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18 pages, 4978 KiB

Open AccessArticle

Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data

by Guizhi Tian and Liming Zhu

Agronomy 2024, 14(4), 758; https://doi.org/10.3390/agronomy14040758 - 06 Apr 2024

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Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote [...] Read more.

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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17 pages, 32017 KiB

Open AccessArticle

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

by Angel James Medina Medina, Rolando Salas López, Jhon Antony Zabaleta Santisteban, Katerin Meliza Tuesta Trauco, Efrain Yury Turpo Cayo, Nixon Huaman Haro, Manuel Oliva Cruz and Darwin Gómez Fernández

Agronomy 2024, 14(3), 557; https://doi.org/10.3390/agronomy14030557 - 08 Mar 2024

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Abstract

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in [...] Read more.

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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29 pages, 11489 KiB

Open AccessArticle

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

by Onggarbek Alipbeki, Chaimgul Alipbekova, Gauhar Mussaif, Pavel Grossul, Darima Zhenshan, Olesya Muzyka, Rimma Turekeldiyeva, Dastan Yelubayev, Daniyar Rakhimov, Przemysław Kupidura and Eerassyl Aliken

Agronomy 2024, 14(2), 268; https://doi.org/10.3390/agronomy14020268 - 25 Jan 2024

Viewed by 1128

Abstract

Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze [...] Read more.

Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze changes in the composition of the land use/land cover (LULC) of Korgalzhyn District from 2010 to 2021 and predict LU transformation by 2030 and 2050. Landsat image classification was performed using Random Forest on the Google Earth Engine. The combined CA-ANN model was used to predict LULC changes by 2030 and 2050, and studies were carried out using the MOLUSCE plugin. The results of these studies showed that from 2010 to 2021, there was a steady increase in the share of ploughable land and an adequate reduction in grassland. It is established that, in 2030 and 2050, this trend will continue. At the same time, there will be no drastic changes in the composition of other land classes. The obtained results can be helpful for the development of land management plans and development policies for the Korgalzhyn District. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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21 pages, 6051 KiB

Open AccessArticle

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

by Haixia Jin, Jingjing Peng, Rutian Bi, Huiwen Tian, Hongfen Zhu and Haoxi Ding

Agronomy 2024, 14(1), 175; https://doi.org/10.3390/agronomy14010175 - 12 Jan 2024

Viewed by 846

Abstract

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire [...] Read more.

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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17 pages, 4843 KiB

Open AccessArticle

Cropland Inundation Mapping in Rugged Terrain Using Sentinel-1 and Google Earth Imagery: A Case Study of 2022 Flood Event in Fujian Provinces

by Mengjun Ku, Hao Jiang, Kai Jia, Xuemei Dai, Jianhui Xu, Dan Li, Chongyang Wang and Boxiong Qin

Agronomy 2024, 14(1), 138; https://doi.org/10.3390/agronomy14010138 - 05 Jan 2024

Viewed by 885

Abstract

South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation [...] Read more.

South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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21 pages, 9661 KiB

Open AccessArticle

Early Mapping Method for Different Planting Types of Rice Based on Planet and Sentinel-2 Satellite Images

by Yunfei Yu, Linghua Meng, Chong Luo, Beisong Qi, Xinle Zhang and Huanjun Liu

Agronomy 2024, 14(1), 137; https://doi.org/10.3390/agronomy14010137 - 04 Jan 2024

Viewed by 1117

Abstract

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The [...] Read more.

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R² by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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26 pages, 13649 KiB

Open AccessArticle

Large-Scale Cotton Classification under Insufficient Sample Conditions Using an Adaptive Feature Network and Sentinel-2 Imagery in Uzbekistan

by Jaloliddin Jaloliddinov, Xiangyu Tian, Yongqing Bai, Yonglin Guo, Zhengchao Chen, Yixiang Li and Shaohua Wang

Agronomy 2024, 14(1), 75; https://doi.org/10.3390/agronomy14010075 - 28 Dec 2023

Viewed by 797

Abstract

Cotton (Gossypium hirsutum L.) is one of the main crops in Uzbekistan, which makes a major contribution to the country’s economy. The cotton industry has played a pivotal role in the economic landscape of Uzbekistan for decades, generating employment opportunities and supporting [...] Read more.

Cotton (Gossypium hirsutum L.) is one of the main crops in Uzbekistan, which makes a major contribution to the country’s economy. The cotton industry has played a pivotal role in the economic landscape of Uzbekistan for decades, generating employment opportunities and supporting the livelihoods of countless individuals across the country. Therefore, having precise and up-to-date data on cotton cultivation areas is crucial for overseeing and effectively managing cotton fields. Nonetheless, there is currently no extensive, high-resolution approach that is appropriate for mapping cotton fields on a large scale, and it is necessary to address the issues related to the absence of ground-truth data, inadequate resolution, and timeliness. In this study, we introduced an effective approach for automatically mapping cotton fields on a large scale. A crop-type mapping method based on phenology was conducted to map cotton fields across the country. This research affirms the significance of phenological metrics in enhancing the mapping of cotton fields during the growing season in Uzbekistan. We used an adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images and automatically generated samples. The map achieved an overall accuracy (OA) of 0.947 and a kappa coefficient (KC) of 0.795. This model can be integrated with additional datasets to predict yield based on the identified crop type, thereby enhancing decision-making processes related to supply chain logistics and seasonal production forecasts. The early boll opening stage, occurring approximately a little more than a month before harvest, yielded the most precise identification of cotton fields. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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19 pages, 3030 KiB

Open AccessArticle

Integrating Spectral, Textural, and Morphological Data for Potato LAI Estimation from UAV Images

by Mingbo Bian, Zhichao Chen, Yiguang Fan, Yanpeng Ma, Yang Liu, Riqiang Chen and Haikuan Feng

Agronomy 2023, 13(12), 3070; https://doi.org/10.3390/agronomy13123070 - 15 Dec 2023

Cited by 1 | Viewed by 925

Abstract

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data [...] Read more.

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + H_dsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R² increase. The performance of H_dsm was verified by including it either as one input feature or not. Results showed that H_dsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R². Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + H_dsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R² of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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19 pages, 13235 KiB

Open AccessArticle

Identifying the Spatio-Temporal Change in Winter Wheat–Summer Maize Planting Structure in the North China Plain between 2001 and 2020

by Bo Yang, Jinglei Wang, Shenglin Li and Xiuqiao Huang

Agronomy 2023, 13(11), 2712; https://doi.org/10.3390/agronomy13112712 - 27 Oct 2023

Cited by 1 | Viewed by 1142

Abstract

Tracking winter wheat–summer maize distribution is crucial for the management of agricultural water resources in the water-scarce North China Plain (NCP). However, the spatio-temporal change in planting structure that has occurred during the last 20 years remains unclear. Therefore, winter wheat–summer maize distribution [...] Read more.

Tracking winter wheat–summer maize distribution is crucial for the management of agricultural water resources in the water-scarce North China Plain (NCP). However, the spatio-temporal change in planting structure that has occurred during the last 20 years remains unclear. Therefore, winter wheat–summer maize distribution between 2001 and 2020 was determined via the maximum likelihood algorithm of supervised classification and a threshold method using the MODIS NDVI product MOD13Q1 and Landsat 5/7 images. The results reveal that dividing distributions into six sample categories—winter wheat–summer maize, winter wheat–rice, spring maize, cotton, other double-cropping systems, and fruit trees—proved to be an efficient way to discriminate winter wheat–summer maize distribution, with R² and RMSE values ranging from 0.738 to 0.901 and from 179.05 to 215.72 km², respectively. From 2001 to 2020, the planting area continually expanded, experiencing a significant growth of 3.32 × 10⁴ km² (23.44%). Specifically, the planting area decreased by 2982.13 km² (10.06%) in the northern part of the NCP, including the Beijing–Tianjin–Hebei region, while it increased by 3.62 × 10⁴ km² (32.30%) in the middle and southern parts, encompassing Shandong, Henan, Anhui, and Jiangsu provinces. The stable growing region was primarily concentrated in the middle of the Hebei Plain, along the Yellow River irrigation areas and humid zones of the southwest, accounting for 75–85% of the total NCP planting area. Our results can provide references for adjusting agricultural planting structures, formulating food security strategies, and optimizing the management of water resources in the NCP. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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17 pages, 8276 KiB

Open AccessArticle

Crop Mapping and Spatio–Temporal Analysis in Valley Areas Using Object-Oriented Machine Learning Methods Combined with Feature Optimization

by Xiaoli Fu, Wenzuo Zhou, Xinyao Zhou and Yichen Hu

Agronomy 2023, 13(10), 2467; https://doi.org/10.3390/agronomy13102467 - 24 Sep 2023

Cited by 1 | Viewed by 1082

Abstract

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on [...] Read more.

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km²/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km²/year and 14.28 km²/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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14 pages, 15253 KiB

Open AccessArticle

Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images

by Changwei Wang, Yongchong Chen, Zhipei Xiao, Xianming Zeng, Shihao Tang, Fei Lin, Luxiang Zhang, Xuelian Meng and Shaoqun Liu

Agronomy 2023, 13(5), 1222; https://doi.org/10.3390/agronomy13051222 - 26 Apr 2023

Cited by 4 | Viewed by 1258

Abstract

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at [...] Read more.

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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Review

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18 pages, 2469 KiB

Open AccessReview

Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review

by Qiong Zheng, Wenjiang Huang, Qing Xia, Yingying Dong, Huichun Ye, Hao Jiang, Shuisen Chen and Shanyu Huang

Agronomy 2023, 13(7), 1851; https://doi.org/10.3390/agronomy13071851 - 13 Jul 2023

Cited by 3 | Viewed by 4910

Abstract

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that [...] Read more.

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS Technology in Agriculture)

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