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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 28925

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Special Issue Editors

Dr. Huajun Tang

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Guest Editor

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 10081, China
Interests: remote sensing; crop monitoring; image classification; soil; vegetation mapping; feature extraction; image processing; agricultural land use; crop disaster; global change; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wenbin Wu

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Guest Editor

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Haidian District, Beijing 100081, China
Interests: smart agriculture; agricultural system; crop mapping; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Wenjuan Li

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Guest Editor

State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: quantitative remote sensing in agriculture; crop phenotyping; crop monitoring; farmland monitoring; soil salinity

Special Issue Information

Dear Colleagues,

Currently, global agriculture production and food systems are facing great challenges as a result of several factors, such as the growing global population, climate change, decreasing global agricultural land areas and an increasing need for healthy diets. To tackle these growing challenges of agricultural production, complex agricultural ecosystems must be better understood. Conventional field surveys are usually time-consuming and costly since agricultural activities are carried out across large regions, and crop growing follows strong seasonal patterns that require timely monitoring. Emerging digital technologies, such as remote sensing from satellites, unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), the Internet of Things (IoT) and smartphones, provide efficient tools for continuously monitoring crops and their environments across a range of spatial scales, from kilometric to centimetric, as well as various temporal scales.

This Special Issue welcomes manuscripts that address any aspect of digital agricultural studies based on remote sensing technologies (satellites, UAV, UGV, IoT and smartphones), including, but not limited to: (1) the acquisition of remote sensing data, including the innovative development of intelligent equipment, new sensors and corresponding applications; (2) the estimation of the key traits of crops through the interpretation of raw reflectance or images, e.g., imagery processing algorithms, radiative transfer model development, improvements to inversion algorithms, and new traits that could be estimated from remote sensing data; (3) the monitoring of crop growth and status in regional, field, microplot or plant scales in near-real-time or continuous modes; (4) the precision management of fields from single or multiple remote sensing platforms, e.g., early mapping of crop types, field heterogeneity analysis, detection of crop stress and diseases, estimation of fertilization dates and amounts, prediction of flowering or harvest dates, and improvements in crop yield prediction; (5) the algorithms that combine data from multiple platforms or sensors to generate more accurate traits and achieve better precision managements.

Dr. Huajun Tang
Dr. Wenbin Wu
Dr. Wenjuan Li
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

digital agriculture
precision agriculture
phenotyping
intelligent equipment
early crop mapping
crop phenology
crop traits
crop yield
crop stress
crop diseases
data fusion
data assimilation
unmanned ground vehicle
unmanned aerial vehicle imagery
internet of Things
machine learning
deep learning

Published Papers (14 papers)

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27 pages, 22666 KiB

Open AccessArticle

High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data

by Chenhao Huang, Shucheng You, Aixia Liu, Penghan Li, Jianhua Zhang and Jinsong Deng

Remote Sens. 2023, 15(16), 4055; https://doi.org/10.3390/rs15164055 - 16 Aug 2023

Cited by 1 | Viewed by 1481

Abstract

Rice has always been one of the major food sources for human beings, and the monitoring and planning of cultivation areas to maintain food security and achieve sustainable development is critical for this crop. Traditional manual ground survey methods have been recognized as being laborious, while remote-sensing technology can perform the accurate mapping of paddy rice due to its unique data acquisition capabilities. The recently emerged Google Earth Engine (GEE) cloud-computing platform was found to be capable of storing and computing the resources required for the rapid processing of massive quantities of remote-sensing data, thereby revolutionizing traditional analysis patterns and offering unique advantages for large-scale crop mapping. Since the phenology of paddy rice depends on local climatic conditions, and considering the vast expanse of China with its outstanding geospatial heterogeneity, a zoning strategy was proposed in this study to separate the monsoon climate zone of China into two regions based on the Qinling Mountain–Huaihe River Line (Q-H Line), while discrepant basic data and algorithms have been adopted to separately map mid-season rice nationwide. For the northern regions, optical indices have been calculated based on Sentinel-2 images, growth spectral profiles have been constructed to identify phenological periods, and rice was mapped using One-Class Support Vector Machine (OCSVM); for the southern regions, microwave sequences have been constructed based on Sentinel-1 images, and rice was mapped using Random Forest (RF). By applying this methodological system, mid-season rice at 10 m spatial resolution was mapped on the GEE for the entire Chinese monsoon region in 2021. According to the accuracy evaluation coefficients and publicly released local statistical yearbook data, the relative error of the mapped areas in each province was limited to 10%, and the overall accuracy exceeded 85%. The results could indicate that mid-season rice can be mapped more accurately and efficiently on a China-wide scale with relatively few samples based on the proposed zoning strategy and mapping methods. By adjusting the parameters, the time interval for mapping could also be further extended. The powerful cloud-computing competence of the GEE platform was used to map rice on a large spatial scale, and the results can help governments to ascertain the distribution of mid-season rice across the country in a short-term period, which would be well suited to meeting the increasingly efficient and fine-grained decision-making and management requirements. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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21 pages, 4344 KiB

Open AccessEditor’s ChoiceArticle

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

by Matthew Wolfe, Iliyana D. Dobreva, Henry A. Ruiz-Guzman, Da Huo, Brody L. Teare, Tyler Adams, Mark E. Everett, Michael Bishop, Russell Jessup and Dirk B. Hays

Remote Sens. 2023, 15(15), 3832; https://doi.org/10.3390/rs15153832 - 01 Aug 2023

Viewed by 1379

Abstract

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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29 pages, 12696 KiB

Open AccessArticle

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

by Haiyang Zhang, Yao Zhang, Tingyao Gao, Shu Lan, Fanghui Tong and Minzan Li

Remote Sens. 2023, 15(11), 2951; https://doi.org/10.3390/rs15112951 - 05 Jun 2023

Cited by 1 | Viewed by 2035

Abstract

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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17 pages, 5658 KiB

Open AccessArticle

Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation

by Afolabi Agbona, Osval A. Montesinos-Lopez, Mark E. Everett, Henry Ruiz-Guzman and Dirk B. Hays

Remote Sens. 2023, 15(7), 1771; https://doi.org/10.3390/rs15071771 - 25 Mar 2023

Viewed by 1479

Abstract

Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 × AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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13 pages, 3746 KiB

Open AccessArticle

Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables

by Wenjuan Li, Marie Weiss, Bernard Garric, Luc Champolivier, Jingyi Jiang, Wenbin Wu and Frédéric Baret

Remote Sens. 2023, 15(6), 1539; https://doi.org/10.3390/rs15061539 - 11 Mar 2023

Cited by 2 | Viewed by 2088

Abstract

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R² = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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13 pages, 3838 KiB

Open AccessArticle

A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields

by Tao Sun, Hongliang Fang, Liding Chen and Ranhao Sun

Remote Sens. 2022, 14(20), 5185; https://doi.org/10.3390/rs14205185 - 17 Oct 2022

Cited by 1 | Viewed by 1665 | Correction

Abstract

As a major crop type in the global agroecosystem, paddy rice fields contribute to global greenhouse gas emissions. Surface albedo plays a vital role in estimating carbon emissions. However, it is difficult to find a broadband albedo estimation over paddy rice fields. The objective of this study was to derive an applicable method to improve albedo estimation over a paddy rice field. Field multiangle reflectance and surface albedo were collected throughout the growing season. A physically based model (AMBRALS) was utilized to reconstruct the directional reflectance into the spectral albedo. Multiple spectral albedos (at the wavelengths of 470, 550, 660, 850, 1243, 1640 and 2151 nm) were calculated, and new narrowband to broadband conversion coefficients were derived between the observed spectral albedo and broadband albedo. The conversion schemes showed high consistency with the field albedo observations in the shortwave (285–3000 nm), infrared (700–3000 nm), and visible (400–700 nm) bands. This method can help improve albedo estimation in partially submerged environments. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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22 pages, 5969 KiB

Open AccessArticle

Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery

by Ziqian Tang, Yaqin Sun, Guangtong Wan, Kefei Zhang, Hongtao Shi, Yindi Zhao, Shuo Chen and Xuewei Zhang

Remote Sens. 2022, 14(19), 4887; https://doi.org/10.3390/rs14194887 - 30 Sep 2022

Cited by 8 | Viewed by 1981

Abstract

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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20 pages, 7579 KiB

Open AccessArticle

Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods

by Caiwang Zheng, Amr Abd-Elrahman, Vance Whitaker and Cheryl Dalid

Remote Sens. 2022, 14(18), 4511; https://doi.org/10.3390/rs14184511 - 09 Sep 2022

Cited by 20 | Viewed by 2479

Abstract

Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques—multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)—were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R² of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R² from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SR_RedEdge), modified simple ratio red-edge (MSR_RedEdge) and chlorophyll index red and red-edge (CI_red&RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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18 pages, 5844 KiB

Open AccessArticle

Branch Identification and Junction Points Location for Apple Trees Based on Deep Learning

by Siyuan Tong, Yang Yue, Wenbin Li, Yaxiong Wang, Feng Kang and Chao Feng

Remote Sens. 2022, 14(18), 4495; https://doi.org/10.3390/rs14184495 - 09 Sep 2022

Cited by 6 | Viewed by 2204

Abstract

Branch identification is key to the robotic pruning system for apple trees. High identification accuracy and the positioning of junction points between branch and trunk are important prerequisites for pruning with a robotic arm. Recently, with the development of deep learning, Transformer has been gradually applied to the field of computer vision and achieved good results. However, the effect of branch identification based on Transformer has not been verified so far. Taking Swin-T and Resnet50 as a backbone, this study detected and segmented the trunk, primary branch and support of apple trees on the basis of Mask R-CNN and Cascade Mask R-CNN. The results show that, when Intersection over Union (IoU) is 0.5, the bbox mAP and segm mAP of Cascade Mask R-CNN Swin-T are the highest, which are 0.943 and 0.940; as for the each category identification, Cascade Mask R-CNN Swin-T shows no significant difference with the other three algorithms in trunk and primary branch; when the identified object is a support, the bbox AP and segm AP of Cascade Mask R-CNN Swin-T is significantly higher than that of other algorithms, which are 0.879 and 0.893. Next, Cascade Mask R-CNN SW-T is combined with Zhang & Suen to obtain the junction point. Compared with the direct application of Zhang & Suen algorithm, the skeleton obtained by this method is advantaged by trunk diameter information, and its shape and junction points position are closer to the actual apple trees. This model and method can be applied to follow-up research and offer a new solution to the robotic pruning system for apple trees. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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15 pages, 3863 KiB

Open AccessArticle

Stacking of Canopy Spectral Reflectance from Multiple Growth Stages Improves Grain Yield Prediction under Full and Limited Irrigation in Wheat

by Muhammad Adeel Hassan, Shuaipeng Fei, Lei Li, Yirong Jin, Peng Liu, Awais Rasheed, Rabiu Sani Shawai, Liang Zhang, Aimin Ma, Yonggui Xiao and Zhonghu He

Remote Sens. 2022, 14(17), 4318; https://doi.org/10.3390/rs14174318 - 01 Sep 2022

Cited by 1 | Viewed by 1419

Abstract

Grain yield (GY) prediction for wheat based on canopy spectral reflectance can improve selection efficiency in breeding programs. Time-series spectral information from different growth stages such as flowering to maturity is considered to have high accuracy in predicting GY and combining this information from multiple growth stages could effectively improve prediction accuracy. For this, 207 wheat cultivars and breeding lines were grown in full and limited irrigation treatments, and their canopy spectral reflectance was measured at the flowering, early, middle, and late grain fill stages. The potential of temporal spectral information at multiple growth stages for GY prediction was evaluated by a new method based on stacking the multiple growth stages data. Twenty VIs derived from spectral reflectance were used as the input feature of a support vector regression (SVR) to predict GY at each growth stage. The predicted GY values at multiple growth stages were trained by multiple linear regression (MLR) to establish a second-level prediction model. Results suggested that the prediction accuracy (R²) of VIs data from single growth stages ranged from 0.60 to 0.66 and 0.35 to 0.42 in the full and limited irrigation treatments, respectively. The prediction accuracy was increased by an average of 0.06, 0.07, and 0.07 after stacking the VIs of two, three, and four growth stages, respectively, under full irrigation. Similarly, under limited irrigation, the prediction accuracy was increased by 0.03, 0.04, and 0.04 by stacking the VIs of two, three, and four growth stages, respectively. Stacking of VIs of multiple important growth stages can increase the accuracy of GY prediction and application of a stable stacking model could increase the usefulness of data obtained from different phenotyping platforms. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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15 pages, 4742 KiB

Open AccessArticle

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

by Yiping Peng, Zhenhua Liu, Chenjie Lin, Yueming Hu, Li Zhao, Runyan Zou, Ya Wen and Xiaoyun Mao

Remote Sens. 2022, 14(14), 3311; https://doi.org/10.3390/rs14143311 - 09 Jul 2022

Cited by 4 | Viewed by 1791

Abstract

Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R²) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R² of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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24 pages, 12459 KiB

Open AccessArticle

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

by He Li, Peng Wang and Chong Huang

Remote Sens. 2022, 14(13), 3143; https://doi.org/10.3390/rs14133143 - 30 Jun 2022

Cited by 10 | Viewed by 2178

Abstract

With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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17 pages, 19540 KiB

Open AccessArticle

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

by Junjun Cao, Huijing Wang, Jinxiao Li, Qun Tian and Dev Niyogi

Remote Sens. 2022, 14(7), 1707; https://doi.org/10.3390/rs14071707 - 01 Apr 2022

Cited by 20 | Viewed by 4083

Abstract

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R² of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance. Full article

(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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Correction: Sun et al. A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields. Remote Sens. 2022, 14, 5185

by Tao Sun, Hongliang Fang, Liding Chen and Ranhao Sun

Remote Sens. 2023, 15(10), 2654; https://doi.org/10.3390/rs15102654 - 19 May 2023

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(This article belongs to the Special Issue Digital Farming with Remote Sensing)

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