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Special Issue "Remote Sensing in Precision Agriculture"

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

Deadline for manuscript submissions: closed (31 July 2016).

Special Issue Editor

Guest Editor
Dr. Mutlu Ozdogan

Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI, 53726 USA
Website | E-Mail
Fax: +1 608 265 4113
Interests: agriculture; image analysis; spatial analysis; geography; food security; irrigation; hydrology

Special Issue Information

Dear Colleagues,

Precision agriculture (PA)–defined as a set of technologies that combines acquisition, analysis, management, and delivery of information to help make site-specific decisions, with the ultimate goal of optimizing production–will play an important role in addressing this grand challenge. At the heart of the evolving tools, technologies, and information management strategies found in precision agriculture is remote sensing. However, the technology of capturing, analyzing, storing, and delivering the remotely sensed observations associated with precision agriculture is changing rapidly, thus making it difficult to keep up with the ever-expanding volume of scientific research. It is time to take stock of the current state-of-the-art in the remote sensing associated with precision agriculture.

A total of 25 papers are published, e.g.,

Tilly, N.; et al. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449–11480.
Mesas-Carrascosa, F.-J.; et al. Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management. Remote Sens. 2015, 7, 12793-12814.
Gonzalez-Dugo, V.; et al. Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sens. 2015, 7, 13586-13605.

Dr. Mutlu Ozdogan
Guest Editor

Published Papers (25 papers)

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Open AccessArticle
Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations
Remote Sens. 2016, 8(11), 927; https://doi.org/10.3390/rs8110927
Received: 13 July 2016 / Revised: 30 October 2016 / Accepted: 3 November 2016 / Published: 8 November 2016
Cited by 8 | PDF Full-text (4747 KB) | HTML Full-text | XML Full-text
Abstract
The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory [...] Read more.
The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R2 = 0.62, RMSE = 5.46) and clay (R2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
Remote Sens. 2016, 8(10), 848; https://doi.org/10.3390/rs8100848
Received: 24 June 2016 / Revised: 5 October 2016 / Accepted: 8 October 2016 / Published: 16 October 2016
Cited by 9 | PDF Full-text (3851 KB) | HTML Full-text | XML Full-text
Abstract
A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery [...] Read more.
A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging
Remote Sens. 2016, 8(10), 822; https://doi.org/10.3390/rs8100822
Received: 1 July 2016 / Revised: 15 September 2016 / Accepted: 28 September 2016 / Published: 7 October 2016
Cited by 15 | PDF Full-text (3165 KB) | HTML Full-text | XML Full-text
Abstract
Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower [...] Read more.
Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery
Remote Sens. 2016, 8(9), 706; https://doi.org/10.3390/rs8090706
Received: 6 May 2016 / Revised: 17 August 2016 / Accepted: 24 August 2016 / Published: 27 August 2016
Cited by 40 | PDF Full-text (7196 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned [...] Read more.
Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)
Remote Sens. 2016, 8(8), 638; https://doi.org/10.3390/rs8080638
Received: 23 February 2016 / Revised: 28 July 2016 / Accepted: 1 August 2016 / Published: 8 August 2016
Cited by 32 | PDF Full-text (2727 KB) | HTML Full-text | XML Full-text
Abstract
A field experiment was carried out to implement a remote sensing energy balance (RSEB) algorithm for estimating the incoming solar radiation (Rsi), net radiation (Rn), sensible heat flux (H), soil heat flux (G) and latent heat flux (LE) over a drip-irrigated olive (cv. [...] Read more.
A field experiment was carried out to implement a remote sensing energy balance (RSEB) algorithm for estimating the incoming solar radiation (Rsi), net radiation (Rn), sensible heat flux (H), soil heat flux (G) and latent heat flux (LE) over a drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley, Maule Region, Chile (35°25′S; 71°44′W; 90 m above sea level). For this study, a helicopter-based unmanned aerial vehicle (UAV) was equipped with multispectral and infrared thermal cameras to obtain simultaneously the normalized difference vegetation index (NDVI) and surface temperature (Tsurface) at very high resolution (6 cm × 6 cm). Meteorological variables and surface energy balance components were measured at the time of the UAV overpass (near solar noon). The performance of the RSEB algorithm was evaluated using measurements of H and LE obtained from an eddy correlation system. In addition, estimated values of Rsi and Rn were compared with ground-truth measurements from a four-way net radiometer while those of G were compared with soil heat flux based on flux plates. Results indicated that RSEB algorithm estimated LE and H with errors of 7% and 5%, respectively. Values of the root mean squared error (RMSE) and mean absolute error (MAE) for LE were 50 and 43 W m−2 while those for H were 56 and 46 W m−2, respectively. Finally, the RSEB algorithm computed Rsi, Rn and G with error less than 5% and with values of RMSE and MAE less than 38 W m−2. Results demonstrated that multispectral and thermal cameras placed on an UAV could provide an excellent tool to evaluate the intra-orchard spatial variability of Rn, G, H, LE, NDVI and Tsurface over the tree canopy and soil surface between rows. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index
Remote Sens. 2016, 8(7), 605; https://doi.org/10.3390/rs8070605
Received: 1 April 2016 / Revised: 2 July 2016 / Accepted: 11 July 2016 / Published: 19 July 2016
Cited by 18 | PDF Full-text (4219 KB) | HTML Full-text | XML Full-text
Abstract
The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this [...] Read more.
The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg·ha−1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5–V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%–90%, 87%–89% and 83%–84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m2·m−2, AGB > 3 t·ha−1 or PNU > 80 kg·ha−1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5–V6 and V7–V8 than the V9–V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI–NDVI) and ratio vegetation index (R2 = 0.56–0.68) performed consistently better than the original VIs (R2 = 0.33–0.55) for estimating NNI. The N status diagnosis accuracy rate using RI–NDVI was 81% and 71% at V7–V8 and V9–V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Quantifying Fertilizer Application Response Variability with VHR Satellite NDVI Time Series in a Rainfed Smallholder Cropping System of Mali
Remote Sens. 2016, 8(6), 531; https://doi.org/10.3390/rs8060531
Received: 30 March 2016 / Revised: 2 June 2016 / Accepted: 16 June 2016 / Published: 22 June 2016
Cited by 6 | PDF Full-text (7163 KB) | HTML Full-text | XML Full-text
Abstract
Soil fertility in smallholder farming areas is known to vary strongly on multiple scales. This study measures the sensitivity of the recorded satellite signal to on-farm soil fertility treatments applied to five crop types, and quantifies this fertilization effect with respect to within-field [...] Read more.
Soil fertility in smallholder farming areas is known to vary strongly on multiple scales. This study measures the sensitivity of the recorded satellite signal to on-farm soil fertility treatments applied to five crop types, and quantifies this fertilization effect with respect to within-field variation, between-field variation and field position in the catena. Plant growth was assessed in 5–6 plots per field in 48 fields located in the Sudano-Sahelian agro-ecological zone of southeastern Mali. A unique series of Very High Resolution (VHR) satellite and Unmanned Aerial Vehicle (UAV) images were used to calculate the Normalized Difference Vegetation Index (NDVI). In this experiment, for half of the fields at least 50% of the NDVI variance within a field was due to fertilization. Moreover, the sensitivity of NDVI to fertilizer application was crop-dependent and varied through the season, with optima at the end of August for peanut and cotton and early October for sorghum and maize. The influence of fertilizer on NDVI was comparatively small at the landscape scale (up to 35% of total variation), relative to the influence of other components of variation such as field management and catena position. The NDVI response could only partially be benchmarked against a fertilization reference within the field. We conclude that comparisons of the spatial and temporal responses of NDVI, with respect to fertilization and crop management, requires a stratification of soil catena-related crop growth conditions at the landscape scale. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms
Remote Sens. 2016, 8(4), 303; https://doi.org/10.3390/rs8040303
Received: 31 December 2015 / Revised: 21 February 2016 / Accepted: 22 March 2016 / Published: 4 April 2016
Cited by 26 | PDF Full-text (8129 KB) | HTML Full-text | XML Full-text
Abstract
Field crop yield prediction is crucial to grain storage, agricultural field management, and national agricultural decision-making. Currently, crop models are widely used for crop yield prediction. However, they are hampered by the uncertainty or similarity of input parameters when extrapolated to field scale. [...] Read more.
Field crop yield prediction is crucial to grain storage, agricultural field management, and national agricultural decision-making. Currently, crop models are widely used for crop yield prediction. However, they are hampered by the uncertainty or similarity of input parameters when extrapolated to field scale. Data assimilation methods that combine crop models and remote sensing are the most effective methods for field yield estimation. In this study, the World Food Studies (WOFOST) model is used to simulate the growing process of spring maize. Common assimilation methods face some difficulties due to the scarce, constant, or similar nature of the input parameters. For example, yield spatial heterogeneity simulation, coexistence of common assimilation methods and the nutrient module, and time cost are relatively important limiting factors. To address the yield simulation problems at field scale, a simple yet effective method with fast algorithms is presented for assimilating the time-series HJ-1 A/B data into the WOFOST model in order to improve the spring maize yield simulation. First, the WOFOST model is calibrated and validated to obtain the precise mean yield. Second, the time-series leaf area index (LAI) is calculated from the HJ data using an empirical regression model. Third, some fast algorithms are developed to complete assimilation. Finally, several experiments are conducted in a large farmland (Hongxing) to evaluate the yield simulation results. In general, the results indicate that the proposed method reliably improves spring maize yield estimation in terms of spatial heterogeneity simulation ability and prediction accuracy without affecting the simulation efficiency. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification
Remote Sens. 2016, 8(3), 257; https://doi.org/10.3390/rs8030257
Received: 21 January 2016 / Revised: 7 March 2016 / Accepted: 11 March 2016 / Published: 18 March 2016
Cited by 20 | PDF Full-text (5330 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related [...] Read more.
Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based) to three-band imagery with RGB (red, green, and blue bands) and four-band imagery with RGB and near-infrared (NIR) bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Estimating Evapotranspiration of an Apple Orchard Using a Remote Sensing-Based Soil Water Balance
Remote Sens. 2016, 8(3), 253; https://doi.org/10.3390/rs8030253
Received: 30 November 2015 / Revised: 7 March 2016 / Accepted: 11 March 2016 / Published: 17 March 2016
Cited by 13 | PDF Full-text (5959 KB) | HTML Full-text | XML Full-text
Abstract
The main goal of this research was to estimate the actual evapotranspiration (ETc) of a drip-irrigated apple orchard located in the semi-arid region of Talca Valley (Chile) using a remote sensing-based soil water balance model. The methodology to estimate ETc [...] Read more.
The main goal of this research was to estimate the actual evapotranspiration (ETc) of a drip-irrigated apple orchard located in the semi-arid region of Talca Valley (Chile) using a remote sensing-based soil water balance model. The methodology to estimate ETc is a modified version of the Food and Agriculture Organization of the United Nations (FAO) dual crop coefficient approach, in which the basal crop coefficient (Kcb) was derived from the soil adjusted vegetation index (SAVI) calculated from satellite images and incorporated into a daily soil water balance in the root zone. A linear relationship between the Kcb and SAVI was developed for the apple orchard Kcb = 1.82·SAVI − 0.07 (R2 = 0.95). The methodology was applied during two growing seasons (2010–2011 and 2012–2013), and ETc was evaluated using latent heat fluxes (LE) from an eddy covariance system. The results indicate that the remote sensing-based soil water balance estimated ETc reasonably well over two growing seasons. The root mean square error (RMSE) between the measured and simulated ETc values during 2010–2011 and 2012–2013 were, respectively, 0.78 and 0.74 mm·day−1, which mean a relative error of 25%. The index of agreement (d) values were, respectively, 0.73 and 0.90. In addition, the weekly ETc showed better agreement. The proposed methodology could be considered as a useful tool for scheduling irrigation and driving the estimation of water requirements over large areas for apple orchards. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data
Remote Sens. 2016, 8(3), 235; https://doi.org/10.3390/rs8030235
Received: 10 December 2015 / Revised: 23 February 2016 / Accepted: 29 February 2016 / Published: 12 March 2016
Cited by 10 | PDF Full-text (5683 KB) | HTML Full-text | XML Full-text
Abstract
In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards [...] Read more.
In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Simulating an Autonomously Operating Low-Cost Static Terrestrial LiDAR for Multitemporal Maize Crop Height Measurements
Remote Sens. 2016, 8(3), 205; https://doi.org/10.3390/rs8030205
Received: 30 November 2015 / Revised: 5 February 2016 / Accepted: 25 February 2016 / Published: 2 March 2016
Cited by 8 | PDF Full-text (4676 KB) | HTML Full-text | XML Full-text
Abstract
In order to optimize agricultural processes, near real-time spatial information about in-field variations, such as crop height development (i.e., changes over time), is indispensable. This development can be captured with a LiDAR system. However, its applicability in precision agriculture is often [...] Read more.
In order to optimize agricultural processes, near real-time spatial information about in-field variations, such as crop height development (i.e., changes over time), is indispensable. This development can be captured with a LiDAR system. However, its applicability in precision agriculture is often hindered due to high costs and unstandardized processing methods. This study investigates the potential of an autonomously operating low-cost static terrestrial laser scanner (TLS) for multitemporal height monitoring of maize crops. A low-cost system is simulated by artificially reducing the point density of data captured during eight different campaigns. The data were used to derive and assess crop height models (CHM). Results show that heights calculated with CHM based on the unreduced point cloud are accurate when compared to manually measured heights (mean deviation = 0.02 m, standard deviation = 0.15 m, root mean square error (RMSE) = 0.16 m). When reducing the point cloud to 2% of its original size to simulate a low-cost system, this difference increases (mean deviation = 0.12 m, standard deviation = 0.19 m, RMSE = 0.22 m). We found that applying the simulated low-cost TLS system in precision agriculture is possible with acceptable accuracy up to an angular scan resolution of 8 mrad (i.e., point spacing of 80 mm at 10 m distance). General guidelines for the measurement set-up and an automatically executable method for CHM generation and assessment are provided and deserve consideration in further studies. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI
Remote Sens. 2016, 8(3), 202; https://doi.org/10.3390/rs8030202
Received: 14 December 2015 / Revised: 15 January 2016 / Accepted: 18 February 2016 / Published: 1 March 2016
Cited by 23 | PDF Full-text (7273 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective [...] Read more.
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series
Remote Sens. 2016, 8(2), 154; https://doi.org/10.3390/rs8020154
Received: 12 October 2015 / Revised: 21 January 2016 / Accepted: 25 January 2016 / Published: 17 February 2016
Cited by 10 | PDF Full-text (6414 KB) | HTML Full-text | XML Full-text
Abstract
Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from [...] Read more.
Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards
Remote Sens. 2016, 8(1), 39; https://doi.org/10.3390/rs8010039
Received: 7 October 2015 / Revised: 8 December 2015 / Accepted: 29 December 2015 / Published: 5 January 2016
Cited by 23 | PDF Full-text (2985 KB) | HTML Full-text | XML Full-text
Abstract
In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has [...] Read more.
In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ѰL) throughout the season. The feasibility of mapping within-orchard spatial variability of ѰL from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. ѰL relationships. For a specific given CWSI value, ѰL was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive ѰL maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise ѰL mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m2. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment
Remote Sens. 2015, 7(11), 14708-14730; https://doi.org/10.3390/rs71114708
Received: 3 July 2015 / Revised: 28 September 2015 / Accepted: 30 October 2015 / Published: 5 November 2015
Cited by 19 | PDF Full-text (1454 KB) | HTML Full-text | XML Full-text
Abstract
The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead “tendone” systems in the Apulia [...] Read more.
The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead “tendone” systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the “direct” methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm·d−1, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013, and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features
Remote Sens. 2015, 7(11), 14559-14575; https://doi.org/10.3390/rs71114559
Received: 25 August 2015 / Revised: 26 October 2015 / Accepted: 29 October 2015 / Published: 4 November 2015
Cited by 9 | PDF Full-text (769 KB) | HTML Full-text | XML Full-text
Abstract
The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) [...] Read more.
The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R2 = 0.88 and RMSE = 11.34%) or textural feature (R2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard
Remote Sens. 2015, 7(11), 14458-14481; https://doi.org/10.3390/rs71114458
Received: 1 September 2015 / Revised: 30 September 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
Cited by 18 | PDF Full-text (1385 KB) | HTML Full-text | XML Full-text
Abstract
Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, [...] Read more.
Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38–0.70) and mean cluster weight (0.40). These results proved the utility of the multi-spectral imagery acquired from a RPAS to delineate homogeneous zones within the vineyard, allowing the grapegrower to carry out a specific management of each subarea. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping
Remote Sens. 2015, 7(10), 13586-13605; https://doi.org/10.3390/rs71013586
Received: 28 July 2015 / Revised: 29 September 2015 / Accepted: 1 October 2015 / Published: 19 October 2015
Cited by 28 | PDF Full-text (850 KB) | HTML Full-text | XML Full-text
Abstract
There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing [...] Read more.
There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI), but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat). The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices (i.e., NDVI) did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method), and carotenoids pigment indices (PRI and CAR) demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high-resolution thermal and hyperspectral airborne imagery are efficient tools for field-based phenotyping providing additional information to standard NDVI imagery currently used. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management
Remote Sens. 2015, 7(10), 12793-12814; https://doi.org/10.3390/rs71012793
Received: 17 June 2015 / Revised: 22 August 2015 / Accepted: 21 September 2015 / Published: 29 September 2015
Cited by 37 | PDF Full-text (3632 KB) | HTML Full-text | XML Full-text
Abstract
This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop [...] Read more.
This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation
Remote Sens. 2015, 7(9), 12400-12418; https://doi.org/10.3390/rs70912400
Received: 29 June 2015 / Revised: 31 August 2015 / Accepted: 7 September 2015 / Published: 22 September 2015
Cited by 9 | PDF Full-text (820 KB) | HTML Full-text | XML Full-text
Abstract
The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy [...] Read more.
The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve simulated output of the grain yield and protein content of winter wheat. The results show that the modified simple ratio (MSR) and normalized difference red edge (NDRE) better estimated LAI and CNA, respectively, compared with the other possible vegetation indices. The integration of both LAI and CNA resulted in a more robust DSSAT-CERES models with than each one alone. The R2 and RMSE values, respectively, of the regression between the simulated (using the two assimilation variables method) and measured LAI were 0.828 and 0.494, and for CNA were 0.808 and 20.26 kg N∙ha−1. These two assimilation variables resulted in grain yield and protein content estimates of winter wheat with a high precision and R2 and RMSE values of 0.698 and 0.726 ton∙ha−1, and 0.758% and 1.16%, respectively. This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass
Remote Sens. 2015, 7(9), 11449-11480; https://doi.org/10.3390/rs70911449
Received: 22 June 2015 / Revised: 23 August 2015 / Accepted: 2 September 2015 / Published: 9 September 2015
Cited by 52 | PDF Full-text (2086 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and [...] Read more.
Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R2: 0.07–0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessTechnical Note
Imagine All the Plants: Evaluation of a Light-Field Camera for On-Site Crop Growth Monitoring
Remote Sens. 2016, 8(10), 823; https://doi.org/10.3390/rs8100823
Received: 29 July 2016 / Revised: 6 September 2016 / Accepted: 22 September 2016 / Published: 7 October 2016
Cited by 10 | PDF Full-text (8178 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The desire to obtain a better understanding of ecosystems and process dynamics in nature accentuates the need for observing these processes in higher temporal and spatial resolutions. Linked to this, the measurement of changes in the external structure and phytomorphology of plants is [...] Read more.
The desire to obtain a better understanding of ecosystems and process dynamics in nature accentuates the need for observing these processes in higher temporal and spatial resolutions. Linked to this, the measurement of changes in the external structure and phytomorphology of plants is of particular interest. In the fields of environmental research and agriculture, an inexpensive and field-applicable on-site imaging technique to derive three-dimensional information about plants and vegetation would represent a considerable improvement upon existing monitoring strategies. This is particularly true for the monitoring of plant growth dynamics, due to the often cited lack of morphological information. To this end, an innovative low-cost light-field camera, the Lytro LF (Light-Field), was evaluated in a long-term field experiment. The experiment showed that the camera is suitable for monitoring plant growth dynamics and plant traits while being immune to ambient conditions. This represents a decisive contribution for a variety of monitoring and modeling applications, as well as for the validation of remote sensing data. This strongly confirms and endorses the assumption that the light-field camera presented in this study has the potential to be a light-weight and easy to use measurement tool for on-site environmental monitoring and remote sensing purposes. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessTechnical Note
A Combination of Plant NDVI and LiDAR Measurements Improve the Estimation of Pasture Biomass in Tall Fescue (Festuca arundinacea var. Fletcher)
Remote Sens. 2016, 8(2), 109; https://doi.org/10.3390/rs8020109
Received: 7 October 2015 / Revised: 19 January 2016 / Accepted: 25 January 2016 / Published: 1 February 2016
Cited by 25 | PDF Full-text (2477 KB) | HTML Full-text | XML Full-text
Abstract
The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized [...] Read more.
The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized difference vegetation index (NDVI). In a random plot design, measurements of NDVI and pasture height were combined to estimate biomass with a root mean square error of prediction (RMSEP) equal to ±455.28 kg green dry matter (GDM)/ha, over a range of 286 kg to 3933 kg GDM/ha. The combination of NDVI and height measurements were observed to be more accurate in assessing total biomass than just the NDVI (RMSEP ± 846.51 kg/ha) and height (RMSEP ± 708.13 kg/ha). Based on the results of the study it was concluded the use of combined LiDAR and active optical reflectance sensors can help unlock the complex interrelationship between green fraction and biomass in swards containing both green and senescent material. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessCorrection
Correction: Tilly, N. et al. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449–11480
Remote Sens. 2015, 7(12), 17291-17296; https://doi.org/10.3390/rs71215878
Received: 10 December 2015 / Accepted: 15 December 2015 / Published: 19 December 2015
Cited by 1 | PDF Full-text (1277 KB) | HTML Full-text | XML Full-text
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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