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Special Issue "Advances in Remote Sensing of Agriculture"

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

Deadline for manuscript submissions: closed (30 November 2012)

Special Issue Editor

Guest Editor
Prof. Dr. Clement Atzberger

University of Natural Resources and Life Sciences (BOKU), A-1190 Vienna, Austria
Website | E-Mail
Phone: +43 (1) 47654 5101
Interests: advanced remote sensing techniques for vegetation monitoring and dynamics; drought early warning systems; remote sensing for agriculture, forestry and natural resource management; imaging spectroscopy; time series analysis; radiative transfer modeling

Special Issue Information

Dear Colleagues,

The regular and timely monitoring of agricultural resources at regional to local scale is vital for economic and environmental purposes, as well as for appropriate response to food security issues at global scale. Remote sensing has shown a high potential to provide valuable information regarding the extent, status and management of agricultural land at various spatial and temporal scales.

With this special issue we compile state-of-the-art research that specifically addresses various aspects of the agricultural systems: national to global monitoring activities, crop area estimates and yield predictions, retrieval of crop biophysical characteristics, data assimilation in mechanistic crop growth models, application of remote sensing in precision agriculture (e.g., irrigation water management, fertilization). Review contributions are welcomed as well as papers describing new measurement concepts/sensors.

Prof. Clement Atzberger
Guest Editor

Keywords

  • Cropland area & crop identification
  • Yield & production forecasts
  • Global food security & early warning systems
  • Crop functioning & remote sensing data assimilation
  • Crop biophysical variables & crop status
  • Agricultural water management
  • Precision agriculture
  • Cropping patterns

Published Papers (18 papers)

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Research

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Open AccessArticle Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method
Remote Sens. 2013, 5(7), 3190-3211; doi:10.3390/rs5073190
Received: 5 May 2013 / Revised: 23 June 2013 / Accepted: 24 June 2013 / Published: 1 July 2013
Cited by 12 | PDF Full-text (1745 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the
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In recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the crop. In this study, a new method was used to define such thresholds for identifying the start and end of the growing season (SOS/EOS) for 43 different agricultural zones in China. The method used 2000–2003 NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data with a spatial resolution of eight kilometers and a temporal resolution of 15 days. Following data pre-processing, time series for the normalized difference vegetation index (NDVI or N), slope of the NDVI curve (S), and difference (D) between the NDVI value and a base NDVI value for bare land without snow were constructed. For each zone, an optimal set of threshold values for N, D, and S was determined, based on the remote sensing data and observed SOS/EOS data for 2003 at 261 agro-meteorological stations. Results were verified by comparing the accuracy of the new proposed NDS threshold method with the results of three other methods for SOS/EOS detection with remote sensing data. The findings of all four methods were compared to in situ SOS/EOS data from 2000 to 2002 for 110 agro-meteorological stations. Results show that the developed NDS threshold method had a significantly higher accuracy compared with other methods. The method is mainly limited by the observed data and the necessity of reestablishing the thresholds periodically. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle The Intercomparison of X-Band SAR Images from COSMO‑SkyMed and TerraSAR-X Satellites: Case Studies
Remote Sens. 2013, 5(6), 2928-2942; doi:10.3390/rs5062928
Received: 4 April 2013 / Revised: 14 May 2013 / Accepted: 4 June 2013 / Published: 6 June 2013
Cited by 15 | PDF Full-text (3251 KB) | HTML Full-text | XML Full-text
Abstract
The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from
[...] Read more.
The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle) at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target). Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB), while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM). Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI
Remote Sens. 2013, 5(5), 2184-2199; doi:10.3390/rs5052184
Received: 15 March 2013 / Revised: 26 April 2013 / Accepted: 26 April 2013 / Published: 10 May 2013
Cited by 11 | PDF Full-text (1080 KB) | HTML Full-text | XML Full-text
Abstract
This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002–2010), to forecast sugarcane yield on an annual and zonal base. To take into account
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This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002–2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha−1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data
Remote Sens. 2013, 5(4), 1734-1753; doi:10.3390/rs5041734
Received: 12 February 2013 / Revised: 20 March 2013 / Accepted: 21 March 2013 / Published: 8 April 2013
Cited by 4 | PDF Full-text (658 KB) | HTML Full-text | XML Full-text
Abstract
Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal
[...] Read more.
Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY) would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs) of the National Agricultural Statistics Service (NASS). Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale
Remote Sens. 2013, 5(4), 1549-1567; doi:10.3390/rs5041549
Received: 29 January 2013 / Revised: 7 March 2013 / Accepted: 11 March 2013 / Published: 26 March 2013
Cited by 4 | PDF Full-text (950 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past
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The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets
Remote Sens. 2013, 5(3), 1335-1354; doi:10.3390/rs5031335
Received: 31 December 2012 / Revised: 1 March 2013 / Accepted: 1 March 2013 / Published: 14 March 2013
Cited by 19 | PDF Full-text (1808 KB) | HTML Full-text | XML Full-text
Abstract
For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution
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For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection
Remote Sens. 2013, 5(3), 1389-1404; doi:10.3390/rs5031389
Received: 25 January 2013 / Revised: 11 March 2013 / Accepted: 12 March 2013 / Published: 14 March 2013
Cited by 16 | PDF Full-text (1223 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture
[...] Read more.
Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and ΔHV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and ΔHV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas
Remote Sens. 2013, 5(3), 1274-1291; doi:10.3390/rs5031274
Received: 16 January 2013 / Revised: 6 March 2013 / Accepted: 6 March 2013 / Published: 12 March 2013
Cited by 20 | PDF Full-text (717 KB) | HTML Full-text | XML Full-text
Abstract
This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing
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This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring
Remote Sens. 2013, 5(3), 1091-1116; doi:10.3390/rs5031091
Received: 5 January 2013 / Revised: 26 February 2013 / Accepted: 27 February 2013 / Published: 1 March 2013
Cited by 14 | PDF Full-text (2580 KB) | HTML Full-text | XML Full-text
Abstract
A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using
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A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields
Remote Sens. 2013, 5(2), 864-890; doi:10.3390/rs5020864
Received: 21 December 2012 / Revised: 6 February 2013 / Accepted: 15 February 2013 / Published: 20 February 2013
Cited by 6 | PDF Full-text (1620 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater
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This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia
Remote Sens. 2013, 5(2), 539-557; doi:10.3390/rs5020539
Received: 3 December 2012 / Revised: 15 January 2013 / Accepted: 16 January 2013 / Published: 28 January 2013
Cited by 19 | PDF Full-text (1233 KB) | HTML Full-text | XML Full-text
Abstract
Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in
[...] Read more.
Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R2, are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing
Remote Sens. 2013, 5(1), 254-273; doi:10.3390/rs5010254
Received: 20 November 2012 / Revised: 4 January 2013 / Accepted: 4 January 2013 / Published: 15 January 2013
Cited by 6 | PDF Full-text (2688 KB) | HTML Full-text | XML Full-text
Abstract
Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop
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Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale
Remote Sens. 2013, 5(1), 19-41; doi:10.3390/rs5010019
Received: 15 October 2012 / Revised: 18 December 2012 / Accepted: 19 December 2012 / Published: 24 December 2012
Cited by 24 | PDF Full-text (2700 KB) | HTML Full-text | XML Full-text
Abstract
Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i)
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Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach in order to create the derived map of cropland areas at 250 m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1 km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3,591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the cropland represents more than 30% of the area of the validation pixel. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series
Remote Sens. 2012, 4(12), 3972-3994; doi:10.3390/rs4123972
Received: 22 October 2012 / Revised: 2 December 2012 / Accepted: 5 December 2012 / Published: 11 December 2012
Cited by 8 | PDF Full-text (723 KB) | HTML Full-text | XML Full-text
Abstract
The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the
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The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study
Remote Sens. 2012, 4(11), 3364-3389; doi:10.3390/rs4113364
Received: 31 August 2012 / Revised: 24 October 2012 / Accepted: 31 October 2012 / Published: 6 November 2012
Cited by 23 | PDF Full-text (1385 KB) | HTML Full-text | XML Full-text
Abstract
Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to
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Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to agricultural change over longtime periods. This specifically relates to sub-humid to semi-arid ecosystems, whereagricultural change in grazing lands can only be detected based on long time series. Byintegrating data from different sensors it is theoretically possible to construct NDVI timeseries back to the early 1980s. However, such integration is hampered by uncertainties inthe comparability between different sensor products. To be able to rely on vegetationtrends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent acrossproducts. In this paper we analyzed several indicators of vegetation change for a range ofagricultural systems in Inner Mongolia, China, and compared the results across differentsatellite archives. Specifically, we compared two of the prime NDVI archives—AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT)NDVI. Because a true accuracy assessment of long time series is not possible, we furthercompared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We foundhigh similarities in interannual trends, and also in trends of the seasonal amplitude andintegral between SPOT VGT and MODIS Terra (r > 0.9). However, we observedconsiderable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOTVGT. We detected similar discrepancies for trends based on phenological parameters, suchas amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles.Inconsistencies were partially related to land cover and vegetation density. Differentpre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introducedfurther uncertainties. Our results corroborate findings from other studies that vegetationtrends derived from AVHRR GIMMS data not always reflect true vegetation changes. Amore thorough understanding of the factors introducing uncertainties in AVHRR GIMMStime series is needed, and we caution against using AVHRR GIMMS data in regionalstudies without applying regional sensitivity analyses.  Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Open AccessArticle Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery
Remote Sens. 2012, 4(9), 2492-2509; doi:10.3390/rs4092492
Received: 30 June 2012 / Revised: 27 July 2012 / Accepted: 15 August 2012 / Published: 27 August 2012
Cited by 9 | PDF Full-text (897 KB) | HTML Full-text | XML Full-text
Abstract
Coffee is the second most valuable traded commodity worldwide. Brazil is the world’s largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield
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Coffee is the second most valuable traded commodity worldwide. Brazil is the world’s largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)

Review

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Open AccessReview Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
Remote Sens. 2013, 5(4), 1704-1733; doi:10.3390/rs5041704
Received: 8 February 2013 / Revised: 28 March 2013 / Accepted: 2 April 2013 / Published: 8 April 2013
Cited by 52 | PDF Full-text (998 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally
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Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessReview Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
Remote Sens. 2013, 5(2), 949-981; doi:10.3390/rs5020949
Received: 30 December 2012 / Revised: 6 February 2013 / Accepted: 6 February 2013 / Published: 22 February 2013
Cited by 98 | PDF Full-text (2268 KB) | HTML Full-text | XML Full-text | Correction | Supplementary Files
Abstract
Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus
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Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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