps in cropland phenologies between the affected and non-affected sites were likely induced from the locust damage, not drought, with fine scale data representing a larger gap. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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22 pages, 8880 KiB

Open AccessArticle

Areas Available for the Potential Sustainable Expansion of Soy in Brazil: A Geospatial Assessment Using the SAFmaps Database

by Marjorie Mendes Guarenghi, Arnaldo Walter, Joaquim E. A. Seabra, Jansle Vieira Rocha, Nathália Vieira, Desirée Damame and João Luís Santos

Remote Sens. 2022, 14(7), 1628; https://doi.org/10.3390/rs14071628 - 28 Mar 2022

Viewed by 2115

Abstract

Recently, soybean production almost doubled in Brazil, reaching 122 million tonnes, and it is expected to increase even more. Brazil is the world’s largest producer and is primarily an exporter. From a sustainability point of view, soy production has been strongly criticized mainly in relation to deforestation, albeit for indirect effects. Soybean oil is a potential feedstock for the production of bio-jet fuels, which needs to be sustainable according to international criteria (sustainable aviation fuels—SAF). This paper aims to estimate the areas still available for soy expansion in Brazil, considering conditions that would allow the production of SAF. We used the SAFmaps platform, a geospatial database with information on the most promising bioenergy crops for SAF and their supply chains. Just by displacing pastures and observing a set of constraints, the total area available for expansion was estimated at 192.8 thousand km², of which 43% is of high suitability. These areas are concentrated in the Center-West region. Assuming a vertical supply chain, the results of the case studies of SAF production indicate potential feasibility, but some hypotheses considered are optimistic. Moreover, the results indicate that there can be sustainable production of soybean oil and contribution to the production of SAF. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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12 pages, 3353 KiB

Open AccessArticle

Interpreting the Trends of Extreme Precipitation in Florida through Pressure Change

by Chi Zhang, Songzi Wu, Tiantian Li, Ziwen Yu and Jiang Bian

Remote Sens. 2022, 14(6), 1410; https://doi.org/10.3390/rs14061410 - 15 Mar 2022

Cited by 5 | Viewed by 1906

Abstract

Precipitation is one of the many important natural factors impacting agriculture and natural resource management. Although statistics have been applied to investigate the non-stationary trend and the unpredictable variances of precipitation under climate change, existing methods usually lack a sound physical basis that can be generally applied in any location and at any time for future extrapolation, especially in tropical areas. Physically, the formation of precipitation is a result of ascending air which reduces air pressure and condenses moisture into drops, either by irregular terrain or atmospheric phenomena (e.g., via frontal lifting). Thus, in this paper, pressure change events (PCEs) will be used as a physical indicator of the stability of atmospheric systems to reveal the impact of temperature on precipitation in the tropical areas of Florida. By using data from both national and regional weather observation networks, this study segments the continuous observation series into PCE sequences for further analysis divided by dry and wet seasons. The results reveal that the frequency and intensity of PCE are highly associated with the occurrences of weather events. Decreasing pressure favors precipitation, and may turn extreme when the temperature and air moisture are sufficient to fuel the process. With similar intensity, decreasing pressure change events (DePCEs) generally bear a higher probability of precipitation (POP) and precipitation depth (PD) than increasing pressure change events (InPCEs). The frequency of alternating between InPCEs and DePCEs is subject to the temperature of the season and climate. Due to the seasonal fluctuations of weather characteristics, such as temperature and relative humidity, the dependence of extreme precipitation on these characteristics can be interpreted via PCE. A 7% increase rate of precipitation vs. temperature rise, determined by the Clausius—Clapeyron (C—C) relationship, can be observed from extreme precipitation with variances in the season and PCE types. Although indicated by other research, active vertical movement of air caused by a phase change in water at the frozen point is not pronounced in Florida. The response patterns of humidity to precipitation also vary by season and PCE types in extreme conditions. In summary, PCEs demonstrate reliable physical evidence of precipitation formation and can better associate the occurrence and intensity of extreme weather with other characteristics. In turn, such associations embody the underlying physical concepts present at any location in the world. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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17 pages, 5629 KiB

Open AccessArticle

An Object-Based Genetic Programming Approach for Cropland Field Extraction

by Caiyun Wen, Miao Lu, Ying Bi, Shengnan Zhang, Bing Xue, Mengjie Zhang, Qingbo Zhou and Wenbin Wu

Remote Sens. 2022, 14(5), 1275; https://doi.org/10.3390/rs14051275 - 05 Mar 2022

Cited by 13 | Viewed by 2843

Abstract

Cropland fields are the basic spatial units for agricultural management, and information about their distribution is critical for analyzing agricultural investments and management. However, the extraction of cropland fields of smallholder farms is a challenging task because of their irregular shapes and diverse spectrum. In this paper, we proposed a new object-based Genetic Programming (GP) approach to extract cropland fields. The proposed approach used the multiresolution segmentation (MRS) method to acquire objects from a very high resolution (VHR) image, and extracted spectral, shape and texture features as inputs for GP. Then GP was used to automatically evolve the optimal classifier to extract cropland fields. The results show that the proposed approach has obtained high accuracy in two areas with different landscape complexities. Further analysis show that the GP approach significantly outperforms five commonly used classifiers, including K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). By using different numbers of training samples, GP can maintain high accuracy with any volume of samples compared to other classifiers. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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13 pages, 4538 KiB

Open AccessCommunication

Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

by Chaofan Li, Qifei Han and Wenqiang Xu

Remote Sens. 2022, 14(5), 1210; https://doi.org/10.3390/rs14051210 - 01 Mar 2022

Cited by 3 | Viewed by 1829

Abstract

Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pastureland types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 ± 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (−1.53 Pg C) and the lowest with the MERRA (−2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 ± 0.06 Pg C from 1981–2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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15 pages, 2451 KiB

Open AccessTechnical Note

Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture

by Mike Teucher, Detlef Thürkow, Philipp Alb and Christopher Conrad

Remote Sens. 2022, 14(2), 393; https://doi.org/10.3390/rs14020393 - 15 Jan 2022

Cited by 6 | Viewed by 3409

Abstract

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders. Full article

(This article belongs to the Special Issue Progresses in Agro-Geoinformatics)

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