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Sensors and Smart Sensing of Agricultural Land Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 September 2017) | Viewed by 84592

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Dr. Huajun Tang

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

Prof. Dr. Wenbin Wu

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Guest Editor

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

Prof. Dr. Yun Shi

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Guest Editor

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: sensor integration; unmanned aerial vehicle; data fusion

Special Issue Information

Dear Colleagues,

Agricultural land systems are essential to human beings and provide the major biophysical basis for sustaining food production. Over the past few decades, the unprecedented growth of both the economy and the population has led to diverse agricultural land use practices, globally varying from swidden cultivation to multi-cropping, from crop/variety choices to intensified management. As a result, agricultural land systems have experienced rapid changes or modifications in their land uses, functions and services, which greatly impact their abilities of either providing more land resources for food production, using existing land more efficiently, or maximizing output of per unit of land.

Remote sensing technologies provide an innovative means for mapping, monitoring and modeling agricultural land systems. Recent development of new satellite and aerial-borne sensors, vehicle or tractor-based near-sensing devices, and in situ wireless sensor networks provides a wealth of data for supporting smart sensing and proper management of agricultural land systems. However, to integrate and optimize these multi-platforms, multi-sensors and multi-scales data, new concepts, processing algorithms and application systems are necessarily needed, which requires many innovations in the theory and practice of agricultural remote sensing.

This Special Issue, edited by the 3^rd Global Land Project Open Science Meeting held in Beijing 24^–27 October, 2016, is dedicated to communicate latest progresses in remote sensors and its application in agricultural land systems, to look at some key theoretical and technical issues in this field, and to offer some case studies worldwide demonstrating the experience, utility, and models for agricultural land systems monitoring at different scales. Original contributions looking at integration of multi-sensors, possibly spanning long time periods, and/or large geographical extents are especially encouraged.

Prof. Dr. Huajun Tang
Prof. Dr. Wenbin Wu
Prof. Dr. Yun Shi
Guest Editors

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Keywords

Novel sensor design and platforms development
Unmanned Aerial Vehicles
Hyperspectral sensors
Imaging spectroscopy
Laser scanning sensors
Integration and fusion of multiple sensors
Image processing algorithm and systems
Information extraction and data mining
Time series analysis
Cropland and crop distribution mapping, and change detection
Crop inventory survey
Crop growth monitoring and yield estimation
Cropland evapotranspiration, soil moisture, and drought monitoring and assessment
Agricultural land intensification
Agricultural disaster monitoring and loss assessment
Seed/crop disease monitoring and assessment
Precision agriculture and smart agriculture
Data assimilation, validation, and ground truths

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Published Papers (13 papers)

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Research

9096 KiB

Open AccessArticle

Spatial-Temporal Dynamics of Cropping Frequency in Hubei Province over 2001–2015

by Jianbin Tao, Wenbin Wu and Wenbin Liu

Sensors 2017, 17(11), 2622; https://doi.org/10.3390/s17112622 - 14 Nov 2017

Cited by 10 | Viewed by 3843

Abstract

Mapping crop patterns with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. In this paper, a hierarchical clustering method was proposed to map cropping frequency from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Indices (EVI) data and the spatial and temporal patterns of cropping frequency from 2001 to 2015 in Hubei Province of China were analyzed. The results are as follows: (1) The total double crop areas decreased slightly, while total single crop areas decreased significantly during 2001 and 2015; (2) The transfer between double crop and single crop was frequent in Hubei with about 11~15% croplands changed their cropping frequency every 5 years; (3) The crop system has obvious regional differentiation for their change trend at the county level. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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6283 KiB

Open AccessArticle

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters

by Safa Bousbih, Mehrez Zribi, Zohra Lili-Chabaane, Nicolas Baghdadi, Mohammad El Hajj, Qi Gao and Bernard Mougenot

Sensors 2017, 17(11), 2617; https://doi.org/10.3390/s17112617 - 14 Nov 2017

Cited by 128 | Viewed by 6995

Abstract

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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11692 KiB

Open AccessArticle

Village Building Identification Based on Ensemble Convolutional Neural Networks

by Zhiling Guo, Qi Chen, Guangming Wu, Yongwei Xu, Ryosuke Shibasaki and Xiaowei Shao

Sensors 2017, 17(11), 2487; https://doi.org/10.3390/s17112487 - 30 Oct 2017

Cited by 58 | Viewed by 6229

Abstract

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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8581 KiB

Open AccessArticle

Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index

by Cong Wang, Jing Li, Qinhuo Liu, Bo Zhong, Shanlong Wu and Chuanfu Xia

Sensors 2017, 17(9), 1982; https://doi.org/10.3390/s17091982 - 30 Aug 2017

Cited by 51 | Viewed by 6451

Abstract

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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5396 KiB

Open AccessArticle

A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics

by Miao Lu, Wenbin Wu, Liangzhi You, Di Chen, Li Zhang, Peng Yang and Huajun Tang

Sensors 2017, 17(7), 1613; https://doi.org/10.3390/s17071613 - 12 Jul 2017

Cited by 31 | Viewed by 5801

Abstract

Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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4320 KiB

Open AccessArticle

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

by Jialin Zhang, Xiuhong Li, Rongjin Yang, Qiang Liu, Long Zhao and Baocheng Dou

Sensors 2017, 17(6), 1390; https://doi.org/10.3390/s17061390 - 15 Jun 2017

Cited by 34 | Viewed by 6319

Abstract

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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9719 KiB

Open AccessArticle

Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images

by Chong Fan, Xushuai Chen, Lei Zhong, Min Zhou, Yun Shi and Yulin Duan

Sensors 2017, 17(3), 623; https://doi.org/10.3390/s17030623 - 18 Mar 2017

Cited by 15 | Viewed by 6137

Abstract

A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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6894 KiB

Open AccessArticle

Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data

by Kai Zhou, Xinqiang Deng, Xia Yao, Yongchao Tian, Weixing Cao, Yan Zhu, Susan L. Ustin and Tao Cheng

Sensors 2017, 17(3), 578; https://doi.org/10.3390/s17030578 - 13 Mar 2017

Cited by 29 | Viewed by 6452

Abstract

Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CI_Red-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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11665 KiB

Open AccessArticle

eFarm: A Tool for Better Observing Agricultural Land Systems

by Qiangyi Yu, Yun Shi, Huajun Tang, Peng Yang, Ankun Xie, Bin Liu and Wenbin Wu

Sensors 2017, 17(3), 453; https://doi.org/10.3390/s17030453 - 24 Feb 2017

Cited by 33 | Viewed by 7826

Abstract

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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11729 KiB

Open AccessArticle

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

by Chong Fan, Chaoyun Wu, Grand Li and Jun Ma

Sensors 2017, 17(2), 362; https://doi.org/10.3390/s17020362 - 13 Feb 2017

Cited by 36 | Viewed by 6859

Abstract

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the highresolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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6983 KiB

Open AccessArticle

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors

by Aixia Yang, Bo Zhong, Shanlong Wu and Qinhuo Liu

Sensors 2017, 17(1), 204; https://doi.org/10.3390/s17010204 - 22 Jan 2017

Cited by 12 | Viewed by 5052

Abstract

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors’ radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors’ application, and as such will promote the development of Chinese satellite data. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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5556 KiB

Open AccessArticle

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar

by Dongliang Wang, Xiaoping Xin, Quanqin Shao, Matthew Brolly, Zhiliang Zhu and Jin Chen

Sensors 2017, 17(1), 180; https://doi.org/10.3390/s17010180 - 19 Jan 2017

Cited by 83 | Viewed by 8587

Abstract

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R² = 0.340, root-mean-square error (RMSE) = 81.89 g·m⁻², and relative error of 14.1%). The improvement of multiple regressions to the R² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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12877 KiB

Open AccessArticle

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

by Mrinal Singha, Bingfang Wu and Miao Zhang

Sensors 2017, 17(1), 10; https://doi.org/10.3390/s17010010 - 22 Dec 2016

Cited by 30 | Viewed by 6979

Abstract

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification. Full article

(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)

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