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Within-Season Agricultural Monitoring from Remotely Sensed Data

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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 January 2024) | Viewed by 14560

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Special Issue Editors

Dr. Ruyin Cao

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School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tao Cheng

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National Engineering & Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: reflectance spectroscopy; quantitative remote sensing of vegetation; crop growth monitoring; crop mapping; smart farming
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Prof. Dr. Ran Meng

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College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
Interests: plant stress; multiscale remote sensing; vegetation dynamics; smart agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Andrej Halabuk

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Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava, Slovakia
Interests: earth observation; applied remote sensing for agricultural and landscape studies

Dr. Clement E. Akumu

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Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, 3500 John A. Merritt Blvd, Nashville, TN 37209, USA
Interests: remote sensing; data fusion; mapping and monitoring; land cover/land use change; greenhouse gases and carbon stock accounting; precision agriculture; natural resource management

Special Issue Information

Dear Colleagues,

Remote sensing data have been successfully used to investigate various agricultural activities, such as crop type mapping, crop phenology detection, soil moisture assessment, and crop growth monitoring. From a practical point of view, agricultural management requires timely and accurate crop and soil information provided by remote sensing data within the crop-growing season (within-season). For example, it is preferable to acquire the spatial distribution of crop types in an earlier manner, which benefits timely crop management, protection, and yield forecast. However, current agricultural monitoring from remotely sensed data is often conducted after the crop-growing season. Within-season agricultural monitoring is still impeded by limitations in remote sensing data quality, monitoring algorithms, and computing platforms.

In this context, a Special Issue entitled “Within-Season Agricultural Monitoring from Remotely Sensed Data” is being planned in Remote Sensing journal. We welcome all research or review articles on agricultural monitoring as long as they focus on work carried out during the crop-growing season. In addition, methodology papers on processing within-season remote sensing data (e.g., time-series data) are also welcome. This issue has a broad range of topics, including crop monitoring (e.g., crop type classification, crop phenology detection, crop phenotyping, crop yield prediction) and agricultural condition investigations (e.g., agricultural drought, biotic/abiotic stresses). It should be noted that remotely sensed data from satellites, drones, or field instruments should be among the main data sources.

We look forward to receiving your contributions.

Dr. Ruyin Cao
Prof. Dr. Tao Cheng
Prof. Dr. Ran Meng
Dr. Andrej Halabuk
Dr. Clement E. Akumu
Guest Editors

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Keywords

agricultural remote sensing
crop types
crop phenotype
crop yield
in-season
phenotyping
plant stress
precision agriculture
time-series data
within-season

Published Papers (9 papers)

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Research

17 pages, 7772 KiB

Open AccessArticle

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

by Yanxi Zhao, Jiaoyang He, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao and Yongchao Tian

Remote Sens. 2024, 16(7), 1259; https://doi.org/10.3390/rs16071259 - 02 Apr 2024

Viewed by 496

Abstract

The timely and robust prediction of wheat yield is very significant for grain trade and food security. In this study, the yield prediction model was developed by coupling an ensemble model with multi-source data, including vegetation indices (VIs) and meteorological data. The results showed that green chlorophyll vegetation index (GCVI) is the optimal remote sensing (RS) variable for predicting wheat yield compared with other VIs. The accuracy of the adaptive boosting- long short-term memory (AdaBoost-LSTM) ensemble model was higher than the LSTM model. AdaBoost-LSTM coupled with optimal input data had the best performance. The AdaBoost-LSTM model had strong robustness for predicting wheat yield under different irrigation and extreme weather events in general. Additionally, the accuracy of AdaBoost-LSTM for rainfed counties was higher than that for irrigation counties in most years except extreme years. The yield prediction model developed with the characteristic variables of the window from February to April had higher accuracy and smaller data requirements, which was the best prediction window. Therefore, wheat yield can be accurately predicted by the AdaBoost-LSTM model one to two months of lead time before maturity in the HHHP. Overall, the AdaBoost-LSTM model can achieve accurate and robust yield prediction in large-scale regions. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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18 pages, 23193 KiB

Open AccessArticle

Time-Series-Based Spatiotemporal Fusion Network for Improving Crop Type Mapping

by Wenfang Zhan, Feng Luo, Heng Luo, Junli Li, Yongchuang Wu, Zhixiang Yin, Yanlan Wu and Penghai Wu

Remote Sens. 2024, 16(2), 235; https://doi.org/10.3390/rs16020235 - 07 Jan 2024

Viewed by 930

Abstract

Crop mapping is vital in ensuring food production security and informing governmental decision-making. The satellite-normalized difference vegetation index (NDVI) obtained during periods of vigorous crop growth is important for crop species identification. Sentinel-2 images with spatial resolutions of 10, 20, and 60 m are widely used in crop mapping. However, the images obtained during periods of vigorous crop growth are often covered by clouds. In contrast, time-series moderate-resolution imaging spectrometer (MODIS) images can usually capture crop phenology but with coarse resolution. Therefore, a time-series-based spatiotemporal fusion network (TSSTFN) was designed to generate TSSTFN-NDVI during critical phenological periods for finer-scale crop mapping. This network leverages multi-temporal MODIS-Sentinel-2 NDVI pairs from previous years as a reference to enhance the precision of crop mapping. The long short-term memory module was used to acquire data about the time-series change pattern to achieve this. The UNet structure was employed to manage the spatial mapping relationship between MODIS and Sentinel-2 images. The time distribution of the image sequences in different years was inconsistent, and time alignment strategies were used to process the reference data. The results demonstrate that incorporating the predicted critical phenological period NDVI consistently yields better crop classification performance. Moreover, the predicted NDVI trained with time-consistent data achieved a higher classification accuracy than the predicted NDVI trained with the original NDVI. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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18 pages, 9537 KiB

Open AccessArticle

Estimating Crop Sowing and Harvesting Dates Using Satellite Vegetation Index: A Comparative Analysis

by Grazieli Rodigheri, Ieda Del’Arco Sanches, Jonathan Richetti, Rodrigo Yoiti Tsukahara, Roger Lawes, Hugo do Nascimento Bendini and Marcos Adami

Remote Sens. 2023, 15(22), 5366; https://doi.org/10.3390/rs15225366 - 15 Nov 2023

Viewed by 1738

Abstract

In the last decades, several methodologies for estimating crop phenology based on remote sensing data have been developed and used to create different algorithms. Although many studies have been conducted to evaluate the different methodologies, a comprehensive understanding of the potential of the different current algorithms to detect changes in the growing season is still lacking, especially in large regions and with more than one crop per season. Therefore, this work aimed to evaluate different phenological metrics extraction methodologies. Using data from over 1500 fields distributed across Brazil’s central area, six algorithms, including CropPhenology, Digital Earth Australia tools package (DEA), greenbrown, phenex, phenofit, and TIMESAT, to extract soybean crop phenology were applied. To understand how robust the algorithms are to different input sources, the NDVI and EVI2 time series derived from MODIS products (MOD13Q1 and MOD09Q1) and from Sentinel-2 satellites were used to estimate the sowing date (SD) and harvest date (HD) in each field. The algorithms produced significantly different phenological date estimates, with Spearman’s R ranging between 0.26 and 0.82 when comparing sowing and harvesting dates. The best estimates were obtained using TIMESAT and phenex for SD and HD, respectively, with R greater than 0.7 and RMSE of 16–17 days. The DEA tools and greenbrown packages showed higher sensitivity when using different data sources. Double cropping is an added challenge, with no method adequately identifying it. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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19 pages, 12322 KiB

Open AccessArticle

Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

by Ruihao Cui, Zhenqi Hu, Peijun Wang, Jiazheng Han, Xi Zhang, Xuyang Jiang and Yingjia Cao

Remote Sens. 2023, 15(21), 5095; https://doi.org/10.3390/rs15215095 - 24 Oct 2023

Cited by 1 | Viewed by 889

Abstract

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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18 pages, 7484 KiB

Open AccessArticle

Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data

by Qun Wang, Boli Yang, Luchun Li, Hongyi Liang, Xiaolin Zhu and Ruyin Cao

Remote Sens. 2023, 15(20), 5043; https://doi.org/10.3390/rs15205043 - 20 Oct 2023

Cited by 1 | Viewed by 883

Abstract

Crop mapping at an earlier time within the growing season benefits agricultural management. However, crop spectral information is very limited at the early crop phenological stages, leading to difficulties for within-season crop identification. In this study, we proposed a deep learning-based fusion method for crop mapping within the growing season, which first learned a priori information (i.e., pre-season crop types) from historical crop planting data and then integrated the a priori information with the satellite-derived crop types estimated from spectral times-series data. We expect that preseason crop types provided by crop rotation patterns is an effective supplement to spectral information to generate reliable crop maps in the early growing season. We tested the proposed fusion method at three representative sites in the U.S. with different crop rotation intensities and one site with cloudy weather conditions in the Sichuan Province of China. The experimental results showed that the fusion method incorporated the strengths of pre-season crop type estimates and the spectral-based crop type estimates and thus achieved higher crop classification accuracy than the two estimates throughout the growing season. We found that pre-season crop estimates had a higher accuracy in the scenarios with either nearly continuous planting or half-time planting of the same crop. In addition, the historical crop type data strongly affected the performance of pre-season crop estimates, suggesting that high-quality historical crop planting data are particularly important for crop identification at earlier times in the growing season. Our study highlighted the great potential for near real-time crop mapping through the fusion of spectral information and crop rotation patterns. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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14 pages, 11032 KiB

Open AccessArticle

Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing

by Jinnuo Zhang, Dongdong Ma, Xing Wei and Jian Jin

Remote Sens. 2023, 15(12), 3057; https://doi.org/10.3390/rs15123057 - 11 Jun 2023

Viewed by 1376

Abstract

Remote sensing coupled with hyperspectral technology has become increasingly popular to investigate plant traits, showcasing its advantages in studying plant growth, health, and productivity. The quality of the collected hyperspectral images is crucial for subsequent data analysis and plant phenotyping studies. However, diurnal variations in spectral characteristics introduce more data variance in canopy reflectance spectra, raising the cost of subsequent analyses and compromising the performance of trait estimation models. In this study, a fixed gantry platform in a cornfield was used to capture visible and near-infrared (VNIR) hyperspectral images of corn canopies at consecutive time intervals. By applying reference board calibration and locally weighted scatterplot smoothing to minimize the effects of ambient light and daily growth, diurnal spectral changes across all involved VNIR wavelengths were investigated. Several distinct diurnal patterns were observed to have close connections with the plants’ physiological effects. Diurnal calibration models were established at every wavelength by employing the least squares polynomial algorithm, with the highest coefficient of determination reaching 0.84. Moreover, by employing diurnal calibration in canopy spectra processing, the reduction in spectral variance brought about by varying imaging time was evidently exhibited. This study not only reveals the diurnal spectral variation pattern at VNIR bands but also offers a reliable, straightforward, and low-cost approach to improve the quality of remote sensing data and reduce the inherent variance brought about via the different imaging times ensuring that comparable spectral analysis can be performed under relatively fair conditions. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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12 pages, 3394 KiB

Open AccessCommunication

Analysis of Relationship between Grain Yield and NDVI from MODIS in the Fez-Meknes Region, Morocco

by Mohamed Belmahi, Mohamed Hanchane, Nir Y. Krakauer, Ridouane Kessabi, Hind Bouayad, Aziz Mahjoub and Driss Zouhri

Remote Sens. 2023, 15(11), 2707; https://doi.org/10.3390/rs15112707 - 23 May 2023

Cited by 3 | Viewed by 2772

Abstract

Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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31 pages, 33030 KiB

Open AccessArticle

Fresh Yield Estimation of Spring Tea via Spectral Differences in UAV Hyperspectral Images from Unpicked and Picked Canopies

by Zongtai He, Kaihua Wu, Fumin Wang, Lisong Jin, Rongxu Zhang, Shoupeng Tian, Weizhi Wu, Yadong He, Ran Huang, Lin Yuan and Yao Zhang

Remote Sens. 2023, 15(4), 1100; https://doi.org/10.3390/rs15041100 - 17 Feb 2023

Cited by 2 | Viewed by 1916

Abstract

At present, spring tea yield is mainly estimated through a manual sampling survey. Obtaining yield information is time consuming and laborious for the whole spring tea industry, especially at the regional scale. Remote sensing yield estimation is a popular method used in large-scale grain crop fields, and few studies on the estimation of spring tea yield from remote sensing data have been reported. This is a similar spectrum of fresh tea yield components to that of the tea tree canopy. In this study, two types of unmanned aerial vehicle (UAV) hyperspectral images from the unpicked and picked Anji white tea tree canopies are collected, and research on the estimation of the spring tea fresh yield is performed using the differences identified in the single and combined chlorophyll spectral indices (CSIs) or leaf area spectral indices (LASIs) while also considering the changes in the green coverage of the tea tree canopy by way of a linear or piecewise linear function. The results are as follows: (1) in the linear model with a single index variable (LMSV), the accuracy of spring tea fresh yield models based on the selected CSIs was better than that based on the selected LASIs as a whole, in which the model based on the curvature index (CUR) was the best with regard to the accuracy metrics; (2) compared to the LMSVs, the accuracy performance of the piecewise linear model with the same index variables (PLMSVs) was obviously improved, with an encouraging root mean square error (RMSE) and validation determination coefficient (VR²); and (3) in the piecewise model with the combined index variables (PLMCVs), its evaluation metrics are also improved, in which the best performance of them was the CUR&CUR model with a RMSE (124.602 g) and VR² (0.625). It showed that the use of PLMSVs or PLMCVs for fresh tea yield estimation could reduce the vegetation index saturation of the tea tree canopy. These results show that the spectral difference discovered through hyperspectral remote sensing can provide the potential capability of estimating the fresh yield of spring tea on a large scale. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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24 pages, 5911 KiB

Open AccessArticle

Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles

by Mengfan Wei, Hongyan Wang, Yuan Zhang, Qiangzi Li, Xin Du, Guanwei Shi and Yiting Ren

Remote Sens. 2023, 15(3), 853; https://doi.org/10.3390/rs15030853 - 03 Feb 2023

Cited by 6 | Viewed by 2596

Abstract

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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