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Advancements in Remote Sensing for Sustainable Agriculture

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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: 15 January 2025 | Viewed by 6489

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Special Issue Editors

Prof. Dr. Wenjiang Huang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; image processing; image classification; parameter mapping; system development; agricultural application
Special Issues, Collections and Topics in MDPI journals

Dr. Kun Wang

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: precision agriculture; crop growth model; harmonization of remote sensing data; remote sensing observational verifications and environmental monitoring

Prof. Dr. Jadu Dash

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Guest Editor

Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: land surface phenology; earth observation; biophysical variables; agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Tiecheng Huang

E-Mail Website
Guest Editor Assistant

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; agricultural ecological remote sensing; pests and diseases; remote sensing of natural resources and environment; habitat monitoring

Special Issue Information

Dear Colleagues,

The field of remote sensing has emerged as a catalyst for transformation in agriculture, offering unprecedented insights and possibilities for sustainable practices. Rapid advancements in sensor technologies, coupled with sophisticated methodologies, have ushered in an era where remote sensing plays a pivotal role in revolutionizing agricultural sustainability. The capacity to collect, analyze, and interpret data from diverse sources has empowered researchers and practitioners to address challenges ranging from crop management to environmental impact with unprecedented precision.

In the dynamic agricultural landscape, the convergence of state-of-the-art remote sensing technologies and sustainable practices provides fertile ground for exploration and innovation. This Special Issue serves as a crucible for the latest breakthroughs in utilizing remote sensing data to propel sustainable agriculture forward. In addition to the areas already highlighted, articles may address, but are not limited to, the following topics:

Remote sensing applications for precise crop health monitoring and accurate yield prediction.
Implementation of precision agriculture techniques utilizing satellite, aerial, and ground-based remote sensing technologies.
Assessment of the impact of land use changes on sustainable agriculture.
Monitoring and effective management of water resources in agriculture through remote sensing.
Integration of machine learning and artificial intelligence with remote sensing data for robust decision support in agriculture.
Evaluation of the environmental impact of agricultural activities through the lens of remote sensing.
Exploring innovative applications of remote sensing in developing and enhancing models for crop growth, aiming to optimize agricultural practices.
Investigating the influence of pests and diseases on crop production through the lens of remote sensing, with a focus on precision monitoring and management.

Prof. Dr. Wenjiang Huang
Dr. Kun Wang
Prof. Dr. Jadu Dash
Guest Editors

Dr. Tiecheng Huang
Guest Editor Assistant

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

remote sensing
sustainable agriculture
precision farming
crop monitoring
environmental impact
land use change
crop growth model
water resource management
machine learning
artificial intelligence
decision support systems

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Published Papers (6 papers)

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Research

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32 pages, 11057 KiB

Open AccessArticle

Monitoring Helicoverpa armigera Damage with PRISMA Hyperspectral Imagery: First Experience in Maize and Comparison with Sentinel-2 Imagery

by Fruzsina Enikő Sári-Barnácz, Mihály Zalai, Gábor Milics, Mariann Tóthné Kun, János Mészáros, Mátyás Árvai and József Kiss

Remote Sens. 2024, 16(17), 3235; https://doi.org/10.3390/rs16173235 - 31 Aug 2024

Viewed by 610

Abstract

The cotton bollworm (CBW) poses a significant risk to maize crops worldwide. This study investigated whether hyperspectral satellites offer an accurate evaluation method for monitoring maize ear damage caused by CBW larvae. The study analyzed the records of maize ear damage for four maize fields in Southeast Hungary, Csongrád-Csanád County, in 2021. The performance of Sentinel-2 bands, PRISMA bands, and synthesized Sentinel-2 bands was compared using linear regression, partial least squares regression (PLSR), and two-band vegetation index (TBVI) methods. The best newly developed indices derived from the TBVI method were compared with existing vegetation indices. In mid-early grain maize fields, narrow bands of PRISMA generally performed better than wide bands, unlike in sweet maize fields, where the Sentinel-2 bands performed better. In grain maize fields, the best index was the normalized difference of λ_A = 571 and λ_B = 2276 (R² = 0.33–0.54, RMSE 0.06–0.05), while in sweet maize fields, the best-performing index was the normalized difference of green (B03) and blue (B02) Sentinel-2 bands (R² = 0.54–0.72, RMSE 0.02). The findings demonstrate the advantages and constraints of remote sensing for plant protection and pest monitoring. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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20 pages, 14550 KiB

Open AccessArticle

Monitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data

by Fábio Marcelo Breunig, Ricardo Dalagnol, Lênio Soares Galvão, Polyanna da Conceição Bispo, Qing Liu, Elias Fernando Berra, William Gaida, Veraldo Liesenberg and Tony Vinicius Moreira Sampaio

Remote Sens. 2024, 16(15), 2686; https://doi.org/10.3390/rs16152686 - 23 Jul 2024

Viewed by 711

Abstract

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R² = 0.62, p < 0.01), reducing RMSE to 579 kg·ha⁻¹. Using only optical or SAR data yielded R² values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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17 pages, 3358 KiB

Open AccessArticle

Optimized Transfer Learning for Chlorophyll Content Estimations across Datasets of Different Species Using Sun-Induced Chlorophyll Fluorescence and Reflectance

by Yu-an Zhou, Zichen Huang, Weijun Zhou and Haiyan Cen

Remote Sens. 2024, 16(11), 1869; https://doi.org/10.3390/rs16111869 - 23 May 2024

Viewed by 765

Abstract

Remote sensing-based techniques have been widely used for chlorophyll content (C_ab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for C_ab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models’ transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting C_ab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting C_ab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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19 pages, 3476 KiB

Open AccessArticle

Early Detection of Rubber Tree Powdery Mildew by Combining Spectral and Physicochemical Parameter Features

by Xiangzhe Cheng, Mengning Huang, Anting Guo, Wenjiang Huang, Zhiying Cai, Yingying Dong, Jing Guo, Zhuoqing Hao, Yanru Huang, Kehui Ren, Bohai Hu, Guiliang Chen, Haipeng Su, Lanlan Li and Yixian Liu

Remote Sens. 2024, 16(9), 1634; https://doi.org/10.3390/rs16091634 - 3 May 2024

Cited by 1 | Viewed by 940

Abstract

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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24 pages, 7072 KiB

Open AccessArticle

Multi-Year Cropland Mapping Based on Remote Sensing Data: A Case Study for the Khabarovsk Territory, Russia

by Konstantin Dubrovin, Andrey Verkhoturov, Alexey Stepanov and Tatiana Aseeva

Remote Sens. 2024, 16(9), 1633; https://doi.org/10.3390/rs16091633 - 3 May 2024

Viewed by 896

Abstract

Cropland mapping using remote sensing data is the basis for effective crop monitoring, crop rotation control, and the detection of irrational land use. Classification using Normalized Difference Vegetation Index (NDVI) time series from multi-year data requires additional time costs, especially when sentinel data are sparse. Approximation by nonlinear functions was proposed to solve this problem. Time series of weekly NDVI composites were plotted using multispectral Sentinel-2 (Level-2A) images at a resolution of 10 m for sites in Khabarovsk District from April to October in the years 2021 and 2022. Missing values due to the lack of suitable images for analysis were recovered using cubic polynomial, Fourier series, and double sinusoidal function approximation. The classes that were considered included crops, namely, soybean, buckwheat, oat, and perennial grasses, and fallow. The mean absolute percentage error (MAPE) of each class fitting was calculated. It was found that Fourier series fitting showed the highest accuracy, with a mean error of 8.2%. Different classifiers, such as the support vector machine (SVM), random forest (RF), and gradient boosting (GB), were comparatively evaluated. The overall accuracy (OA) for the site pixels during the cross-validation (Fourier series restored) was 67.3%, 87.2%, and 85.9% for the SVM, RF, and GB classifiers, respectively. Thus, it was established that the best result in terms of combined accuracy, performance, and limitations in cropland mapping was achieved by composite construction using Fourier series and machine learning using GB. Similar results should be expected in regions with similar cropland structures and crop phenological cycles, including other regions of the Far East. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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Review

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41 pages, 10304 KiB

Open AccessReview

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

by Pedro Marques, Luís Pádua, Joaquim J. Sousa and Anabela Fernandes-Silva

Remote Sens. 2024, 16(8), 1324; https://doi.org/10.3390/rs16081324 - 9 Apr 2024

Cited by 1 | Viewed by 1856

Abstract

This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture)

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