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Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring
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Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6475

Special Issue Editors

Dr. Oz Kira

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Faculty of Engineering Sciences, Ben Gurion University of the Negev, Be'er Sheva 84105, Israel
Interests: precision agriculture; biosphere-atmosphere interactions; environmental remote sensing
Prof. Dr. Asim Biswas

E-Mail Website
Guest Editor
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; vadose zone hydrology; precision agriculture; proximal soil sensing; digital soil mapping; soil spatial variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Improving food security is crucial as the world population continues to grow, and climatic changes’ future impact on agriculture is still unknown. New and innovative methods are required to increase crop yields and improve agricultural sustainability. One of the main challenges in our era, marked by increasing data accessibility, is gathering important information that farmers and stakeholders can leverage to upgrade their agricultural management practices. Agricultural monitoring using remote sensing has been, for the past few decades, a practice that enriches agriculture, helping to increase crop yields while reducing agriculture’s burden on the environment. Agricultural monitoring using remote sensing is continually being improved concomitantly, with advances in sensor technology upgrading agricultural practices. This Special Issue will concentrate on the contribution of newer satellites, e.g., Sentinel and TROPOMI. We will consider research papers demonstrating how these new sensors improve plant biotic and abiotic stress detection, crop growth and yield predictions, and agricultural nutrient tracking. Additionally, we welcome papers that present innovative uses of these satellites to enhance management practices.

Dr. Oz Kira
Dr. Asim Biswas
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. Sensors 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 2600 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

  • precision agriculture
  • remote sensing
  • satellite monitoring
  • climate monitoring
  • digital soil mapping
  • smart farming
  • agricultural management

Published Papers (5 papers)

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Research

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16 pages, 903 KiB  
Article
Remote Sensing Evaluation Drone Herbicide Application Effectiveness for Controlling Echinochloa spp. in Rice Crop in Valencia (Spain)
by Alberto San Bautista, Daniel Tarrazó-Serrano, Antonio Uris, Marta Blesa, Vicente Estruch-Guitart, Sergio Castiñeira-Ibáñez and Constanza Rubio
Sensors 2024, 24(3), 804; https://doi.org/10.3390/s24030804 - 25 Jan 2024
Viewed by 758
Abstract
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU [...] Read more.
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)
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17 pages, 3920 KiB  
Article
Identification and Spatial Analysis of Land Salinity in China’s Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery
by Liping Jiang, Guanghui Qiu and Xinyang Yu
Sensors 2023, 23(17), 7584; https://doi.org/10.3390/s23177584 - 01 Sep 2023
Cited by 1 | Viewed by 860
Abstract
Precise identification and spatial analysis of land salinity in China’s Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity [...] Read more.
Precise identification and spatial analysis of land salinity in China’s Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran’s I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6–8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high–high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low–low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies. Full article
(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)
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15 pages, 19095 KiB  
Article
Predicting Rice Lodging Risk from the Distribution of Available Nitrogen in Soil Using UAS Images in a Paddy Field
by Nozomi Kaneko Sato, Takeshi Tsuji, Yoshihiro Iijima, Nobuhito Sekiya and Kunio Watanabe
Sensors 2023, 23(14), 6466; https://doi.org/10.3390/s23146466 - 17 Jul 2023
Cited by 2 | Viewed by 1000
Abstract
Rice lodging causes a loss of yield and leads to lower-quality rice. In Japan, Koshihikari is the most popular rice variety, and it has been widely cultivated for many years despite its susceptibility to lodging. Reducing basal fertilizer is recommended when the available [...] Read more.
Rice lodging causes a loss of yield and leads to lower-quality rice. In Japan, Koshihikari is the most popular rice variety, and it has been widely cultivated for many years despite its susceptibility to lodging. Reducing basal fertilizer is recommended when the available nitrogen in soil (SAN) exceeds the optimum level (80–200 mg N kg−1). However, many commercial farmers prefer to simultaneously apply one-shot basal fertilizer at transplant time. This study investigated the relationship between the rice lodging and SAN content by assessing their spatial distributions from unmanned aircraft system (UAS) images in a Koshihikari paddy field where one-shot basal fertilizer was applied. We analyzed the severity of lodging using the canopy height model and spatially clarified a heavily lodged area and a non-lodged area. For the SAN assessment, we selected green and red band pixel digital numbers from multispectral images and developed a SAN estimating equation by regression analysis. The estimated SAN values were rasterized and compiled into a 1 m mesh to create a soil fertility map. The heavily lodged area roughly coincided with the higher SAN area. A negative correlation was observed between the rice inclination angle and the estimated SAN, and rice lodging occurred even within the optimum SAN level. These results show that the amount of one-shot basal fertilizer applied to Koshihikari should be reduced when absorbable nitrogen (SAN + fertilizer nitrogen) exceeds 200 mg N kg−1. Full article
(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)
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15 pages, 7045 KiB  
Article
Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data
by Salman Mirzaee and Ali Mirzakhani Nafchi
Sensors 2023, 23(4), 2134; https://doi.org/10.3390/s23042134 - 14 Feb 2023
Cited by 5 | Viewed by 1360
Abstract
Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the [...] Read more.
Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the aspatial and spatial relationships between vegetation growth status and drought in the southeastern South Dakota, USA. For this purpose, Landsat 8 OLI images from the months of April through September for the years 2016–2021, with cloud cover of less than 10%, were acquired. After that, radiometric calibration and atmospheric correction were performed on all of the images. Some spectral indices were calculated using the Band Math toolbox in ENVI 5.3 (Environment for Visualizing Images v. 5.3). In the present study, the extracted spectral indices from Landsat 8 OLI images were the Normalized Difference Vegetation Index (NDVI) and the Normalized Multiband Drought Index (NMDI). The results showed that the NDVI values for the month of July in different years were at maximum value at mostly pixels. Based on the statistical criteria, the best regression models for explaining the relationship between NDVI and NMDISoil were polynomial order 2 for 2016 to 2019 and linear for 2021. The developed regression models accounted for 96.7, 95.7, 96.2, 88.4, and 32.2% of vegetation changes for 2016, 2017, 2018, 2019, and 2021, respectively. However, there was no defined trend between NDVI and NMDISoil observed in 2020. In addition, pixel-by-pixel analyses showed that drought significantly impacted vegetation coverage, and 69.6% of the pixels were negatively correlated with the NDVI. It was concluded that the Landsat satellite images have potential information for studying the relationships between vegetation growth status and drought, which is the primary step in site-specific management. Full article
(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)
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Review

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20 pages, 3336 KiB  
Review
Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review
by Manuel Fernández-Urrutia, Manuel Arbelo and Artur Gil
Sensors 2023, 23(15), 6932; https://doi.org/10.3390/s23156932 - 03 Aug 2023
Cited by 1 | Viewed by 1718
Abstract
Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, [...] Read more.
Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions. Full article
(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)
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