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Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities 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: closed (31 March 2024) | Viewed by 8072

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Special Issue Editors

Dr. Taifeng Dong

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Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Interests: remote sensing application in agriculture; bio-geophysical properties estimation; crop phenology; data assimilation
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Dr. Chunhua Liao

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Guest Editor

School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
Interests: agriculture remote sensing; spatio-temporal data fusion; time series analysis
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Dr. Miao Zhang

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Guest Editor

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
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Dr. Jiali Shang

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Guest Editor

Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Interests: remote sensing research and application development; land use mapping; precision agriculture
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Dr. Pengfei Chen

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Guest Editor

State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; geographic information system application

Dr. Hongquan Wang

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1. Department of Environement and Resource Sciences, Zhejiang University, HangZhou, China
2. Center for Research and Application of Remote Sensing (CARTEL), University of Sherbrooke, Sherbrooke, QC, Canada
Interests: remote sensing
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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.

The previous volume of ‘Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities’, was a great success. 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. Miao Zhang
Dr. Jiali Shang
Dr. Pengfei Chen
Dr. Hongquan Wang
Guest Editors

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Keywords

crop monitoring
crop stress
crop phenology
crop loss
data fusion
data assimilation
crop yield prediction
precision agriculture

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Related Special Issue

Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities in Remote Sensing (14 articles)

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Research

23 pages, 8396 KiB

Open AccessArticle

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

by Yafeng Li, Xingang Xu, Wenbiao Wu, Yaohui Zhu, Guijun Yang, Xiaodong Yang, Yang Meng, Xiangtai Jiang and Hanyu Xue

Remote Sens. 2024, 16(12), 2174; https://doi.org/10.3390/rs16122174 - 15 Jun 2024

Viewed by 1204

Abstract

Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves’ chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3,

R^{2}

= 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring. Full article

(This article belongs to the Special Issue Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities II)

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24 pages, 5315 KiB

Open AccessArticle

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

by Weilong Li, Jianjun Wang, Yuting Zhang, Quan Yin, Weiling Wang, Guisheng Zhou and Zhongyang Huo

Remote Sens. 2023, 15(24), 5715; https://doi.org/10.3390/rs15245715 - 13 Dec 2023

Cited by 6 | Viewed by 1988

Abstract

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R² = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R² = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R² = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy. Full article

(This article belongs to the Special Issue Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities II)

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19 pages, 3396 KiB

Open AccessArticle

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

by Yiqun Wang, Hui Huang and Radu State

Remote Sens. 2023, 15(20), 4962; https://doi.org/10.3390/rs15204962 - 14 Oct 2023

Cited by 2 | Viewed by 1752

Abstract

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping. Full article

(This article belongs to the Special Issue Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities II)

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20 pages, 12217 KiB

Open AccessArticle

Corn Phenology Detection Using the Derivative Dynamic Time Warping Method and Sentinel-2 Time Series

by Junyan Ye, Wenhao Bao, Chunhua Liao, Dairong Chen and Haoxuan Hu

Remote Sens. 2023, 15(14), 3456; https://doi.org/10.3390/rs15143456 - 8 Jul 2023

Cited by 8 | Viewed by 2252

Abstract

Accurate determination of crop phenology information is essential for effective field management and decision-making processes. Remote sensing time series analyses are widely employed to extract the phenological stages. Each crop’s phenological stage has its unique characteristic on the crop plant, while the satellite-derived crop phenology refers to some key transition dates in time series satellite observations. Current techniques primarily estimate specific phenological stages by detecting points with distinctive features on the remote sensing time series curve. But these stages may be different from the Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale, which is commonly used to identify the phenological development stages of crops. Moreover, when aiming to extract various phenological stages concurrently, it becomes necessary to adjust the extraction strategy for each unique feature. This need for distinct strategies at each stage heightens the complexity of simultaneous extraction. In this study, we utilize the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data and propose a phenology extraction framework based on the Derivative Dynamic Time Warping (DDTW) algorithm. This method is capable of simultaneously extracting complete phenological stages, and the results demonstrate that the Root Mean Square Errors (RMSEs, days) of detected phenology on the BBCH scale for corn were less than 6 days overall. Full article

(This article belongs to the Special Issue Crop Growth Monitoring Using Remote Sensing: Progress, Challenges and Opportunities II)

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