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Special Issue "Earth Observations for Precision Farming in China (EO4PFiC)"

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 2017

Special Issue Editors

Guest Editor
Prof. Guijun Yang

National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, 11 Middle Road, Haidian District, Beijing 100097, China
Website | E-Mail
Phone: +86 (0)10 5150 3647
Interests: remote sensing; agronomic modelling; UAV-based sensors; precision farming
Guest Editor
Prof. Dr. Zhenhong Li

School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
Website | E-Mail
Phone: +44 (0) 191 208 5704
Interests: InSAR atmospheric correction models, advanced InSAR time series techniques, high-rate GNSS, landslides (slope instability), stability monitoring of man-made infrastructure

Special Issue Information

Dear Colleagues,

Precision farming (PF) has been implemented in some form across nearly all agricultural production systems over the past 20 years. The degree of PF development varies from one place in the world to another due to the differences in technology, agronomy, economy and culture, and PF adoption has been greatest in developed agricultural countries. It has been recognised that the gap in PF adoption in China (and other developing countries) has been due to the transition of it moving from a research activity to a commercial context. There are many reasons for this, but it is in part due to a lack of capacity in the industry, a lack in the population to exploit technology, and a lack of IT infrastructure to support PF. Earth Observations (EO) from space and aircraft, combined with complementary terrestrial observations and with agronomic models, have been widely employed in PF applications in recent years. However, there is a lack of common EO operating modes for local or regional PF applications in China and a need for better integration of the different types of EO data that have recently become available or are projected to be available in the near future.

To better understand the opportunities and limitations to increasing PF adoption in China, this Special Issue invites contributions on: (i) innovative EO methods and applications on precision farming in China; (ii) optimization of EO operating modes for PF applications, especially in developing agricultural economies; and (iii) PF impact assessment. Submissions are encouraged to cover a broad range of topics which may include, but are not limited to, the following activities:

  • EO algorithm development, automation, implementation, and validation
  • Multi-sensor and multi-system EO data fusion
  • EO for monitoring crop growth and stress
  • EO for pests and diseases
  • Multi-GNSS for precise location
  • EO-assisted digital soil mapping
  • Incorporation of EO into agronomic decision support systems
  • Social-economical impact assessment

Prof. Guijun Yang
Prof. Zhenhong 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 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 monthly 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 1600 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

  • Multi-spectral imagery
  • Hyper-spectral imagery
  • SAR processing
  • SAR interferometry
  • SAR/InSAR polarimetry
  • Thermal infrared imagery
  • Global navigation satellite system (GNSS)
  • Lidar
  • UAV/GAV sensors
  • Data fusion and assimilations
  • Time series analysis
  • Soil mapping
  • Nitrogen stress
  • Water stress
  • Pest and diseases
  • Crop modelling
  • Yield mapping
  • Decision support systems
  • Social-economical impact assessment

Published Papers (15 papers)

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Open AccessArticle Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance
Remote Sens. 2017, 9(9), 951; doi:10.3390/rs9090951
Received: 13 July 2017 / Revised: 6 September 2017 / Accepted: 12 September 2017 / Published: 13 September 2017
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Abstract
Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has
[...] Read more.
Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R2, root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015). Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessEditor’s ChoiceArticle Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models
Remote Sens. 2017, 9(7), 708; doi:10.3390/rs9070708
Received: 12 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017
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Abstract
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV).
[...] Read more.
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R670 only: R2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager
Remote Sens. 2017, 9(7), 642; doi:10.3390/rs9070642
Received: 5 April 2017 / Revised: 1 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
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Abstract
Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight
[...] Read more.
Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Integrated System for Auto-Registered Hyperspectral and 3D Structure Measurement at the Point Scale
Remote Sens. 2017, 9(6), 512; doi:10.3390/rs9060512
Received: 2 March 2017 / Revised: 9 May 2017 / Accepted: 21 May 2017 / Published: 23 May 2017
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Abstract
Hyperspectral and 3D structure measurement are among the active research areas of remote sensing in recent years. The combination of these two kinds of information can provide improved outcomes distinctly, which is widely used in vegetation physiology, precision agriculture and radiative transfer modeling.
[...] Read more.
Hyperspectral and 3D structure measurement are among the active research areas of remote sensing in recent years. The combination of these two kinds of information can provide improved outcomes distinctly, which is widely used in vegetation physiology, precision agriculture and radiative transfer modeling. However, the registration and synchronization has been overlooked in data acquisition. The mismatched characteristics have limited the potential application of the hyperspectral and 3D structure data as a complete data set. This paper proposes a laboratory prototype which can integrate the hyperspectral and 3D structure measurement at the point scale. The prism dispersion and laser triangulation ranging are performed in a common optical path as a result of the coplanar design of the critical optical devices. The hyperspectral data and depth data of the same object point are acquired from the same focal plane, which makes the data auto-registered spatially and temporally. Test experiment verifies the accuracy of the data provided by the prototype and the actual measurement experiment demonstrates the feasibility of the design in vegetation observation. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models
Remote Sens. 2017, 9(5), 509; doi:10.3390/rs9050509
Received: 5 March 2017 / Revised: 15 May 2017 / Accepted: 19 May 2017 / Published: 22 May 2017
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Abstract
Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land
[...] Read more.
Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models’ specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method
Remote Sens. 2017, 9(5), 488; doi:10.3390/rs9050488
Received: 24 February 2017 / Revised: 8 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
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Abstract
Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape
[...] Read more.
Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014–2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R2 > 0.954 and RMSE <0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models
Remote Sens. 2017, 9(4), 309; doi:10.3390/rs9040309
Received: 28 December 2016 / Revised: 15 March 2017 / Accepted: 21 March 2017 / Published: 25 March 2017
Cited by 1 | PDF Full-text (2660 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI
[...] Read more.
Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period). Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images
Remote Sens. 2017, 9(3), 261; doi:10.3390/rs9030261
Received: 29 December 2016 / Revised: 3 March 2017 / Accepted: 8 March 2017 / Published: 12 March 2017
Cited by 1 | PDF Full-text (4405 KB) | HTML Full-text | XML Full-text
Abstract
Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes
[...] Read more.
Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages
Remote Sens. 2017, 9(3), 227; doi:10.3390/rs9030227
Received: 3 January 2017 / Revised: 23 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
Cited by 1 | PDF Full-text (2207 KB) | HTML Full-text | XML Full-text
Abstract
For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red
[...] Read more.
For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping
Remote Sens. 2017, 9(3), 239; doi:10.3390/rs9030239
Received: 30 December 2016 / Revised: 19 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
Cited by 4 | PDF Full-text (8033 KB) | HTML Full-text | XML Full-text
Abstract
Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop
[...] Read more.
Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements
Remote Sens. 2017, 9(3), 237; doi:10.3390/rs9030237
Received: 31 December 2016 / Revised: 24 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
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Abstract
Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is
[...] Read more.
Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is spatially variable due to the inherent variability of soil properties and position in the landscape. The objectives of this field study were: (i) to assess the spatial and temporal variability of wheat nitrogen (N) attributes related to the grain quality of winter wheat production through canopy fluorescence sensor measurements; and (ii) to examine the influence of spatial variability of soil N and moisture across different growth stages on the wheat grain quality. A geostatistical approach was used to analyze data collected from 110 georeferenced locations. In particular, Ordinary Kriging Analysis (OKA) was used to produce maps of wheat GPC, GPC yield, and wheat canopy fluorescence parameters, including simple florescence ratio and Nitrogen Balance Indices (NBI). Soil Nitrate-Nitrogen (NO3-N) content and soil Time Domain Reflectometry (TDR) value in the study field were also interpolated through the OKA method. The fluorescence parameter maps, soil NO3-N and soil TDR maps obtained from the OKA output were compared with the wheat GPC and GPC yield maps in order to assess their relationships. The results of this study indicate that the NBI spatial variability map in the late stage of wheat growth can be used to distinguish areas that produce higher GPC. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data
Remote Sens. 2017, 9(2), 168; doi:10.3390/rs9020168
Received: 10 November 2016 / Accepted: 9 February 2017 / Published: 17 February 2017
Cited by 1 | PDF Full-text (10864 KB) | HTML Full-text | XML Full-text
Abstract
Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability
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Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R2 (>0.6144) between surface scattering and SM was significantly higher (R2 < 0.4484) between dihedral scattering and SM in the regression analysis. The ISSM was better at different maize growth stages than the Bragg and X-Bragg models with a higher R2 (>0.6599) and lower absolute error (AE) (<5.82) and root mean square error (RMSE) (<3.73). The best algorithm was obtained at the sowing stage (R2 = 0.8843, AE = 3.13, RMSE = 1.76). In addition, the X-Bragg model provided better approximation of actual surface scattering without the measured data (better algorithm: R2 = 0.8314, AE = 4.39, RMSE = 2.81). Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China
Remote Sens. 2017, 9(2), 152; doi:10.3390/rs9020152
Received: 13 December 2016 / Revised: 6 February 2017 / Accepted: 9 February 2017 / Published: 13 February 2017
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Abstract
The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST
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The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST is important for its use in studying land–surface–atmosphere interactions. However, the validation of AATSR-derived LST products is scarce in China, especially in arid and semi-arid areas. In this study, we evaluated the accuracy of the AATSR LST product using ground-based measurements from 2007 to 2011 in the Heihe River Basin (HRB), China. The AATSR-derived LST results over Yingke site are closer to ground measurements than those over A’rou site for both daytime and nighttime temperatures. For nighttime, the averaged bias, STD, RMSE and R2 over both sites are 0.67 K, 3.03 K, 3.13 K and 0.93 K, respectively. Based on the accuracy assessment, we analyzed the AATSR-derived annual LST variations both in the HRB region and the two validation sites for the period of 2003 to 2011. The results at the A’rou site show an obvious increasing trend for daytime from 2003 to 2011. For the whole HRB region, the warming trend is clearly shown in the downstream of HRB. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data
Remote Sens. 2016, 8(11), 931; doi:10.3390/rs8110931
Received: 25 June 2016 / Revised: 31 October 2016 / Accepted: 3 November 2016 / Published: 9 November 2016
Cited by 2 | PDF Full-text (37155 KB) | HTML Full-text | XML Full-text | Correction
Abstract
The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For
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The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination ( R C V 2 ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessCorrection Correction: Wang, J., et al. Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data. Remote Sens. 2016, 8, 931
Remote Sens. 2017, 9(2), 94; doi:10.3390/rs9020094
Received: 15 December 2016 / Revised: 15 December 2016 / Accepted: 14 January 2017 / Published: 24 January 2017
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Abstract
The authors wish to make the following corrections to their paper [1].[...] Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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