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Crop Quantitative Monitoring with Remote Sensing II

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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: 31 January 2025 | Viewed by 9897

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Special Issue Editors

Dr. Tiecheng Bai

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Guest Editor

School of Information Engineering, Tarim University, Alaer 843300, China
Interests: crop growth simulation; precision agriculture; vegetation parameter retrieval; remote sensing assimilation
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Prof. Dr. Jianxi Huang

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Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: crop type mapping; crop yield forecasting; data assimilation; crop growth models
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Dr. Qingling Wu

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Department of Biology, University of Oxford, OXFORD OX1 3SZ, UK
Interests: image processing; data mining; data assimilation
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Prof. Dr. Wei Su

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Guest Editor

College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: LiDAR application in vegetation; vegetation parameter retrieval; vegetation monitoring; hyperspectral remote sensing
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Special Issue Information

Dear Colleagues,

Crop quantitative monitoring is important to decision support in crop production management practices for sustainable agricultural development and global food security. Today, remote sensing has been extensively used to monitor agricultural fields for crop field mapping, crop phenology, crop disaster stress, real-time crop yield estimation or forecasting, and so on. Various advanced quantitative algorithms have been developed for improved crop classification (e.g., long-term and high-resolution crop maps for wheat, maize, and rice), as well as time series for crop phenology detection and critical crop parameter retrieval (e.g., leaf area index retrieval from canopy radiative transfer model), crop disaster monitoring (drought, flooding, lodging, pests, and diseases), and so on. Applications can be at the global, national, regional, farm or field level, such as county-level yield prediction under climate change and agricultural emissions, which a combination of quantitative remote sensing and crop growth models can carry out.

Dr. Tiecheng Bai
Prof. Dr. Jianxi Huang
Dr. Qingling Wu
Prof. Dr. Wei Su
Guest Editors

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Keywords

quantitative remote sensing
time series analysis
crop growth models
data assimilation
machine learning
deep learning
climate change
crop parameter retrieval
crop growth monitoring
crop stress monitoring
crop disaster monitoring
crop phenology detection
crop type mapping
crop yield estimation or forecasting

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Related Special Issue

Crop Quantitative Monitoring with Remote Sensing in Remote Sensing (8 articles)

Published Papers (5 papers)

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Research

22 pages, 9686 KiB

Open AccessArticle

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

by Yuyang Ma, Gongxin Jiang, Jianxi Huang, Yonglin Shen, Haixiang Guan, Yi Dong, Jialin Li and Chuli Hu

Remote Sens. 2024, 16(5), 826; https://doi.org/10.3390/rs16050826 - 27 Feb 2024

Viewed by 1504

Abstract

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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23 pages, 11034 KiB

Open AccessArticle

Estimating Fraction of Absorbed Photosynthetically Active Radiation of Winter Wheat Based on Simulated Sentinel-2 Data under Different Varieties and Water Stress

by Zheng Sun, Liang Sun, Yu Liu, Yangwei Li, Luís Guilherme Teixeira Crusiol, Ruiqing Chen and Deji Wuyun

Remote Sens. 2024, 16(2), 362; https://doi.org/10.3390/rs16020362 - 16 Jan 2024

Viewed by 1975

Abstract

The fraction of absorbed photosynthetically active radiation (

f P A R

) is an important parameter reflecting the level of photosynthesis and growth status of vegetation, and is widely used in energy cycling, carbon cycling, and vegetation productivity estimation. In agricultural production, [...] Read more.

The fraction of absorbed photosynthetically active radiation (

f P A R

f P A R

is often combined with the light use efficiency model to estimate crop yield. Therefore, accurate estimation of PAR is of great importance for improving the accuracy of crop yield estimation and ensuring national food security. Existing studies based on vegetation indices have not considered the effects of genetic variety, light, and water stress on

f P A R

estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat

f P A R

. The stability of the vegetation index with the highest correlation in inverting

f P A R

under different cultivars, light, and water stress was tested, and then the model was validated at the satellite scale. Finally, a sensitivity analysis was performed. The results showed that the index model based on modified NDVI (MNDVI) had the highest correlation not only throughout the critical phenological period of winter wheat (

R^{2}

of 0.6649) but also under different varieties, observation dates, and water stress (

R^{2}

of 0.918, 0.881, and 0.830, respectively). It even performed the highest

R^{2}

of 0.8312 at the satellite scale. Moreover, through comparison, we found that considering water stress and variety differences can improve the estimation accuracy of

f P A R

. The study showed that using MNDVI for

f P A R

estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of

f P A R

by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution

f P A R

mapping. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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27 pages, 9512 KiB

Open AccessArticle

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

by Gengze Wang, Di Meng, Riqiang Chen, Guijun Yang, Laigang Wang, Hailiang Jin, Xiaosan Ge and Haikuan Feng

Remote Sens. 2024, 16(2), 277; https://doi.org/10.3390/rs16020277 - 10 Jan 2024

Cited by 4 | Viewed by 1680

Abstract

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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23 pages, 7370 KiB

Open AccessArticle

Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

by Di Pan, Changchun Li, Guijun Yang, Pengting Ren, Yuanyuan Ma, Weinan Chen, Haikuan Feng, Riqiang Chen, Xin Chen and Heli Li

Remote Sens. 2023, 15(22), 5413; https://doi.org/10.3390/rs15225413 - 18 Nov 2023

Cited by 1 | Viewed by 1551

Abstract

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IA_DAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IA_DAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IA_DAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IA_DAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IA_DAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IA_DAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R²) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IA_DAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IA_DAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IA_DAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IA_DAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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Graphical abstract

18 pages, 6258 KiB

Open AccessArticle

Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery

by Yu Liu, Liang Sun, Binhui Liu, Yongfeng Wu, Juncheng Ma, Wenying Zhang, Bianyin Wang and Zhaoyang Chen

Remote Sens. 2023, 15(19), 4800; https://doi.org/10.3390/rs15194800 - 1 Oct 2023

Cited by 6 | Viewed by 2382

Abstract

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘

(b_{1} - b_{2}) / (b_{3} - b_{4})

’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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