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Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture

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 (13 October 2023) | Viewed by 21573

Special Issue Editors

Department of Agricultural and Food Sciences-DISTAL, University of Bologna, Viale Fanin 44, North Wing, 40127 Bologna, Italy
Interests: foliar application of nutrients; organic farming; quantum GIS; ArcGIS; landsat vegetation indices; ECa directed to soil sampling technique; geostatistical analysis; precision agriculture; remote sensing

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Guest Editor
Senior IT Officer, IT Service Division (CSI), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
Interests: remote sensing applications in agriculture; data assimilation; agro-geoinformatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences—SGGW, 159 Nowoursynowska St., 02-776 Warsaw, Poland
Interests: statisticsbio; statistics; GIS; geostatistics; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current global food and agriculture system is facing major global challenges including climate change, population growth, environmental degradation biodiversity loss and natural resources depletion. It is recognized that agricultural digitalization might be one of the approaches that can help to counterbalance the current situation with the help of remote sensing and other technologies producing a huge amount of relevant data at parcel, farm and regional levels.

Today, we can model the crop yield performances, quality of the agricultural product and environmental effects of agricultural input usage in the spatio-temporal dimension by exploiting the remotely and proximal collectable data and by analyzing the relationships among crop, soil, weather and farm management practices. Several approaches are being developed to allow the precise management of farm resources as function of the within-field variability enacting a relevant leap compared to the traditional agricultural practices. However, the implementation of precision agriculture practices faces the challenge due to the diversity of factors which impact the crop yields and quality such as size of agricultural lands and variability of topography, soil, moisture and microclimatic conditions etc.

In this special issue; we focus on the state-of-art research on digital agriculture enabled by integrating remote, proximal and ground sensing technologies with crop, soil and weather data in search of a sustainable use of farm inputs. Innovative approaches are solicited on measurement, management/integration and use of data established by technologies for better understanding and managing the within-field variability and its relationship with remote, proximally and ground-sensed data. We invite you to submit reviews, case studies, or research articles for that focus on scientific methods, technological tools, and innovative statistical analyses, to capture the current advancements and fostering an open discussion on the future perspectives on the smart exploitation of spatial data integration in agriculture.

Dr. Abid Ali
Dr. Flavio Lupia
Dr. Zhongxin Chen
Dr. Dariusz Gozdowski
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

  • Spatio-temporal soil/crop/product quality variability
  • Spectral vegetation indices
  • Proximal soil and crop sensing
  • Soil spatial variability
  • Weather data and irrigation
  • Geostatistical analysis of soil and crop variability
  • Site-specific crop management
  • Precision farming
  • Digital agriculture

Published Papers (8 papers)

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Research

20 pages, 33326 KiB  
Article
3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data
by Mahdiyeh Fathi, Reza Shah-Hosseini and Armin Moghimi
Remote Sens. 2023, 15(23), 5551; https://doi.org/10.3390/rs15235551 - 29 Nov 2023
Cited by 1 | Viewed by 1406
Abstract
Ensuring food security in precision agriculture requires early prediction of soybean yield at various scales within the United States (U.S.), ranging from international to local levels. Accurate yield estimation is essential in preventing famine by providing insights into food availability during the growth [...] Read more.
Ensuring food security in precision agriculture requires early prediction of soybean yield at various scales within the United States (U.S.), ranging from international to local levels. Accurate yield estimation is essential in preventing famine by providing insights into food availability during the growth season. Numerous deep learning (DL) algorithms have been developed to estimate soybean yield effectively using time-series remote sensing (RS) data to achieve this goal. However, the training data with short time spans can limit their ability to adapt to the dynamic and nuanced temporal changes in crop conditions. To address this challenge, we designed a 3D-ResNet-BiLSTM model to efficiently predict soybean yield at the county level across the U.S., even when using training data with shorter periods. We leveraged detailed Sentinel-2 imagery and Sentinel-1 SAR images to extract spectral bands, key vegetation indices (VIs), and VV and VH polarizations. Additionally, Daymet data was incorporated via Google Earth Engine (GEE) to enhance the model’s input features. To process these inputs effectively, a dedicated 3D-ResNet architecture was designed to extract high-level features. These enriched features were then fed into a BiLSTM layer, enabling accurate prediction of soybean yield. To evaluate the efficacy of our model, its performance was compared with that of well-known models, including the Linear Regression (LR), Random Forest (RF), and 1D/2D/3D-ResNet models, as well as a 2D-CNN-LSTM model. The data from a short period (2019 to 2020) were used to train all models, while their accuracy was assessed using data from the year 2021. The experimental results showed that the proposed 3D-Resnet-BiLSTM model had a superior performance compared to the other models, achieving remarkable metrics (R2 = 0.791, RMSE = 5.56 Bu Ac−1, MAE = 4.35 Bu Ac−1, MAPE = 9%, and RRMSE = 10.49%). Furthermore, the 3D-ResNet-BiLSTM model showed a 7% higher R2 than the ResNet and RF models and an enhancement of 27% and 17% against the LR and 2D-CNN-LSTM models, respectively. The results highlighted our model’s potential for accurate soybean yield predictions, supporting sustainable agriculture and food security. Full article
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15 pages, 3556 KiB  
Communication
Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation
by Dimo Dimov and Patrick Noack
Remote Sens. 2023, 15(16), 3990; https://doi.org/10.3390/rs15163990 - 11 Aug 2023
Cited by 1 | Viewed by 915
Abstract
In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop [...] Read more.
In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92. Full article
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19 pages, 3914 KiB  
Article
A Study of the Relationships between Depths of Soil Constraints and Remote Sensing Data from Different Stages of the Growing Season
by Fathiyya Ulfa, Thomas G. Orton, Yash P. Dang and Neal W. Menzies
Remote Sens. 2023, 15(14), 3527; https://doi.org/10.3390/rs15143527 - 13 Jul 2023
Viewed by 759
Abstract
The presence of salinity and sodicity in the root zone can limit root development and impact crop yield. Topsoil constraints are likely to have the greatest impact on crop growth early in the growing season, when plant roots are still shallow. Later in [...] Read more.
The presence of salinity and sodicity in the root zone can limit root development and impact crop yield. Topsoil constraints are likely to have the greatest impact on crop growth early in the growing season, when plant roots are still shallow. Later in the growing season, subsoil constraints may have a greater impact as roots reach deeper into the soil. This study investigated whether different patterns of spatial variation in crop growth would be evident in remote sensing data captured from different stages of the growing season, with the aim of providing a means of indicating whether soil constraints in the topsoil and in the subsoil might be impacting crop growth. If a topsoil constraint is impacting growth, we might expect its effects to show through a negative correlation between the soil constraint and the early-season vegetation index. However, we would not expect to observe the impact of a subsoil constraint until later in the season (when roots have reached the constraint). To test the results from the analysis of remote sensing data, we used soil data from five fields from across Australia’s northern grains-growing region. We used these data to assess soil constraint severity and correlations between the soil constraints and enhanced vegetation index (EVI). The results of the study were inconclusive, and it was difficult to identify a dominant soil constraint with a clear relationship to crop growth. The soil data were also insufficient to draw conclusions about the depths of any dominant soil constraints. Furthermore, there was a lot of subjectivity in the interpretations of the correlations between remote sensing and soil data. The study also investigated the consistency of the spatial variation in EVI over multiple years, but the results were still inconclusive. In conclusion, this study highlights the challenges of using remote sensing data to diagnose soil constraints in agricultural settings. While remote sensing can provide useful insights into crop growth, interpreting these data and drawing meaningful conclusions about soil constraints requires further research and development. Full article
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19 pages, 4835 KiB  
Article
Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data
by Md. Suruj Mia, Ryoya Tanabe, Luthfan Nur Habibi, Naoyuki Hashimoto, Koki Homma, Masayasu Maki, Tsutomu Matsui and Takashi S. T. Tanaka
Remote Sens. 2023, 15(10), 2511; https://doi.org/10.3390/rs15102511 - 10 May 2023
Cited by 6 | Viewed by 2981
Abstract
Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been [...] Read more.
Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies. Full article
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18 pages, 5407 KiB  
Article
Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model
by Samira Luns Hatum de Almeida, Jarlyson Brunno Costa Souza, Sandra Furlan Nogueira, José Ricardo Macedo Pezzopane, Antônio Heriberto de Castro Teixeira, Cristiam Bosi, Marcos Adami, Cristiano Zerbato, Alberto Carlos de Campos Bernardi, Gustavo Bayma and Rouverson Pereira da Silva
Remote Sens. 2023, 15(3), 815; https://doi.org/10.3390/rs15030815 - 31 Jan 2023
Cited by 1 | Viewed by 1603
Abstract
The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this [...] Read more.
The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method. Full article
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21 pages, 9634 KiB  
Article
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
by Shahid Nawaz Khan, Dapeng Li and Maitiniyazi Maimaitijiang
Remote Sens. 2022, 14(12), 2843; https://doi.org/10.3390/rs14122843 - 14 Jun 2022
Cited by 18 | Viewed by 5398
Abstract
Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random [...] Read more.
Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R2 = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions. Full article
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24 pages, 5238 KiB  
Article
Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties
by Roomesh Kumar Jena, Siladitya Bandyopadhyay, Upendra Kumar Pradhan, Pravash Chandra Moharana, Nirmal Kumar, Gulshan Kumar Sharma, Partha Deb Roy, Dibakar Ghosh, Prasenjit Ray, Shelton Padua, Sundaram Ramachandran, Bachaspati Das, Surendra Kumar Singh, Sanjay Kumar Ray, Amnah Mohammed Alsuhaibani, Ahmed Gaber and Akbar Hossain
Remote Sens. 2022, 14(9), 2101; https://doi.org/10.3390/rs14092101 - 27 Apr 2022
Cited by 7 | Viewed by 2216
Abstract
Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms [...] Read more.
Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India. Full article
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20 pages, 3425 KiB  
Article
Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy
by Peng Guo, Ting Li, Han Gao, Xiuwan Chen, Yifeng Cui and Yanru Huang
Remote Sens. 2021, 13(19), 4000; https://doi.org/10.3390/rs13194000 - 06 Oct 2021
Cited by 30 | Viewed by 3634
Abstract
Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting [...] Read more.
Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture. Full article
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