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Remote Sensing for Crop Growth Monitoring

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 4738

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Special Issue Editors

Prof. Dr. Antonio Uris Martínez

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Guest Editor

Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: ultrasound propagation in complex media; ultrasonic metamaterials; ultrasound focusing; ultrasonic lenses
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Dr. Alberto San Bautista

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Guest Editor

Crop Production Department, Universitat Politècnica de València, Cno Vera 14, 46020 Valencia, Spain
Interests: agronomy and remote sensing
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Special Issue Information

Dear Colleagues,

In recent years, we have observed growing interest in the application of remote sensing in agriculture, offering novel opportunities to enhance crop production and yields and reduce environmental impact. Thanks to remote sensing, it is possible to monitor the phenological state of crops and detect pests and diseases, water requirements, and other factors that determine their production. However, we still need to work on analyzing and interpreting remote sensing data so that they can be used more effectively.

Therefore, this Special Issue aims to combine original research and review articles on recent advances, technologies, solutions, applications, and new challenges in crop growth monitoring.

Potential topics include, but are not limited to, the following:

Crop modelling;
Crop growth modelling;
Processing of remote sensing data for agronomic information;
Artificial intelligence-based platforms;
Statistical methods to identify and evaluate the different factors affecting crop growth based on remote sensing data;
Use of remote sensing data to identify crop growth and the factors affecting it;
Machine learning applications in agriculture based on remote sensing.

Prof. Dr. Antonio Uris Martínez
Dr. Alberto Bautista
Guest Editors

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Keywords

remote sensing
crop modelling
vegetation indices
machine learning
data processing
productivity
crop management

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Published Papers (5 papers)

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Research

16 pages, 7115 KB

Open AccessArticle

Generation of High-Resolution Time-Series NDVI Images for Monitoring Heterogeneous Crop Fields

by Sun-Hwa Kim, Jeong Eun, Inkwon Baek and Tae-Ho Kim

Sensors 2025, 25(16), 5183; https://doi.org/10.3390/s25165183 - 20 Aug 2025

Viewed by 305

Abstract

Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)

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18 pages, 3441 KB

Open AccessArticle

Assessment of Water Depth Variability and Rice Farming Using Remote Sensing

by Rubén Simeón, Constanza Rubio, Antonio Uris, Javier Coronado, Alba Agenjos-Moreno and Alberto San Bautista

Sensors 2025, 25(15), 4860; https://doi.org/10.3390/s25154860 - 7 Aug 2025

Viewed by 267

Abstract

Remote sensing is a widely used tool for crop monitoring to improve water management. Rice, a crop traditionally grown under flooded conditions, requires farmers to understand the relationship between crop reflectance, water depth and final yield. This study focused on seven commercial rice fields in 2022 and six in 2023, analyzing the correlations between water depth and Sentinel-2 reflectance over two growing seasons in Valencia, Spain. During the tillering stage across both seasons, water depth showed positive correlations with visible bands and negative correlations with NIR and SWIR bands. There were no correlations with the indices NDVI, GNDVI, NDRE and NDWI. The NIR band showed significant correlations across both seasons, with R² values of 0.69 and 0.71, respectively. In addition, the calculation of NIR anomalies for each field proved to be a good indicator of final yield anomalies. In 2022, anomalies above 10% corresponded to yield deviations above 500 kg·ha⁻¹, while in 2023, anomalies above 15% were associated with yield deviations above 1000 kg·ha⁻¹. The response of final yield to water level was positive up to average values of 9 cm. The use of the NIR band during the rice crop tillering stage can support farmers in improving irrigation management. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)

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23 pages, 7293 KB

Open AccessArticle

Possibilities of Using a Multispectral Camera to Assess the Effects of Biostimulant Application in Soybean Cultivation

by Paweł Karpiński and Sławomir Kocira

Sensors 2025, 25(11), 3464; https://doi.org/10.3390/s25113464 - 30 May 2025

Viewed by 594

Abstract

Soybean cultivation plays a crucial role in the global food system, providing raw materials for both the food and feed industries. To enhance cultivation efficiency, plant biostimulants are used to improve metabolism and stimulate growth. A key aspect of modern cultivation is the ability to rapidly and non-invasively assess crop status. One such method involves the use of drones equipped with multispectral cameras. This paper presents the results of an experimental study on soybean cultivation involving a natural biostimulant in the form of Epilobium angustifolium extract (commonly known as fireweed) and a commercial seaweed-based biostimulant, Kelpak. The research was conducted at an experimental farm in eastern Poland. The effectiveness of the preparations was evaluated using a drone-mounted multispectral camera. Changes in the values of selected spectral indices were analyzed: the Normalized Difference Red Edge Index (NDRE), the Leaf Chlorophyll Index (LCI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI). The study included a control group treated with pure water. Mathematical and statistical analyses of the mean values and standard deviations of the indices were conducted. The results demonstrated that multispectral scanning allows for the detection of significant differences between the effects of the E. angustifolium extract, the seaweed-based biostimulant, and the water control. These findings confirm the utility of this method for assessing the effectiveness of biostimulant applications in soybean cultivation. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)

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36 pages, 13780 KB

Open AccessArticle

Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines

by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven and Rebecca Retzlaff

Sensors 2025, 25(2), 431; https://doi.org/10.3390/s25020431 - 13 Jan 2025

Viewed by 1422

Abstract

Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy). Full article

(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)

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19 pages, 5440 KB

Open AccessArticle

Evaluating UAV-Based Remote Sensing for Hay Yield Estimation

by Kyuho Lee, Kenneth A. Sudduth and Jianfeng Zhou

Sensors 2024, 24(16), 5326; https://doi.org/10.3390/s24165326 - 17 Aug 2024

Cited by 2 | Viewed by 1450

Abstract

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel⁻¹) and 50 m (35 mm pixel⁻¹) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R² values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)

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