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Special Issue "Recent Advances in Remote Sensing for Crop Growth Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2015)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Prof. Dr. Tao Cheng

National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University, 1 Weigang, Nanjing 210095, China
Website | E-Mail
Fax: +86 25 8439 6672
Interests: quantitative remote sensing; UAV; crop mapping; precision farming; crop growth monitoring; spectral analysis; imaging spectroscopy; crop disease detection
Guest Editor
Dr. Zhengwei Yang

USDA National Agricultural Statistics Service, Research and Development Division, 3251 Old lee Highway, Room 305, Fairfax, VA 22030, USA
E-Mail
Interests: agricultural remote sensing methodology; crop condition and growth monitoring; crop disaster monitoring; land cover and land use; geospatial information analysis and application; image processing; geospatial information systems; crop acreage and yield estimation and cropland stratification and sampling
Guest Editor
Dr. Yoshio Inoue

National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, Japan
Website | E-Mail
Phone: +81-29-838-8222
Interests: plant eco-physiology; remote sensing, modeling, agro-ecosystem; precision farming; GIS
Guest Editor
Dr. Yan Zhu

National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University, 1 Weigang, Nanjing 210095, China
Website | E-Mail
Fax: +86 25 8439 6672
Interests: crop growth modeling; precision agriculture; crop growth monitoring; assimilation of remotely sensed data into crop models; remote sensing of agro-ecosystems
Co-Guest Editor
Prof. Dr. Weixing Cao

National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University, 1 Weigang, Nanjing 210095, China
Website | E-Mail
Fax: +86 25 8439 6672
Interests: crop system modeling; crop physiology; crop growth monitoring; diagnosis of plant nutrition status; precision crop management; information agriculture; agricultural remote sensing

Special Issue Information

Dear Colleagues,

Accurate and timely information of crop growth and conditions is critical for precision farming, crop management, crop yield estimation, crop disaster early warning and mitigation, agricultural production planning, crop commodity trading, and food security decision support. Recent advances in imaging and non-imaging sensor technologies, remote sensing platforms, and satellite data availability have provided new opportunities and challenges, and have resulted in many new investigations and much progress in crop growth monitoring. Ground-based millimeter-level very high spatial resolution hyperspectral imaging, which is acquired from sensors, such as ImSpectorV10E (SpecIm, Spectral Imaging Ltd., Finland) and HySpec VNIR-1600 (Norsk Elektro Optikk, Norway), enables us to discern the within-canopy and within-leaf variation in crop conditions in target fields. Affordable low-weight multispectral/hyperspectral sensors on unmanned aerial systems (UAVs) and/or regular aircrafts have significantly improved the efficiency and effectiveness in monitoring within- and between-field variations in crop growth. The recent very-high-resolution satellite imagery, acquired typically in sub-meter to 5 meter resolution, such as WorldView-2, Pleiades-1, IKONOS, and RapidEye, has brought us into a new phase of remote sensing for precision crop management over large farming areas. The freely available satellite data from sensors, such as MODIS, NPP VIIRS, and Landsat, have greatly facilitated large scale (i.e., regional or even global level) crop growth monitoring. This Special Issue calls for submissions that report the latest research, developments, and applications in instrumentation, technology, theory, and systems for remote sensing based crop growth monitoring. Interested authors are invited to contribute to this Special Issue of the Remote Sensing open-access journal (Impact Factor 2012: 2.1) by submitting an original paper. Papers are solicited in, but not limited to, the following topics:

  • New platforms and sensors for crop monitoring
  • Crop imaging using unmanned aerial systems (UAS’s)
  • Ground-based hyperspectral crop sensing
  • Advanced use of field sensors and sensing network
  • Crop monitoring using very-high-resolution satellite imagery
  • Global/regional agricultural monitoring systems for operational purposes
  • Crop monitoring using Landsat-8 and preceding Landsat imagery
  • Real-time diagnosis of crop water and nutrition status
  • Time series analysis of crop growth cycle
  • Detection of pre-visual stress in crops
  • Estimation of crop condition variables (biophysical, biochemical, and physiological)
  • Crop cover classification and cropland mapping
  • Crop yield estimation and forecasting

Those interested in submission must follow the strict guidelines of the Remote Sensing open-access journal (http://www.mdpi.com/journal/remotesensing/instructions) as well as the instructions provided in: https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf

All papers will go through normal peer-review process. Only papers of very high quality will be published.

Dr. Tao Cheng
Dr. Yan Zhu
Dr. Zhengwei Yang
Dr. Yoshio Inou
Prof. Dr. Weixing Cao
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.


Published Papers (17 papers)

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Editorial

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Open AccessEditorial Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring
Remote Sens. 2016, 8(2), 116; doi:10.3390/rs8020116
Received: 1 February 2016 / Accepted: 2 February 2016 / Published: 4 February 2016
PDF Full-text (148 KB) | HTML Full-text | XML Full-text
Abstract
This Special Issue gathers sixteen papers focusing on applying various remote sensing techniques to crop growth monitoring. The studies span observations from multiple scales, a combination of model simulations and experimental measurements, and a range of topics on crop monitoring and mapping. This
[...] Read more.
This Special Issue gathers sixteen papers focusing on applying various remote sensing techniques to crop growth monitoring. The studies span observations from multiple scales, a combination of model simulations and experimental measurements, and a range of topics on crop monitoring and mapping. This preface provides a brief overview of the contributed papers. Full article

Research

Jump to: Editorial

Open AccessArticle Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration
Remote Sens. 2015, 7(11), 14939-14966; doi:10.3390/rs71114939
Received: 30 April 2015 / Revised: 14 October 2015 / Accepted: 27 October 2015 / Published: 10 November 2015
Cited by 6 | PDF Full-text (1275 KB) | HTML Full-text | XML Full-text
Abstract
The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain
[...] Read more.
The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation accuracies using the six metrics that were the coefficients of determination for the calibration (R2C) and validation (R2V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R²C, R²V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R²C, R²V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings of empirical hyperspectral models for mapping important vegetation biophysical properties of other crops. Full article
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Open AccessArticle Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
Remote Sens. 2015, 7(10), 14179-14199; doi:10.3390/rs71014179
Received: 31 March 2015 / Revised: 20 October 2015 / Accepted: 22 October 2015 / Published: 27 October 2015
Cited by 3 | PDF Full-text (1190 KB) | HTML Full-text | XML Full-text
Abstract
In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the
[...] Read more.
In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R2) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10−6 W·m2·sr1·nm1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m2·sr1·nm1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated. Full article
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Open AccessArticle The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements
Remote Sens. 2015, 7(10), 14079-14098; doi:10.3390/rs71014079
Received: 18 August 2015 / Revised: 5 October 2015 / Accepted: 20 October 2015 / Published: 26 October 2015
Cited by 4 | PDF Full-text (2034 KB) | HTML Full-text | XML Full-text
Abstract
A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions
[...] Read more.
A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions on the consistency of vegetation indices (normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI)) derived from ground-based spectral measurements in three different types of cropland (paddy field, upland field, cultivated grassland) in Japan. In general, the vegetation indices decreased with decreasing solar zenith angle. This response was affected significantly by the growth stage and diffuse/direct light conditions. The decreasing response of the NDVI to the decreasing solar zenith angle was high during the middle growth stage (0.4 < NDVI < 0.8). On the other hand, a similar response of the GRVI was evident except in the early growth stage (GRVI < 0). The response of vegetation indices to the solar zenith angle was evident under clear sky conditions but almost negligible under cloudy sky conditions. At large solar zenith angles, neither the NDVI nor the GRVI were affected by diffuse/direct light conditions in any growth stage. These experimental results were supported well by the results of simulations based on a physically-based canopy reflectance model (PROSAIL). Systematic selection of the data from continuous diurnal spectral measurements in consideration of the solar light conditions would be effective for accurate and consistent assessment of the canopy structure and functioning. Full article
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Open AccessArticle In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
Remote Sens. 2015, 7(10), 12859-12886; doi:10.3390/rs71012859
Received: 28 May 2015 / Revised: 22 September 2015 / Accepted: 24 September 2015 / Published: 30 September 2015
Cited by 7 | PDF Full-text (4028 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree
[...] Read more.
The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features. Full article
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Open AccessCommunication Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice
Remote Sens. 2015, 7(9), 11326-11343; doi:10.3390/rs70911326
Received: 30 June 2015 / Revised: 24 August 2015 / Accepted: 27 August 2015 / Published: 7 September 2015
Cited by 2 | PDF Full-text (2621 KB) | HTML Full-text | XML Full-text
Abstract
The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were
[...] Read more.
The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology. Full article
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Open AccessArticle Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China
Remote Sens. 2015, 7(8), 10646-10667; doi:10.3390/rs70810646
Received: 21 April 2015 / Revised: 11 August 2015 / Accepted: 13 August 2015 / Published: 18 August 2015
Cited by 9 | PDF Full-text (848 KB) | HTML Full-text | XML Full-text
Abstract
Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM)
[...] Read more.
Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers’ fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments. Full article
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Open AccessArticle Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards
Remote Sens. 2015, 7(8), 9886-9903; doi:10.3390/rs70809886
Received: 23 March 2015 / Revised: 24 July 2015 / Accepted: 28 July 2015 / Published: 4 August 2015
Cited by 2 | PDF Full-text (753 KB) | HTML Full-text | XML Full-text
Abstract
Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both
[...] Read more.
Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process. Full article
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Open AccessArticle Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images
Remote Sens. 2015, 7(7), 8883-8905; doi:10.3390/rs70708883
Received: 10 January 2015 / Revised: 27 June 2015 / Accepted: 6 July 2015 / Published: 14 July 2015
Cited by 2 | PDF Full-text (17110 KB) | HTML Full-text | XML Full-text
Abstract
A large-area map of the spatial distribution of rice is important for grain yield estimations, water management and an understanding of the biogeochemical cycling of carbon and nitrogen. In this paper, we developed the Normalized Weighted Difference Water Index (NWDWI) for identifying the
[...] Read more.
A large-area map of the spatial distribution of rice is important for grain yield estimations, water management and an understanding of the biogeochemical cycling of carbon and nitrogen. In this paper, we developed the Normalized Weighted Difference Water Index (NWDWI) for identifying the unique characteristics of rice during the flooding and transplanting period. With the aid of the ASTER Global Digital Elevation Model and the phenological data observed at agrometeorological stations, the spatial distributions of single cropping rice and double cropping early and late rice in the Yangtze River Delta region were generated using the NWDWI and time-series Enhanced Vegetation Index data derived from MODIS/Terra data during the 2000–2010 period. The accuracy of the MODIS-derived rice planting area was validated against agricultural census data at the county level. The spatial accuracy was also tested based on a land use map and Landsat ETM+ data. The decision coefficients for county-level early and late rice were 0.560 and 0.619, respectively. The MODIS-derived area of late rice exhibited higher consistency with the census data during the 2000–2010 period. The algorithm could detect and monitor rice fields with different cropping patterns at the same site and is useful for generating spatial datasets of rice on a regional scale. Full article
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Open AccessArticle Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data
Remote Sens. 2015, 7(7), 8728-8756; doi:10.3390/rs70708728
Received: 10 February 2015 / Revised: 25 June 2015 / Accepted: 6 July 2015 / Published: 10 July 2015
Cited by 11 | PDF Full-text (25032 KB) | HTML Full-text | XML Full-text
Abstract
Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear
[...] Read more.
Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013. Full article
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Open AccessArticle Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan
Remote Sens. 2015, 7(6), 6535-6557; doi:10.3390/rs70606535
Received: 17 March 2015 / Revised: 12 May 2015 / Accepted: 13 May 2015 / Published: 26 May 2015
Cited by 6 | PDF Full-text (2958 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to
[...] Read more.
Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to support mitigation and disaster response strategies in the region. This study describes and demonstrates a method for rapid, pre-event crop status assessment. The ex-post test case is Typhoon Haiyan and its impact on the rice crop in Leyte Province in the Philippines. A synthetic aperture radar (SAR) derived rice area map was used to delineate the area at risk while crop status at the moment of typhoon landfall was estimated from specific time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. A spatially explicit indicator of risk of standing crop loss was calculated as the time between estimated heading date and typhoon occurrence. Results of the analysis of pre- and post-event SAR images showed that 6500 ha were flooded in northeastern Leyte. This area was also the region most at risk to storm related crop damage due to late establishment of rice. Estimates highlight that about 700 ha of rice (71% of which was in northeastern Leyte) had not reached maturity at the time of the typhoon event and a further 8400 ha (84% of which was in northeastern Leyte) were likely to be not yet harvested. We demonstrated that the proposed approach can provide pre-event, in-season information on the status of rice and other field crops and the risk of damage posed by tropical storms. Full article
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Open AccessArticle Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA
Remote Sens. 2015, 7(5), 5347-5369; doi:10.3390/rs70505347
Received: 29 January 2015 / Revised: 17 April 2015 / Accepted: 22 April 2015 / Published: 28 April 2015
Cited by 15 | PDF Full-text (16845 KB) | HTML Full-text | XML Full-text
Abstract
Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop
[...] Read more.
Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries–Matusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas. Full article
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Open AccessArticle Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan
Remote Sens. 2015, 7(5), 5329-5346; doi:10.3390/rs70505329
Received: 1 December 2014 / Revised: 18 April 2015 / Accepted: 22 April 2015 / Published: 27 April 2015
Cited by 6 | PDF Full-text (10101 KB) | HTML Full-text | XML Full-text
Abstract
Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on
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Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760–R739 = R760 – R739). Among published and newly developed SIs, DSIR760–R739 exhibited a significant and strong linear relationship with LAI and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760–R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760–R739 is a simple but accurate method for timely and nondestructive monitoring of LAI. Full article
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Open AccessArticle Exploring the Vertical Distribution of Structural Parameters and Light Radiation in Rice Canopies by the Coupling Model and Remote Sensing
Remote Sens. 2015, 7(5), 5203-5221; doi:10.3390/rs70505203
Received: 1 March 2015 / Revised: 16 April 2015 / Accepted: 20 April 2015 / Published: 24 April 2015
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Abstract
Canopy structural parameters and light radiation are important for evaluating the light use efficiency and grain yield of crops. Their spatial variation within canopies and temporal variation over growth stages could be simulated using dynamic models with strong application and predictability. Based on
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Canopy structural parameters and light radiation are important for evaluating the light use efficiency and grain yield of crops. Their spatial variation within canopies and temporal variation over growth stages could be simulated using dynamic models with strong application and predictability. Based on an optimized canopy structure vertical distribution model and the Beer-Lambert law combined with hyperspectral remote sensing (RS) technology, we established a new dynamic model for simulating leaf area index (LAI), leaf angle (LA) distribution and light radiation at different vertical heights and growth stages. The model was validated by measuring LAI, LA and light radiation in different leaf layers at different growth stages of two different types of rice (Oryza sativa L.), i.e., japonica (Wuxiangjing14) and indica (Shanyou63). The results show that the simulated values were in good agreement with the observed values, with an average RRMSE (relative root mean squared error) between simulated and observed LAI and LA values of 14.75% and 21.78%, respectively. The RRMSE values for simulated photosynthetic active radiation (PAR) transmittance and interception rates were 14.25% and 9.22% for Wuxiangjing14 and 15.71% and 4.40% for Shanyou63, respectively. In addition, the corresponding RRMSE values for red (R), green (G) and blue (B) radiation transmittance and interception rates were 16.34%, 15.96% and 15.36% for Wuxiangjing14 and 5.75%, 8.23% and 5.03% for Shanyou63, respectively. The results indicate that the model performed well for different rice cultivars and under different cultivation conditions. Full article
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Open AccessArticle A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
Remote Sens. 2015, 7(4), 3633-3650; doi:10.3390/rs70403633
Received: 14 January 2015 / Revised: 13 March 2015 / Accepted: 23 March 2015 / Published: 26 March 2015
Cited by 10 | PDF Full-text (18784 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information
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Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece. Full article
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Open AccessArticle Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images
Remote Sens. 2015, 7(4), 3467-3488; doi:10.3390/rs70403467
Received: 30 December 2014 / Accepted: 17 March 2015 / Published: 24 March 2015
Cited by 11 | PDF Full-text (25824 KB) | HTML Full-text | XML Full-text
Abstract
Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate
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Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition. Full article
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Open AccessArticle MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis
Remote Sens. 2015, 7(1), 512-529; doi:10.3390/rs70100512
Received: 17 September 2014 / Accepted: 23 December 2014 / Published: 6 January 2015
Cited by 4 | PDF Full-text (30844 KB) | HTML Full-text | XML Full-text
Abstract
Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently
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Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest. Full article
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