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Special Issue "Earth Observations and Crop Models for Sustainable Agricultural Management"

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: 30 June 2019

Special Issue Editors

Guest Editor
Prof. Zhongxin Chen

Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, China
Institute of Agricultural Resources and Regional Planning, CAAS, 12 Zhongguancun Nan Dajie, Beijing 100081
Website | E-Mail
Phone: +86 (0)10 8210 5089
Interests: remote sensing applications in agriculture; data assimilation; agro-geoinformatics
Guest Editor
Prof. Jianxi Huang

Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs & College of Land Science and Technology of China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, China
Website | E-Mail
Phone: +86 (0)10 6273 7628
Interests: data assimilation; deep learning; crop yield prediction; remote sensing monitoring for agri-hazards; crop modeling
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. Shibo Fang

Institute of Eco-environment and Agro-Meteorology (IEAM), Chinese Academy of Meteorology Science, No. 46, Zhongguancun South Street, Haidian District, Beijing, China
Website | E-Mail
Phone: +86-10-68406142
Interests: soil moisture remote sensing; SAR remote sensing in Agriculture; Crop stress; crop growth observation
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: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture; GNSS

Special Issue Information

Dear Colleagues,

Modern agricultural management strongly requires intensive and extensive information from earth observation and spatially explicit models (SEMs). Thanks to the rapid development of earth observation systems and data processing technologies, the quantity and quality of the available information for agriculture have improved substantially in the past decade. On the other hand, crop models have contributed greatly to agricultural management and research. Both process-based and statistical crop models often require wide-spectrum data input, and inadequate data input will limit the performance and thus the applications of crop models. Many innovative research works have been committed to incorporating earth observations into crop models to facilitate agricultural management, but there are still gaps to be met for sustainable and profitable agricultural management.

To better understand the challenges and opportunities to integrate earth observation with crop modelling technologies, this Special Issue invites contributions on: (i) innovative EO methods to derive crop parameters; (ii) novel spatially-explicit crop models towards a better understanding of agricultural production system and ecosystems; and (iii) remote sensing data assimilation with crop models. Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following:

  • EO quantitative inversion of crop and relevant environmental parameters
  • Calibration and verification of various of EO datasets including Sentinel and Gaofen imagery
  • Multi-sensor and multi-system EO data fusion
  • EO for monitoring crop growth, health and yield
  • EO for pests and diseases
  • Spatially-explicit crop model development, implementation, and validation
  • Data assimilation algorithms, system and uncertainty
Prof. Zhongxin Chen
Dr Jianxi Huang
Prof. Guijun Yang
Prof. Shibo Fang
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 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 1800 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

  • Multispectral imagery 
  • Hyperspectral imagery 
  • SAR processing 
  • Thermal infrared imagery
  • Lidar 
  • UAV/GAV sensors 
  • Quantitative remote sensing 
  • Data fusion 
  • Data assimilations 
  • Crop modelling
  • Crop growth and health 
  • Pest and diseases 
  • Yield mapping and prediction

Published Papers (11 papers)

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Research

Open AccessArticle
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
Remote Sens. 2019, 11(11), 1353; https://doi.org/10.3390/rs11111353
Received: 22 April 2019 / Revised: 3 June 2019 / Accepted: 4 June 2019 / Published: 5 June 2019
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Abstract
Using plastic film mulch on cropland improves crop yield in water-deficient areas, but the use of plastic film on cropland leads to soil pollution. The accurate mapping of plastic-mulched land (PML) is valuable for monitoring the environmental problems caused by the use of [...] Read more.
Using plastic film mulch on cropland improves crop yield in water-deficient areas, but the use of plastic film on cropland leads to soil pollution. The accurate mapping of plastic-mulched land (PML) is valuable for monitoring the environmental problems caused by the use of plastic film. The drawback of PML mapping is that the detectable period of PML changes among the fields, which causes uncertainty when supervised classification methods are used to identify PML. In this study, a new workflow which merging PML of multiple temporal phases (MTPML) is proposed. For each temporal phase, the “possible PML” is firstly generated, these “temporal possible PML” layers are then combined to generate the “possible PML” layer. Finally, the maximum normalized difference vegetation index (NDVI) of the growing season is used to remove the non-cropland pixels from the “possible PML layer,” and then generate PML images. When generating “temporal possible PML layers,” three new PML indices (PMLI with near-infrared bands known as PMLI_NIR, PMLI with shortwave infrared bands known as PMLI_SWIR, and Normalized Difference PMLI known as PMLI_ND) are proposed to separate PML from bare land at plastic film cover stage; and the “temporal possible PML layer” are identified by the threshold based method. To estimate the performance of the three PML indices, two other approaches, PMLI threshold and Random Forest (RF) are used to generate “temporal possible PML layer.” Finally, PML images generated from the five MTPML approaches are compared with the image time series supervised classification (SUPML) result. Two study regions, Hengshui (HS) and Guyuan (GY), are used in this study. PML identification models are generated using training samples in HS and the models are used for PML mapping in both study regions. The results showed that MTPML workflow outperformed SUPML with 3%–5% higher classification accuracy. The three proposed PML indices had higher separability and importance score for bare land and PML discrimination. Among the five approaches used to generate the “temporal possible PML layer,” PMLI_SWIR is the recommended approach because the PMLI_SWIR threshold approach is easy to implement and the accuracy is only slightly lower than the RF approach. It is notable that no training sample was used in GY and the accuracy of the MTPML approach was higher than 85%, which indicated that the rules proposed in this study are suitable for other study regions. Full article
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Open AccessArticle
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model
Remote Sens. 2019, 11(9), 1119; https://doi.org/10.3390/rs11091119
Received: 15 April 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 10 May 2019
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Abstract
Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data [...] Read more.
Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha−1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m−2 for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R2 = 0.79) and prediction accuracy (RMSE = 0.17 m2 m−2). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R2 of 0.62 and RMSE of 0.74 t ha−1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha−1 for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops. Full article
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Open AccessArticle
Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery
Remote Sens. 2019, 11(9), 1109; https://doi.org/10.3390/rs11091109
Received: 9 March 2019 / Revised: 24 April 2019 / Accepted: 1 May 2019 / Published: 9 May 2019
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Abstract
Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior [...] Read more.
Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity. Full article
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Open AccessArticle
Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta
Remote Sens. 2019, 11(9), 1006; https://doi.org/10.3390/rs11091006
Received: 20 March 2019 / Revised: 14 April 2019 / Accepted: 25 April 2019 / Published: 28 April 2019
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Abstract
Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep [...] Read more.
Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy. Full article
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Open AccessArticle
Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method
Remote Sens. 2019, 11(7), 888; https://doi.org/10.3390/rs11070888
Received: 19 February 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 11 April 2019
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Abstract
The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information [...] Read more.
The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images. Full article
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Open AccessArticle
Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm
Remote Sens. 2019, 11(7), 820; https://doi.org/10.3390/rs11070820
Received: 24 February 2019 / Revised: 23 March 2019 / Accepted: 1 April 2019 / Published: 5 April 2019
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Abstract
Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize [...] Read more.
Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses. Full article
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Open AccessArticle
A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery
Remote Sens. 2019, 11(6), 619; https://doi.org/10.3390/rs11060619
Received: 30 January 2019 / Revised: 8 March 2019 / Accepted: 12 March 2019 / Published: 14 March 2019
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Abstract
When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models [...] Read more.
When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery. Full article
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Open AccessArticle
Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data
Remote Sens. 2019, 11(5), 572; https://doi.org/10.3390/rs11050572
Received: 24 January 2019 / Revised: 24 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents [...] Read more.
Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p < 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p < 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data. Full article
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Open AccessArticle
Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation
Remote Sens. 2019, 11(3), 268; https://doi.org/10.3390/rs11030268
Received: 3 January 2019 / Revised: 24 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
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Abstract
Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to [...] Read more.
Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAImean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation. Full article
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Open AccessArticle
Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation
Remote Sens. 2019, 11(2), 108; https://doi.org/10.3390/rs11020108
Received: 23 December 2018 / Accepted: 5 January 2019 / Published: 9 January 2019
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Abstract
Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and [...] Read more.
Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction. Full article
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Open AccessArticle
Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction
Remote Sens. 2018, 10(12), 2015; https://doi.org/10.3390/rs10122015
Received: 16 October 2018 / Revised: 8 December 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
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Abstract
Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases [...] Read more.
Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data. Full article
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