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Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities

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 (31 December 2022) | Viewed by 38564

Special Issue Editors

School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
Interests: agriculture remote sensing; spatio-temporal data fusion; time series analysis
Special Issues, Collections and Topics in MDPI journals
Applied Geosolutions, Durham, NH 03824, USA
Interests: radar remote sensing; Synthetic Aperture Radar (SAR) signal; image processing; software development; soil moisture retrevial
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: crop monitoring with remote sensing; big earth data for cropland monitoring; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate and timely information of crop growth condition is essential to precision farming and sustainable agricultural production. Remote sensing data acquired by different platforms (e.g., satellite, airborne, UAV and ground) have been increasingly used to capture crop growth at various spatial and temporal scales. More recently, many newly developed sensors and data acquisition technologies have been developed to further enhance the capability of remote sensing in supporting crop growth monitoring and yield prediction. Multispectral imageries with red-edge bands, hyperspectral imageries and synthetic aperture radar imageries have become commonly available, providing unprecedented data support to stimulate innovation for crop monitoring. When combined with new data processing algorithms (e.g., machine learning and big data architecture) and high-performance computers, the power of remote technology has been unleashed.

Given the improvement of advanced sensor technologies, the early detection of crop stress and the quantitation impacts on crop yield remain challenging. This special issue calls for innovative research in using remote sensing and other cutting-edge technologies such as data fusion and artificial intelligence to tackle the issues facing the modern field crop production. The topics include but are not limited to the following:

  • Site-management zone delineation in precision agriculture
  • Crop biophysical and biochemical parameter (e.g., LAI/fAPAR, leaf chlorophyll and leaf nitrogen) retrieval
  • Crop biomass and yield estimation
  • Crop logging detection
  • Crop stress (e.g., nutrient, pests, diseases, drought, and heat stress) monitoring
  • Crop progress (e.g., sowing, flowering and harvest) detection
  • In-season crop types classification
  • Sustainable agricultural practices

Dr. Taifeng Dong
Dr. Chunhua Liao
Dr. Xiaodong Huang
Dr. Miao Zhang
Prof. Dr. Jiali Shang
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

  • Crop monitoring
  • Crop stress
  • Crop phenology
  • Crop loss
  • Data fusion
  • Data assimilation
  • Crop yield prediction
  • Precision agriculture

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Published Papers (14 papers)

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16 pages, 13397 KiB  
Article
Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm
by Wei Wu, Xiaochun Zhong, Chaokai Lei, Yuanyuan Zhao, Tao Liu, Chengming Sun, Wenshan Guo, Tan Sun and Shengping Liu
Remote Sens. 2023, 15(5), 1280; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051280 - 25 Feb 2023
Viewed by 1250
Abstract
The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear [...] Read more.
The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 104/hm2 and 3.37%, respectively, and for YFM4, 13.65 × 104/hm2 and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation. Full article
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16 pages, 2749 KiB  
Article
Study of Genetic Variation in Bermuda Grass along Longitudinal and Latitudinal Gradients Using Spectral Reflectance
by Jingxue Zhang, Mengli Han, Liwen Wang, Minghui Chen, Chen Chen, Sicong Shen, Jiangui Liu, Chao Zhang, Jiali Shang and Xuebing Yan
Remote Sens. 2023, 15(4), 896; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040896 - 06 Feb 2023
Cited by 3 | Viewed by 1200
Abstract
Genetic variation among populations within plant species can have huge impact on canopy biochemistry and structure across broad spatial scales. Since canopy spectral reflectance is determined largely by canopy biochemistry and structure, spectral reflectance can be used as a means to capture the [...] Read more.
Genetic variation among populations within plant species can have huge impact on canopy biochemistry and structure across broad spatial scales. Since canopy spectral reflectance is determined largely by canopy biochemistry and structure, spectral reflectance can be used as a means to capture the variability of th genetic characteristics of plant species. In this study, we used spectral measurements of Bermuda grass [Cynodon dactylon (L.) Pers.] at both the leaf and canopy levels to characterize the variability of plant traits pertinent to phylogeographic variation along the longitudinal and latitudinal gradients. An integration of airborne multispectral and hyperspectral data allows for the exploitation of spectral variations to discriminate between the five different genotypic groups using ANOVA and RF models. We evaluated the spectral variability among high-latitude genotypic groups and other groups along the latitudinal gradients and assessed spectral variability along longitudinal gradients. Spectral difference was observed between genetic groups from the northern regions and those from other regions along the latitudinal gradient, which indicated the usefulness of spectral signatures for discriminating between genetic groups. The canopy spectral reflectance was better suited to discriminate between genotypes of Bermuda grass across multiple scales than leaf spectral data, as assessed using random forest models. The use of spectral reflectance, derived from remote sensing, for studying genetic variability across landscapes is becoming an emerging research topic, with the potential to monitor and forecast phenology, evolution and biodiversity. Full article
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18 pages, 2995 KiB  
Article
Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology
by Muhammad Moshiur Rahman, Andrew Robson and James Brinkhoff
Remote Sens. 2022, 14(23), 5942; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235942 - 24 Nov 2022
Cited by 2 | Viewed by 1814
Abstract
The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops’ resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced [...] Read more.
The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops’ resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced vegetation indices (EVIs) from 2017 to 2021 were used to retrieve canopy reflectance information that coincided with crop phenological stages, such as flowering (F), vegetative growth (V), fruit maturity (M), and harvest (H), in commercial avocado orchards in Bundaberg, Queensland and Renmark, South Australia. Tukey’s honestly significant difference (Tukey-HSD) test after one-way analysis of variance (ANOVA) with EVI metrics (EVImean and EVIslope) showed statistically significant differences between the four phenological stages. From a Pearson correlation analysis, a distinctive seasonal trend of EVIs was observed (R = 0.68 to 0.95 for Bundaberg and R = 0.8 to 0.96 for Renmark) in all 5 years, with the peak EVIs being observed at the M stage and the trough being observed at the F stage. However, a Tukey-HSD test showed significant variability in mean EVI values between seasons for both the Bundaberg and Renmark farms. The variability of the mean EVIs between the two farms was also evident with a p-value < 0.001. This novel study highlights the applicability of remote sensing for the monitoring of avocado phenological stages retrospectively and near-real time. This information not only supports the ‘benchmarking’ of seasonal orchard performance to identify potential impacts of seasonal weather variation and pest and disease incursions, but when seasonal growth profiles are aligned with the corresponding annual production, it can also be used to develop phenology-based yield prediction models. Full article
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20 pages, 6727 KiB  
Article
Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus
by Sebastian Varela, Xuying Zheng, Joyce N. Njuguna, Erik J. Sacks, Dylan P. Allen, Jeremy Ruhter and Andrew D. B. Leakey
Remote Sens. 2022, 14(21), 5333; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215333 - 25 Oct 2022
Cited by 2 | Viewed by 3040
Abstract
Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more [...] Read more.
Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding. Full article
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21 pages, 15410 KiB  
Article
Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
by Matteo Rolle, Stefania Tamea, Pierluigi Claps, Emna Ayari, Nicolas Baghdadi and Mehrez Zribi
Remote Sens. 2022, 14(15), 3712; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153712 - 03 Aug 2022
Cited by 4 | Viewed by 2145
Abstract
The reliability of crop-growth modelling is related to the accuracy of the information used to describe the agricultural growing phases. A proper knowledge of sowing periods has a significant impact on the effectiveness of any analysis based on modeled crop growth. In this [...] Read more.
The reliability of crop-growth modelling is related to the accuracy of the information used to describe the agricultural growing phases. A proper knowledge of sowing periods has a significant impact on the effectiveness of any analysis based on modeled crop growth. In this work, an estimation of maize actual sowing periods for year 2019 is presented, combining the optical and radar information from Sentinel-1 and Sentinel-2. The crop classification was conducted according to the information provided by local public authorities over an area of 30 km × 30 km, and 1154 maize fields were considered within the analysis. The combined use of NDVI and radar time series enabled a high-resolution assessment of sowing periods and the description of maize emergence through the soil, by detecting changes in the ground surface geometry. A radar-based index was introduced to detect the periods when plants emerge through the soil, and the sowing periods were retrieved considering the thermal energy needed by seeds to germinate and the daily temperatures before the emergence. Results show that 52% of maize hectares were sowed in late April, while about 30% were sowed during the second half of May. Sentinel-1 appears more suitable to describe the late growing phase of maize, since the radar backscattering is sensitive to the dry biomass of plants while the NDVI decreases because of the chromatic change of leaves. This study highlights the potential of synergy between remote sensing sources for agricultural management policies and improving the accuracy of crop-related modelling. Full article
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21 pages, 1638 KiB  
Article
A Bibliometric and Visualized Analysis of Research Progress and Trends in Rice Remote Sensing over the Past 42 Years (1980–2021)
by Tianyue Xu, Fumin Wang, Qiuxiang Yi, Lili Xie and Xiaoping Yao
Remote Sens. 2022, 14(15), 3607; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153607 - 28 Jul 2022
Cited by 11 | Viewed by 2359
Abstract
Rice is one of the most important food crops around the world. Remote sensing technology, as an effective and rapidly developing method, has been widely applied to precise rice management. To observe the current research status in the field of rice remote sensing [...] Read more.
Rice is one of the most important food crops around the world. Remote sensing technology, as an effective and rapidly developing method, has been widely applied to precise rice management. To observe the current research status in the field of rice remote sensing (RRS), a bibliometric analysis was carried out based on 2680 papers of RRS published during 1980–2021, which were collected from the core collection of the Web of Science database. Quantitative analysis of the number of publications, top countries and institutions, popular keywords, etc. was conducted through the knowledge mapping software CiteSpace, and comprehensive discussions were carried out from the aspects of specific research objects, methods, spectral variables, and sensor platforms. The results revealed that an increasing number of countries and institutions have conducted research on RRS and a great number of articles have been published annually, among which, China, the United States of America, and Japan were the top three and the Chinese Academy of Sciences, Zhejiang University, and Nanjing Agricultural University were the first three research institutions with the largest publications. Abundant interest was paid to “reflectance”, followed by “vegetation index” and “yield” and the specific objects mainly focused on growth, yield, area, stress, and quality. From the perspective of spectral variables, reflectance, vegetation index, and back-scattering coefficient appeared the most frequently in the frontiers. In addition to satellite remote sensing data and empirical models, unmanned air vehicle (UAV) platforms and artificial intelligence models have gradually become hot topics. This study enriches the readers’ understanding and highlights the potential future research directions in RRS. Full article
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21 pages, 10679 KiB  
Article
Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data
by Qinghua Xie, Qi Dou, Xing Peng, Jinfei Wang, Juan M. Lopez-Sanchez, Jiali Shang, Haiqiang Fu and Jianjun Zhu
Remote Sens. 2022, 14(11), 2668; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112668 - 02 Jun 2022
Cited by 7 | Viewed by 1812
Abstract
Crop identification and classification are of great significance to agricultural land use management. The physically constrained general model-based decomposition (PCGMD) has proven to be a promising method in comparison with the typical four-component decomposition methods in scattering mechanism interpretation and identifying vegetation types. [...] Read more.
Crop identification and classification are of great significance to agricultural land use management. The physically constrained general model-based decomposition (PCGMD) has proven to be a promising method in comparison with the typical four-component decomposition methods in scattering mechanism interpretation and identifying vegetation types. However, the robustness of PCGMD requires further investigation from the perspective of final applications. This paper aims to validate the efficiency of the PCGMD method on crop classification for the first time. Seven C-band time-series RADARSAT-2 images were exploited, covering the entire growing season over an agricultural region near London, Ontario, Canada. Firstly, the response and temporal evolution of the four scattering components obtained by PCGMD were analyzed. Then, a forward selection approach was applied to achieve the highest classification accuracy by searching an optimum combination of multi-temporal SAR data with the random forest (RF) algorithm. For comparison, the general model-based decomposition method (GMD), the original and its three improved Yamaguchi four-component decomposition approaches (Y4O, Y4R, S4R, G4U), were used in all tests. The results reveal that the PCGMD method is highly sensitive to seasonal crop changes and matches well with the real physical characteristics of the crops. Among all test methods used, the PCGMD method using six images obtained the optimum classification performance, reaching an overall accuracy of 91.83%. Full article
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17 pages, 6526 KiB  
Article
Estimation of Winter Wheat Tiller Number Based on Optimization of Gradient Vegetation Characteristics
by Fei Wu, Junchan Wang, Yuzhuang Zhou, Xiaoxin Song, Chengxin Ju, Chengming Sun and Tao Liu
Remote Sens. 2022, 14(6), 1338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061338 - 10 Mar 2022
Cited by 4 | Viewed by 2568
Abstract
Tiller are an important biological characteristic of wheat, a primary food crop. Accurate estimation of tiller number can help monitor wheat growth and is important in forecasting wheat yield. However, because of leaf cover and other factors, it is difficult to estimate tiller [...] Read more.
Tiller are an important biological characteristic of wheat, a primary food crop. Accurate estimation of tiller number can help monitor wheat growth and is important in forecasting wheat yield. However, because of leaf cover and other factors, it is difficult to estimate tiller number and the accuracy of estimates based on vegetation indices is low. In this study, a gradual change feature was introduced to optimize traditional prediction models of wheat tiller number. Accuracy improved in optimized models, and model R2 values for three varieties of winter wheat were 0.7044, 0.7060, and 0.7357. The optimized models improved predictions of tiller number in whole wheat fields. Thus, compared with the traditional linear model, the addition of a gradual change feature greatly improved the accuracy of model predictions of wheat tiller number. Full article
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18 pages, 9327 KiB  
Article
Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams
by Yahui Guo, Shouzhi Chen, Yongshuo H. Fu, Yi Xiao, Wenxiang Wu, Hanxi Wang and Kirsten de Beurs
Remote Sens. 2022, 14(2), 244; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020244 - 06 Jan 2022
Cited by 8 | Viewed by 2506
Abstract
Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and [...] Read more.
Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data. Full article
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22 pages, 7589 KiB  
Article
Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index
by Yiru Ma, Qiang Zhang, Xiang Yi, Lulu Ma, Lifu Zhang, Changping Huang, Ze Zhang and Xin Lv
Remote Sens. 2022, 14(1), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010136 - 29 Dec 2021
Cited by 30 | Viewed by 4023
Abstract
Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. [...] Read more.
Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. Accurate monitoring of cotton LAI has guiding significance for nutritional diagnosis and the accurate fertilization of cotton. This study aimed to obtain hyperspectral images of the cotton canopy using a UAV carrying a hyperspectral sensor and to extract effective information to achieve cotton LAI monitoring. In this study, cotton field experiments with different nitrogen application levels and canopy spectral images of cotton at different growth stages were obtained using a UAV carrying hyperspectral sensors. Hyperspectral reflectance can directly reflect the characteristics of vegetation, and vegetation indices (VIs) can quantitatively describe the growth status of plants through the difference between vegetation in different band ranges and soil backgrounds. In this study, canopy spectral reflectance was extracted in order to reduce noise interference, separate overlapping samples, and highlight spectral features to perform spectral transformation; characteristic band screening was carried out; and VIs were constructed using a correlation coefficient matrix. Combined with canopy spectral reflectance and VIs, multiple stepwise regression (MSR) and extreme learning machine (ELM) were used to construct an LAI monitoring model of cotton during the whole growth period. The results show that, after spectral noise reduction, the bands screened by the successive projections algorithm (SPA) are too concentrated, while the sensitive bands screened by the shuffled frog leaping algorithm (SFLA) are evenly distributed. Secondly, the calculation of VIs after spectral noise reduction can improve the correlation between vegetation indices and LAI. The DVI (540,525) correlation was the largest after standard normal variable transformation (SNV) pretreatment, with a correlation coefficient of −0.7591. Thirdly, cotton LAI monitoring can be realized only based on spectral reflectance or VIs, and the ELM model constructed by calculating vegetation indices after SNV transformation had the best effect, with verification set R2 = 0.7408, RMSE = 1.5231, and rRMSE = 24.33%, Lastly, the ELM model based on SNV-SFLA-SNV-VIs had the best performance, with validation set R2 = 0.9066, RMSE = 0.9590, and rRMSE = 15.72%. The study results show that the UAV equipped with a hyperspectral sensor has broad prospects in the detection of crop growth index, and it can provide a theoretical basis for precise cotton field management and variable fertilization. Full article
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19 pages, 3000 KiB  
Article
UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening
by Jianjun Wang, Qi Zhou, Jiali Shang, Chang Liu, Tingxuan Zhuang, Junjie Ding, Yunyu Xian, Lingtian Zhao, Weiling Wang, Guisheng Zhou, Changwei Tan and Zhongyang Huo
Remote Sens. 2021, 13(24), 5166; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245166 - 20 Dec 2021
Cited by 26 | Viewed by 3093
Abstract
In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study [...] Read more.
In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening. Full article
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22 pages, 18334 KiB  
Article
Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2
by Katharina Harfenmeister, Sibylle Itzerott, Cornelia Weltzien and Daniel Spengler
Remote Sens. 2021, 13(24), 5036; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245036 - 11 Dec 2021
Cited by 13 | Viewed by 3506
Abstract
Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter [...] Read more.
Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences. Full article
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17 pages, 7592 KiB  
Article
A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images
by Pengfei Chen, Xiao Ma, Fangyong Wang and Jing Li
Remote Sens. 2021, 13(17), 3526; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173526 - 05 Sep 2021
Cited by 9 | Viewed by 3037
Abstract
Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently [...] Read more.
Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein. Full article
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Review

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47 pages, 50821 KiB  
Review
A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization
by Hazhir Bahrami, Heather McNairn, Masoud Mahdianpari and Saeid Homayouni
Remote Sens. 2022, 14(22), 5633; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225633 - 08 Nov 2022
Cited by 5 | Viewed by 2388
Abstract
Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as [...] Read more.
Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can assist in mitigating these stresses by providing reliable product information. Research has been conducted to estimate crop biophysical parameters by destructive and non-destructive approaches. In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. To this end, they have investigated diverse sources of Earth observations, including radar and optical images with various spatial, spectral, and temporal resolutions. This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. This analysis is accomplished through a systematic review of 277 papers and documents the methods, challenges, and opportunities frequently cited in the scientific literature. The results revealed that research in this field had increased dramatically over this study period. In addition, the analyses confirmed that the normalized difference vegetation index (NDVI) had been the most studied vegetation index to estimate crop parameters. Moreover, this analysis showed that wheat and corn were the most studied crops, globally. Full article
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