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Special Issue "Crop Quantitative Monitoring with Remote Sensing"

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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: 30 June 2023 | Viewed by 4389

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Prof. Dr. Jianxi Huang

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Guest Editor

College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: crop type mapping; crop yield forecasting; data assimilation; crop growth models
Special Issues, Collections and Topics in MDPI journals

Dr. Qingling Wu

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Guest Editor

Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
Interests: image processing; data mining; data assimilation

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

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.

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

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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

Published Papers (4 papers)

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Open AccessArticle

A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data

Mohamed A. M. Abd Elbasit

and

Junxiao Zhang

Remote Sens. 2023, 15(5), 1305; https://doi.org/10.3390/rs15051305 - 26 Feb 2023

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Abstract

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work. Full article

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

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Open AccessArticle

Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion

Adrián Lapaz Olveira

Hernán Saínz Rozas

Mauricio Castro-Franco

and

Remote Sens. 2023, 15(3), 824; https://doi.org/10.3390/rs15030824 - 01 Feb 2023

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Abstract

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha⁻¹) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R² = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. Full article

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

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Open AccessArticle

An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence

and

Remote Sens. 2023, 15(3), 693; https://doi.org/10.3390/rs15030693 - 24 Jan 2023

Viewed by 731

Abstract

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIF_yield (normalized by absorbed photosynthetically active radiation), fluorescence yield (Φ_F) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (Φ_F-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), Φ_F-r (R = 0.53) and SIF (R = 0.51), and the response of Φ_F (R = 0.45) and SIF_yield (R = 0.34) to SL was weak. Under lightly diseased conditions, Φ_F-r (R = 0.62) showed the strongest response to disease, followed by SIF_yield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between Φ_F and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, Φ_F-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for Φ_F) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of Φ_F-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress. Full article

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

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Open AccessTechnical Note

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

and

Remote Sens. 2023, 15(7), 1825; https://doi.org/10.3390/rs15071825 - 29 Mar 2023

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Abstract

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R² = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters. Full article

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

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