Special Issue "Advances in Remote Sensing for Crop Monitoring and Yield Estimation"

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: 31 December 2021.

Special Issue Editors

Dr. Bin Chen
E-Mail Website
Guest Editor
Department of Land, Air and Water Resources, University of California, Davis, 133 Veihmeyer Hall, One Shields Ave, CA 95616-8627, USA
Interests: remote sensing; data fusion and applications; agricultural monitoring; urban studies; environmental heath
Special Issues, Collections and Topics in MDPI journals
Dr. Yufang Jin
E-Mail Website
Guest Editor
Department of Land, Air and Water Resources, University of California Davis, 133 Veihmeyer Hall, One Shields Ave, Davis, CA 95616-8627, USA
Interests: remote sensing; drivers and consequences of wildland fires; crop monitoring and precision agriculture; eco-hydrology; vegetation-climate-fire-human interaction; machine learning; UAV applications; geospatial technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global food security will remain a worldwide concern, especially in the face of challenges from climate change, population growth, water scarcity, environmental degradation, and biodiversity loss. Improving yields and maintaining agricultural sustainability through argoecological approaches and scientific farming management are of the utmost importance. The use of remote sensing in monitoring crop conditions and production estimates has proven to be very useful and supportive for agricultural management from local to regional, continental, and global scales. Many previous efforts have been made to advance the monitoring of crop conditions including the blooming and phenology cycle, health and productivity, drought and heat stress, and other processes. These remote sensing based indicators of critical crop conditions are always integrated with crop characteristics, climatic and soil variables, and auxiliary variables to build yield prediction models for cost-effective estimates of crop productions at different spatial-temporal scales.

Nowadays, the emerging satellite missions, remote sensing sensors, geospatial big data, and the development of artificial intelligence and machine learning have provided further new opportunities for a better understanding of the crop’s physical and biophysical process. This Special Issue calls for innovative data, methods, and analysis techniques for remote sensing-based crop monitoring and yield estimations. Acceptable topics include, but are not limited to, crop condition monitoring, crop phenology, crop stress detection, remote sensing indicators of crops, crop yield prediction, controls on yield potentials, drivers of yield variability, and multi-source data integration for sustainable agriculture.

You may choose our Joint Special Issue in Land.

Dr. Bin Chen
Dr. Yufang Jin
Prof. Dr. Le Yu
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 papers will be 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 2400 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

  • Remote sensing agriculture
  • Crop yield estimation and prediction
  • Crop types and cropping intensity
  • Crop phenology, stress, and health status
  • Controls and drivers of yield variability
  • Remote sensing spectral indices for crops
  • Multi-scale monitoring and mapping of crop yields
  • Multi-source data fusion for sustainable agriculture

Published Papers (16 papers)

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Article
Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton Fields in Northern Xinjiang, China
Remote Sens. 2021, 13(23), 4819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234819 - 27 Nov 2021
Viewed by 295
Abstract
Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. [...] Read more.
Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
High Resolution Distribution Dataset of Double-Season Paddy Rice in China
Remote Sens. 2021, 13(22), 4609; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224609 - 16 Nov 2021
Viewed by 353
Abstract
Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify [...] Read more.
Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Evaluating the Farmland Use Intensity and Its Patterns in a Farming—Pastoral Ecotone of Northern China
Remote Sens. 2021, 13(21), 4304; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214304 - 26 Oct 2021
Viewed by 368
Abstract
The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use [...] Read more.
The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m3/hm2, 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?
Remote Sens. 2021, 13(21), 4227; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214227 - 21 Oct 2021
Viewed by 466
Abstract
Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery [...] Read more.
Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco
Remote Sens. 2021, 13(16), 3101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163101 - 06 Aug 2021
Cited by 1 | Viewed by 967
Abstract
Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields [...] Read more.
Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season
Remote Sens. 2021, 13(15), 3001; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13153001 - 30 Jul 2021
Cited by 1 | Viewed by 641
Abstract
Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) [...] Read more.
Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest
Remote Sens. 2021, 13(15), 2988; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152988 - 29 Jul 2021
Viewed by 595
Abstract
Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence [...] Read more.
Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Quantifying Effects of Excess Water Stress at Early Soybean Growth Stages Using Unmanned Aerial Systems
Remote Sens. 2021, 13(15), 2911; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152911 - 24 Jul 2021
Viewed by 515
Abstract
Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk [...] Read more.
Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m2 and 77 g/m2 across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Oilseed Rape (Brassica napus L.) Phenology Estimation by Averaged Stokes-Related Parameters
Remote Sens. 2021, 13(14), 2652; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142652 - 06 Jul 2021
Viewed by 640
Abstract
Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to [...] Read more.
Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to vegetation structure. This study aims to explore the potential of averaged Stokes-related parameters derived from multiple PolSAR data in oilseed rape phenology identification. In this study, the averaged Stokes-related parameters were first computed by two different wave polarimetric states. Then, the two groups of averaged Stokes-related parameters were generated and applied for analyzing averaged Stokes-related parameter sensitivity to oilseed rape phenology changes. At last, decision tree (DT) algorithms trained using 60% of the data were used for oilseed rape phenological stage classification. Four Stokes parameters (g0, g1, g2 and g3) and eight sub parameters (degree of polarization m, entropy H, ellipticity angle χ, orientation angle φ, degree of linear polarization Dolp, degree of circular polarization Docp, linear polarization ratio Lpr and circular polarization ratio Cpr) were extracted from a multi-temporal RADARSAT-2 dataset acquired during the whole oilseed rape growth cycle in 2013. Their sensitivities to oilseed rape phenology were analyzed versus five main rape phenology stages. In two groups (two different wave polarimetric states) of this study, g0, g1, g2, g3, m, H, Dolp and Lpr showed high sensitivity to oilseed rape growth stages while χ, φ, Docp and Cpr showed good performance for phenology classification in previous studies, which were quite noisy during the whole oilseed rape growth circle and showed unobvious sensitivity to the crop’s phenology change. The DT algorithms performed well in oilseed rape phenological stage identification. The results were verified at the parcel level with left 40% of the point dataset. Five phenology intervals of oilseed rape were identified with no more than three parameters by simple but robust decision tree algorithm groups. The identified phenology stages agree well with the ground measurements; the overall identification accuracies were 71.18% and 79.71%, respectively. For each growth stage, the best performance occurred at stage S1 with the accuracy of 95.65% for Group 1 and 94.23% for Group 2, and the worst performance occurred at stage S3 and S5 with the values around 60%. Most of the classification errors may resulted from the indistinguishability of S3 and S5 using Stokes-related parameters. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model
Remote Sens. 2021, 13(11), 2190; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112190 - 04 Jun 2021
Cited by 2 | Viewed by 916
Abstract
The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs [...] Read more.
The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Predicting Table Beet Root Yield with Multispectral UAS Imagery
Remote Sens. 2021, 13(11), 2180; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112180 - 02 Jun 2021
Viewed by 689
Abstract
Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris [...] Read more.
Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
Remote Sens. 2021, 13(10), 2016; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13102016 - 20 May 2021
Viewed by 786
Abstract
Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and [...] Read more.
Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R2 values of the eight yield estimation models established in the two provinces were all above 0.7, Lin’s concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 °C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Article
Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018
Remote Sens. 2021, 13(9), 1735; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091735 - 29 Apr 2021
Cited by 1 | Viewed by 772
Abstract
Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed [...] Read more.
Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008–2018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPPVPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (>20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPPVPM was linearly related with grain production of winter wheat, with an R2 value of 0.68 across the CONUS. The relationships between grain production and GPPVPM in those counties without a substantial difference (<20%) between planted and harvested area were much stronger and their harvest index (HIGPP) values ranged from 0.2–0.3. GPPVPM in May could explain about 70–90% of the variance of winter wheat grain production. Our findings highlight the potential of GPPVPM in winter wheat monitoring, especially for those high harvested/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Review

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Review
Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review
Remote Sens. 2021, 13(22), 4602; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224602 - 16 Nov 2021
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Abstract
Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the [...] Read more.
Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the difference between potential yield and actual farmer yield). To this end, crop yield gap practices in African countries need to be understood to fill this gap while decreasing the environmental impacts of agricultural systems. For instance, the variability of yields has been demonstrated to be strongly controlled by soil fertilizer use, irrigation management, soil attribute, and the climate. Consequently, the quantitative assessment and mapping information of soil attributes such as nitrogen (N), phosphorus (P), potassium (K), soil organic carbon (SOC), moisture content (MC), and soil texture (i.e., clay, sand and silt contents) on the ground are essential to potentially reducing the yield gap. However, to assess, measure, and monitor these soil yield-related parameters in the field, there is a need for rapid, accurate, and inexpensive methods. Recent advances in remote sensing technologies and high computational performances offer a unique opportunity to implement cost-effective spatiotemporal methods for estimating crop yield with important levels of scalability. However, researchers and scientists in Africa are not taking advantage of the opportunity of increasingly available geospatial remote sensing technologies and data for yield studies. The objectives of this report are to (i) conduct a review of scientific literature on the current status of African yield gap analysis research and their variation in regard to soil properties management by using remote sensing techniques; (ii) review and describe optimal yield practices in Africa; and (iii) identify gaps and limitations to higher yields in African smallholder farms and propose possible improvements. Our literature reviewed 80 publications and covered a period of 22 years (1998-2020) over many selected African countries with a potential yield improvement. Our results found that (i) the number of agriculture yield-focused remote sensing studies has gradually increased, with the largest proportion of studies published during the last 15 years; (ii) most studies were conducted exclusively using multispectral Landsat and Sentinel sensors; and (iii) over the past decade, hyperspectral imagery has contributed to a better understanding of yield gap analysis compared to multispectral imagery; (iv) soil nutrients (i.e., NPK) are not the main factor influencing the studied crop productivity in Africa, whereas clay, SOC, and soil pH were the most examined soil properties in prior papers. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Review
Remote Sensing Applications in Sugarcane Cultivation: A Review
Remote Sens. 2021, 13(20), 4040; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204040 - 10 Oct 2021
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Abstract
A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive [...] Read more.
A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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Other

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Technical Note
Optimization of Topdressing for Winter Wheat by Accurate Growth Monitoring and Improved Production Estimation
Remote Sens. 2021, 13(12), 2349; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122349 - 16 Jun 2021
Viewed by 528
Abstract
Topdressing accounts for approximately 40% of the total nitrogen (N) application of winter wheat on the Huang-Huai-Hai Plain in China. However, N use efficiency of topdressing is low due to the inadaptable topdressing method used by local farmers. To improve the N use [...] Read more.
Topdressing accounts for approximately 40% of the total nitrogen (N) application of winter wheat on the Huang-Huai-Hai Plain in China. However, N use efficiency of topdressing is low due to the inadaptable topdressing method used by local farmers. To improve the N use efficiency of winter wheat, an optimization method for topdressing (THP) is proposed that uses unmanned aerial vehicle (UAV)-based remote sensing to accurately acquire the growth status and an improved model for growth potential estimation and optimization of N fertilizer amount for topdressing (NFT). The method was validated and compared with three other methods by a field experiment: the conventional local farmer’s method (TLF), a nitrogen fertilization optimization algorithm (NFOA) proposed by Raun and Lukina (TRL) and a simplification introduced by Li and Zhang (TLZ). It shows that when insufficient basal fertilizer was provided, the proposed method provided as much NFT as the TLF method, i.e., 25.05% or 11.88% more than the TRL and TLZ methods and increased the yields by 4.62% or 2.27%, respectively; and when sufficient basal fertilizer was provided, the THP method followed the TRL and TLZ methods to reduce NFT but maintained as much yield as the TLF method with a decrease of NFT by 4.20%. The results prove that THP could enhance crop production under insufficient N preceding conditions by prescribing more fertilizer and increase nitrogen use efficiency (NUE) by lowering the fertilizer amount when enough basal fertilizer is provided. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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