Search

Winter wheat (Triticum aestivum L.) is a major crop in the Guanzhong Plain, China. Understanding its water status is important for irrigation planning. A few crop water indicators, such as the leaf equivalent water thickness (EWT: g cm-2), leaf water content (LWC: %) and canopy water content (CWC: kg m-2), have been estimated using remote sensing techniques for a wide range of crops, yet their suitability and utility for revealing winter wheat growth and soil moisture status have not been well studied. To bridge this knowledge gap, field-scale irrigation experiments were conducted over two consecutive years (2014 and 2015) to investigate relationships of crop water content with soil moisture and grain yield, and to assess the performance of four spectral process methods for retrieving these three crop water indicators. The result revealed that the water indicators were more sensitive to soil moisture variation before the jointing stage. All three water indicators were significantly correlated with soil moisture during the reviving stage, and the correlations were stronger for leaf water indicators than that of the canopy water indicator at the jointing stage. No correlation was observed after the heading stage. All three water indicators showed good capabilities of revealing grain yield variability in jointing stage, with R2 up to 0.89. CWC had a consistent relationship with grain yield over different growing seasons, but the performances of EWT and LWC were growing-season specific. The partial least squares regression was the most accurate method for estimating LWC (R2=0.72; RMSE=3.6%) and comparable capability for EWT and CWC. Finally, the work highlights the usefulness of crop water indicators to assess crop growth, productivity, and soil water status and demonstrates the potential of various spectral processing methods for retrieving crop water contents from canopy reflectance spectrums.

A simple target scattering model is developed integrating the shape factor and geometric randomness that are less influenced by the SAR configuration, dielectric properties of crop, and the underlying soil conditions, to describe the shape and statistical distribution of the targets, respectively. 47 fully polarimetric RADARSAT-2 images in FQ1W, FQ6W, FQ10W, FQ15W and FQ19W modes with different incidence angles acquired over Temiskaming Shores, Ontario, Canada in 2015 are adopted for the validation. Comparisons of the geometric features with H, α, and RVI, demonstrate that geometric features of wheat, oat, alfalfa, and barely show less fluctuation and more consistent with their growing stages over time till their harvest, but crops with broad leaves such as corn and soybean still show some fluctuations with high geometric randomness. In addition, the plots of n and δ of different crops also shows their potential on the crop classification.

Climate change is projected to increase growing season length and temperature in Canada but how soybean [Glycine max (L.) Merr.] will respond is uncertain. By modelling soybean responses to climate change scenarios, stakeholders can develop adaptation strategies. The CSM-CROPGRO-Soybean and STICS models were used to simulate soybean responses under baseline (1971–2000) and in near (2041–2070) and distant (2071–2100) future climate scenarios, including those resulting in altered seeding dates in eastern Canada. Field data collected in Ottawa were used to evaluate the models. The simulated seed yield using the CSM-CROPGRO-Soybean model showed an increase of about 14% (0.34 t ha−1) in the near future and a decrease in the distant future under RCP8.5 and the STICS model estimated a decrease in both the near and distant future. When the crop parameters determining the life cycle were increased by 30% and 40%, the simulated seed yield increased by more than 5%–10% and 10%–20% and by more than 20%–30% and 27%–40...

Agricultural production monitoring plays a key role in a variety of economic and environmental practices including crop yield forecasting, identifying risk of disease and application of chemicals. Remote sensing has the potential to provide accurate crop condition information across large areas and has the ability to deliver information products in a timely within-season manner. Synthetic aperture radar (SAR) frequencies are unaffected by most atmospheric conditions, making use of this technology of interest to crop monitoring. In this study, RADARSAT-2 polarimetric SAR responses of 21 parameters are compared with dry biomass of canola, corn, soybean, and spring wheat crops over a 6-week period for a site in western Canada. Dry biomass was targeted as this variable is a strong predictor of crop productivity. During the period of biomass accumulation, significant correlations with dry biomass were observed for most SAR parameters, for corn, canola, and soybeans. These findings are of interest as they could be used to target fungicide applications (canola) and to determine silage yields and resistance to disease (corn). For spring wheat, linear cross polarization and circular cross polarization backscatter, volume scattering and pedestal height were able to detect when wheat entered the milking stage which could prove useful as an indicator for the timing of spring wheat harvest. This study demonstrates that polarimetric SAR responds to accumulation of dry biomass, but as well that several radar parameters can uniquely identify changes in crop structure and phenology.

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.

Early and on-going crop production forecasts are important to facilitate food price stability for regions at risk, and for agriculture exporters, to set market value. Most regional and global efforts in forecasting rely on multiple sources of information from the field. With increased access to data from spaceborne Synthetic Aperture Radar (SAR), these sensors could contribute information on crop acreage. But these acreage estimates must be available early in the season to assist with production forecasts. This study acquired TerraSAR-X and RADARSAT-2 data over a region in eastern Canada dominated by economically important corn and soybean production. Using a supervised decision tree classifier, results determined that either sensor was capable of delivering highly accurate maps of corn and soybeans at the end of the growing season. Accuracies far exceeded 90%. Spatial and multi-temporal filtering approaches were compared and small improvements in accuracies were found by applying the multi-temporal filter to the RADARSAT-2 data. Of significant interest, this study determined that by using only three TerraSAR-X images corn could be accurately identified by the end of June, a mere six weeks after planting and at a vegetative growth stage (V6 – sixth leaf collar developed). However, soybeans required additional acquisitions given the variance in planting densities and planting dates in this region of Canada. In this case, accurate soybean classification required TerraSAR-X images until early August at the start of the reproductive stage (R5 – seed development is beginning). Also important, by applying a multi-temporal filter accurate mapping (close to 90%) of corn and soybeans from RADARSAT-2 could occur five weeks earlier (by August 19) than if a spatial filter was used. Thus application of this filtering approach could accelerate delivery of a crop inventory for this region of Canada. Corn and soybeans are important commodities both globally and within Canada. This study makes an important contribution as it demonstrates that TerraSAR-X can deliver acreage estimates of these two crops early enough to assist with in-season production forecasting.

For successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. The purpose of the study is to examine how two particular parameters, acquisition time and incidence angle, influences the response from various land use/land cover types (forests, urban infrastructure, surface water and marsh wetland targets) using nine RADARSAT-2 C-band fine-beam (FQ7 and FQ21) fully polarimetric SAR data acquired during the 2011 growing season over northern Ontario, Canada. The results indicate that backscatter from steep incidence angle acquisitions was typically higher than shallow angles. Wetlands showed an increase in HH and HV intensity due to the growth of emergent vegetation over the course of the summer. The forest and urban targets displayed little variation in backscatter over time. The surface water target showed the greatest difference with respect to incidence angle, but was also determined to be the most affected by wind conditions. Analysis of the co-polarized phase difference revealed the urban target as greatly influenced by the incidence angle. The observed phase differences of the wetland target for all acquisitions also suggested evidence of double-bounce interactions, while the forest and surface water targets showed little to no phase difference. In addition, Cloude-Pottier and Freeman-Durden decompositions, when analyzed in conjunction with polarimetric response plots, provided supporting information to confidently identify the various targets and their scattering mechanisms.

The well-documented energy balance dynamics within forest ecosystems are poorly implemented in studies of the biophysical effects of forests. This results in limitations to the accurate quantification of forest cooling/warming on local air temperature. Taking into consideration the forest air space, this study proposes a three-layered (canopy, forest air space and soil [CAS]) land surface energy balance model to simulate air temperature within forest spaces (Taf) and subsequently to evaluate its biophysical effects on forest cooling/warming, i.e., the air temperature gradient (∆Ta) between the Taf and air temperature of open spaces (Tao) (∆Ta = Taf − Tao). We test the model using field data for 23 sites across 10 cities worldwide; the model shows satisfactory performance with the test data. High-latitude forests show greater seasonal dynamics of ∆Ta, generating considerable cooling of local air temperatures in warm seasons but minimal cooling or even warming effects during cool seasons, while low-latitude tropical forests always exert cooling effects with less interannual variability. The interannual dynamics of ∆Ta are significantly related to the seasonality of solar geometry and canopy leaf phenology. The differences between forest canopy temperature (Tc) and Tao, which are the two most important terms attributed by the CAS model in impacting Taf, explain a large part of forest cooling and warming (May–July: R2 = 0.35; November–January: R2 = 0.51). The novel CAS model provides a feasible way to represent the energy balance within forest ecosystems and to assess its impacts on local air temperatures globally. Keywords: Forest cooling/warming, Forest biophysical effects, Radiative transfer equation, Land-atmosphere model, Local environmental health, Climate warming mitigation

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as &ldquo, vegetation index&rdquo, &ldquo, crop growth&rdquo, winter wheat&rdquo, leaf area index (LAI)&rdquo, and &ldquo, model&rdquo, in the field of crop growth monitoring, and &ldquo, LAI&rdquo, unmanned aerial vehicle (UAV)&rdquo, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.

Recent development in canopy optical-sensing technology provides the opportunity to apply fertilizer variably at the field scale according to spatial variation in plant growth. A field experiment was conducted in Ottawa, Canada, for two consecutive years to determine the effect of fertilizer nitrogen (N) input at variable- vs. uniform-application strategies at the V6–V8 growth stage, on soil mineral N, canopy reflectance, and grain yield of maize (Zea mays L.). The variable N rates were calculated using an algorithm derived from readings of average normalized difference vegetation index (NDVI) of about 0.8 m × 4.6 m, and N fertilizer was then applied to individual patches of the same size of NDVI readings (0.8 m × 4.6 m) within a plot (2184 m2). Canopy reflectance, expressed as NDVI, was monitored with a hand-held spectrometer, twice weekly before tasseling and once a week thereafter until physiological maturity. Soil mineral N (0–30 cm depth) was analyzed at the V6 and VT growth stages. Our data show that both variable and uniform-application strategies for N side-dressings based on canopy-reflectance mapping data required less amount of N fertilizer (with an average rate of 80 kg N ha–1 as side-dressing in addition to 30 kg N ha–1 applied at planting), and produced grain yields similar to and higher nitrogen-use efficiency (NUE) than the preplant fully fertilized (180 kg N ha–1) treatment. No difference was observed in either grain yield or NUE between the variable- and uniform-application strategies. Compared to unfertilized or fully fertilized treatments, the enhancements in grain yield and NUE of the variable-rate strategy originated from the later N input as side-dressing rather than the variation in N rates. The variable-rate strategy resulted in less spatial variations in soil mineral N at the VT growth stage and greater spatial variations in grain yield at harvest than the uniform-rate strategy. Both variable- and uniform-application strategies reduced spatial variations in soil mineral N at the VT stage and grain yield compared to the unfertilized treatment. The variable-rate strategy resulted in more sampling points with high soil mineral N than the uniform-rate strategy at the VT stage.

This paper studies the feasibility of monitoring crop growth based on a trend analysis of three elementary radar scattering mechanisms using three consecutive years (2008-2010) of RADARSAT-2 (R-2) Fine Quad Mode data. The polarimetric synthetic aperture radar analysis is based on the Pauli decomposition. Multitemporal analysis is applied to RGB images constructed using surface scattering, double-bounce, and volume scattering, along with intensity analysis of these scattering mechanisms. The test site is located in Eastern Ontario, Canada where the cropping system is dominated by corn, spring wheat, and soybeans. Each crop has unique physical structural characteristics which provide different responses for these scattering mechanisms. Significant changes occur in these scattering mechanisms as the crops move from one phenological stage to the next. By monitoring these changes over the season, the crop growth cycle from emergence to harvest can be observed. When harvest occurs, the backscatter intensities change significantly, and these changes aid in identifying crops. The temporal evaluation of the intensity of the scattering mechanisms generally track the measured leaf area index and observed phenological plant development. Changes in growth stage are crop type specific. Thus, to monitor changes in crop phenology and the occurrence of harvest activities, knowledge of the crop grown in any particular field is required. To accommodate this requirement, a maximum likelihood classification was performed on the R-2 data to produce a crop map. An overall classification accuracy of 85% was achieved.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Cropland productivity, characterized by crop yields, is determined by soil and meteorological conditions as well as management practices, e.g., crop types and their associated phenological cycles. As canopy spectral reflectance is governed by vegetation photosynthetic activities and is indicative of primary productivity, we investigated the potential of using time-series NDVI for mapping spatial variability of cropland productivity in south-western Ontario, Canada. NDVI was derived from the 8-day composite 250-m MODIS surface reflectance data, using a general cropland mask and crop specific masks, respectively. It was observed that for the three major annual crops (corn, soybean and winter wheat), using a general cropland mask, the strongest positive linear correlation between county level crop yield and NDVI was reached between the end of July and early August; whereas using crop specific masks the time of strongest linear correlation for wheat was shifted to between mid-May and early June. Large differences in phenological patterns and interleaved spatial distribution of these different crops led to difficulties for yield estimation using low resolution remote sensing data in this region.

Understanding the historical trends and driving mechanism of China's soil moisture change is an important step in combating climate change. Using the time series satellite-derived Essential Climate Variable Soil Moisture (ECV_SM) product, we detected a significant decrease trend in land surface soil moisture in eastern China over a 32-year period (1979–2010). Theoretical sensitivity analysis suggested that soil moisture is regulated collectively by precipitation (P), potential evapotranspiration (PET), land surface conditions such as land cover/use changes, landscape features, irrigation and urban expansion etc. (m), and the water balance between input and output water supplies (O) (the input water supplies minus the output). The change in spatial pattern and temporal trend of C is highly consistent with the corresponding change in soil moisture. The magnitude of soil moisture variation is also well correlated with that of P/PET (R2=0.43; p

Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland productivity in the Canadian Prairies between 2000 and 2013 based on time series of MODIS (Moderate Resolution Imaging Spectroradiometer) FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) product. Key phenological metrics, including the start (SOS) and end of growing season (EOS), and the cumulative FAPAR (CFAPAR) during the growing season (between SOS and EOS), were extracted and calculated from the FAPAR time series with the Parametric Double Hyperbolic Tangent (PDHT) method. The Mann-Kendall test was employed to assess the trends of cropland productivity and climatic variables, and partial correlation analysis was conducted to explore the potential links between climate variability and cropland productivity. An assessment using crop yield statistical data showed that CFAPAR can be taken as a surrogate of cropland productivity in the Canadian Prairies. Cropland productivity showed an increasing trend in most areas of Canadian Prairies, in general, during the period from 2000 to 2013. Interannual variability in cropland productivity on the Canadian Prairies was influenced positively by rainfall variation and negatively by mean air temperature.

Synthetic Aperture Radars (SARs) transmit and receive energy at microwave frequencies. The response recorded by these sensors is largely a function of the structure and dielectric properties of the target. The structure of a canopy is different among crops, and changes as crops grow. SARs respond very well to these structural differences and thus these sensors are able to accurately identify crop type and have proven sensitive to several crop biophysical parameters (Leaf Area Index, biomass, canopy height). Although optical sensors have traditionally been used for crop monitoring, advances in SAR applications research coupled with availability of SAR data at different frequencies and polarizations has raised the profile of these sensors for agricultural monitoring. And the “all weather” capability of SARs makes their use in operational activities of particular interest. Advancements in SAR applications development, continued improved access to data, and a push to transfer SAR research methods to monitoring agencies will lead to an increased role of SAR in monitoring agricultural production. This chapter reviews SAR research as it relates to crop type and acreage estimation, as well as determination of crop condition and crop bio-physical properties.

The potential of a random forest (RF) classifier for radar-only crop classifications was evaluated for an eastern and western Canadian site. Overall classification accuracies were improved by approximately 4%–5% over traditional boosted decision trees with gains of up to 7% in the accuracies of specific classes. Accuracies above 85% were obtained for key crops including canola, soybeans, corn, and wheat. Variable importance measures generated by the RF classifier showed that the most important acquisitions occurred in late August to early September at peak biomass and after wheat harvest. The least important images were acquired in May and mid-July. The HV and VV polarizations had the most significant contributions, while the HH polarization contributed little throughout the season, except in late September when the HH response was largely driven by soil conditions. The sensitivity of three RF parameters (number of training pixels, number of trees, and number of variables to select from at each split) was...

In this study, quadrature-polarization (quad-pol) RADARSAT-2 data at steep (25o) and shallow (40o) incidence angles were acquired during the 2008 season, imaging 13 corn and soybean fields. The leaf area index (LAI) was derived from optical imagery, and volumetric soil moisture was measured coincident with each overpass. Many synthetic aperture radar (SAR) parameters were significantly correlated with derived corn and soybean LAI. The highest correlations were observed for parameters sensitive to volume scattering (HV, LL, and RR backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) at the steeper angle. For corn, the minimum correlation coefficient was 0.95. For soybeans, the coefficients were between 0.83 and 0.86. Sensitivity to LAI was lost late in the season, when the derived LAI exceeded 3.0 m2m−2. The derived LAI and the measured soil moisture were used to model several radar parameters (HV backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter)...

Rice is the staple grain in China and accounts for about 42% of the nation's food production. Most of China's paddy rice production is located in the southern provinces of the country where cloud cover and frequent rain severely limit opportunities for optical satellite acquisitions. The small field sizes, typical of paddy rice, also challenge the exploitation of satellite data for monitoring rice production. Synthetic aperture radar (SAR) sensors are able to successfully acquire data under most atmospheric conditions, and the change in backscatter, from rice emergence through to crop maturity and harvest, permits the detection of rice fields using SAR imagery. Recently launched sensors, including TerraSAR-X, can provide data at spatial resolutions suitable for rice monitoring in southern China. The objective of this study was to assess TerraSAR-X imagery for identification of late rice and to develop a change detection methodology to quantify changes in rice acreages. The lowlands of the Xuwen study site...

Presents corrections to affiliation information in the paper, “Image classification using rapideye data: Integration of spectral and textual features in a random forest classifier,” (Zhang, H., et al),IEEE J. Sel. Topics Appl. Earth Observ. Remote Ses., vol. 10, no. 12, pp. 5334–5349, Dec. 2017.

Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager decomposition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multifrequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable.

Agriculture plays a critical role within Canada’s economy and, as such, sustainability of this sector is of high importance. Targeting and monitoring programs designed to promote economic and environmental sustainability are a vital component within Canada’s agricultural policy. A hierarchy of land information, including up to date information on cropping practices, is needed to measure the impacts of programs on land use decision-making and to gauge the environmental and economic benefits of these investments. A multi-year, multi-site research activity was completed to develop a robust methodology to inventory crops across Canada’s large and diverse agricultural landscapes. To move towards operational implementation the methodology must deliver accurate crop inventories, with consistency and reliability. In order to meet these operational requirements and to mitigate risk associated with reliance on a single data source, the methodology integrated both optical and Synthetic Aperture Radar (SAR) imagery. The results clearly demonstrated that multi-temporal satellite data can successfully classify crops for a variety of cropping systems present across Canada. Overall accuracies of at least 85% were achieved, and most major crops were also classified to this level of accuracy. Although multi-temporal optical data would be the preferred data source for crop classification, a SAR-optical dataset (two Envisat ASAR images and one optical image) provided acceptable accuracies and will mitigate risk associated with operational implementation. The preferred dual-polarization mode would be VV–VH. Not only were these promising classification results repeated year after year, but the target accuracies were met consistently for multiple sites across Canada, all with varying cropping systems.

Acid mine drainage resulting from mine tailings poses an environmental threat. An important initial step towards the reclamation of mine tailing sites is to detect the presence of acid-generating, sulphide-rich minerals and determine their spatial distribution. In this study, the potential of hyperspectral remote sensing for characterizing mine tailings is investigated. The study site is located in northern Ontario, Canada, and the data were collected with PROBE-1, an imaging spectrometer that covers the visible, near-infrared, and shortwave-infrared spectral ranges. The results indicate that using the weakly constrained linear spectral unmixing technique PROBE-1 data can provide information on mineral compositions of the tailing surface. The spatial locations and associations of acid-generating source minerals such as pyrite and pyrrhotite along with their oxidation products (e.g., copiapite, jarosite, ferrihydrite, goethite, and hematite) can provide information about the distribution of oxidation proce...

In recent years, remote-sensing scientists have been developing applications for hyperspectral remote sensing. Analysis techniques, such as linear spectral unmixing, have been used increasingly to solve real-world problems related to vegetation stress detection, mineral prospecting, and environmental monitoring. Information products such as end member fraction maps can be generated by interpreting unmixing results. However, the validity of these maps has not been fully examined. In this paper, two types of constrained linear spectral unmixing techniques are investigated, namely fully constrained and weakly constrained unmixing. Overall, this study revealed that the weakly constrained approach provides more realistic results than the fully constrained technique. From a data-analysis perspective, this study demonstrates that the weakly constrained unmixing can be used successfully when an incomplete list of end members is used in the unmixing, which is almost always the case in reality. In addition, this pa...

Many research studies have investigated the retrieval of surface parameters for bare soils. This study attempts to take into account agricultural fields with crop residues and low vegetation cover. An adaptive two-component decomposition (ATCD) method is developed in this paper to invert surface parameters in these fields. The study area is located in Southwestern Ontario, Canada where two wheat fields and two corn-residue fields are selected as sample sites. Three fully polarimetric RADARSAT-2 data, which were acquired in 2013 and 2014 respectively, are employed for the validation. The ATCD has 100% inversion rate compared with the Bragg and X-Bragg models, while its root mean square errors (RMSE) of the soil moisture and roughness have the lowest value of 6.72% and 0.33, respectively. The derived soil moisture in wheat field also has the lowest RMSE value of 3.97% compared with the Y-CIEM.

This paper uses RADARSAT-2 QUADPOL fully POLarimetric Inteferometric Synthetic Aperture Radar (POLInSAR) data to detect agriculture crop fields changes using coherence optimization method. The RADARSAT-2 POLInSAR data, acquired in July and September 2010, contains wheat, corn and soybean fields. Interferogram and coherence images were generated using single polarimetric data and fully polarimetric data. The coherence optimization method was carried out by maximizing the complex Lagrangian function. The optimized coherence image from the largest eigenvalue can correctly detect changes in the agricultural fields which cannot be detected using single coherence image. The results were validated using ground truth information.

RESUMECet article evalue l'utilisation d'images radar a synthese d'ouverture multidates (RSO) acquises par le satellite ERS-1 et des images multispectrales du capteur Landsat Thematic Mapper (TM) pour la detection et la classification des zones humides dans le sud de l'Ontario. L'etude demontre que quoique les images mono-canal ERS-1 donnent des resultats faibles, les performances des donnees multidates ERS-1 sont comparables a celles des donnees Landsat. La meilleure combinaison d'images ERS-1 acquises au cours d'une periode de quatre mois permet d'atteindre des resultats satisfaisants avec une precision de 83% globalement pour la zone d'etude. Une analyse plus detaillee a permis de demontrer que la performance des donnees radar multidates est nettement meilleure que les donnees radar unidates pour le suivi des zones humides. Comme les donnees radar ne sont pas affectees par la presence de nuages, ces donnees constituent une alternative potentielle aux donnees satellite optiques.

International audience; The study of rice identification during early season aims to maintain cultivated area and distribution status of rice during its early season so as to provide decision support for agricultural management sectors to develop policies and measures, as well as important information for agricultural insurance sectors and agricultural products market. Multi-polarization TerraSAR-X data of Xuwen study site locating in Southern China were programmed during early season of early rice in the year of 2009. By means of comparing analysis, it is discovered that rice has more obvious identification features in jointing period and flowering period. Rice identification experiments are carried out using decision tree and object-oriented classification method. The results show that imagery acquired at the jointing period is more suitable for rice identification during the early season for a nationwide operational rice monitoring system.

This paper investigated the potential of multi-temporal polarimetric RADARSAT-2 data for crop classification in an urban/rural fringe area. Using five scenes of RADARSAT-2 fine beam Quadpol data acquired during the 2012 growing season, five main crop types (wheat, soybeans, corn, field peas, and forage) in Southwestern Ontario, Canada have been identified. The potential of the RADARSAT-2 data for crop classification was assessed on four aspects: (1) the selection of classifier, (2) the effectiveness of polarimetric parameters, (3) the combination of multi-temporal data, and (4) post-classification processing methods. Pauli decomposition parameters proved to be effective in crop classification using Gaussian based Maximum Likelihood Classifier. With five dates of the images, the five crop types and other four non-crop types were classified at an overall accuracy of 91%. Satisfactory results with an overall accuracy of 87.8% were achieved by using only three dates of data given that the images covering the critical crop growth stages were included. Results demonstrate that polarimetric RADARSAT-2 data are suitable for accurate crop mapping in urban/rural fringe areas.

Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. In 2007, Agriculture and Agri-Food Canada (AAFC) took its first steps towards the development of an operational software system for mapping the crop types of individual fields using satellite observations. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC, SPOT) and radar (Radarsat-2) imagery. For the 2011 growing season and further years, this activity is extended to other provinces in support of a national crop inventory. At present, this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m. To achieve full operational status, however, further development is required to optimize the data processing chain. Crop maps covering Canada's entire agricultural region are typically delivered eight months following the end of the growing season. To better meet the needs of AAFC and its partners, as well as those of potential new users, map delivery needs to be more timely. Indeed, there is considerable demand for two map products: an estimated within-season inventory (released during the growing season) as well as a final end-of-season inventory (released shortly after the end of the growing season). To this end, Earth Observation Service (EOS) staff is implementing a new and fully automated crop classifier that should significantly reduce production time. In 2012, the lack of affordable optical data forced AAFC to rely mostly on RADARSAT-2 data. This brings new challenges, given a doubling of the number of images as compared to 2011. In the coming years, new EO data (Landsat 8, Radarsat Constellation Mission, Sentinel-2) will have a significant positive impact on the quality of the AAFC crop inventory.

Time series of remotely sensed data are usually an important source of information for various agricultural applications. However, modeling and analyzing of time series data is not straightforward. In this paper, a new method for classification of time series data is proposed. The method is a modified Maximum Likelihood (ML) algorithms that uses Support Vector Regression (SVR) algorithms to estimates the probability of each class. The method is tested on the NDVI time series obtained from TM and ETM+ data for an agricultural region in Qazvin province, Iran. Six different crops have been considered for this study, and the classification results are evaluated using the kappa coefficient and the overall accuracy. The evaluations of experimental results and their comparison with the classic ML algorithm show that the proposed method, with kappa coefficient greater than 0.9, is a promising method and could be an alternative approach for agricultural classification needs.

Multi-temporal C-band RADARSAT-2 fine quad-pol data (5 scenes) and X-band TerraSAR-X stripmap data (8 scenes) were collected during the 2009 growing season from late May to late end of August. The study site was located in Carman, Manitoba in western Canada. A crop survey was conducted for 441 fields covering a wide range of annual and perennial crops (15 classes) including beans, canola, corn, fallow, field peas, flaxseed, hay/pasture, potato, soybean, sunflower, barley, oat, spring wheat and winter wheat. A supervised support vector machine method was used for image classification. Results revealed that the use of single-date single-frequency SAR data can only achieve marginal success in separating crop classes over regions with complex crop mixtures (15 classes). Better classification accuracies usually occur at the end of the growing season. The C-band SAR image data are generally superior to the X-band dataset. The overall classification accuracies using multi-temporal, single-frequency SAR data are within 50% for mid-season and 75% at the end of the growing season. The integration of RADARSAT-2 and TerraSAR-X data produced more favorable classification results.

Information on crop phenological state such as flowering, maturing, drying, senescence, and harvesting is essential for crop production surveillance and yield prediction. Earth Observation data provide an important information source for monitoring crop development at various spatial scales. Crop phenological changes have long been related to optical spectral signatures such as the normalized difference vegetation index (NDVI) and spectral shifts in red-edge. In recent years, more efforts have been made to explore the sensitivity of synthetic aperture radar (SAR) to crop biophysical parameters. As an experiment, this study investigates the signal response (polarization and decomposition) of C-band SAR to spring wheat development in northern Ontario, Canada. Significant relationships were found between the SAR intensity (HH, HV) and crop height. R2 values of 0.83 and 0.87 were achieved between crop height and the HH and HV polarization modes, respectively. An even higher correlation was found between the SAR decomposition parameter (Alpha angle) and crop height with an R2 of 0.93. The close relationship between crop height and SAR response offers a viable means for using SAR to track crop growth.

This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building�road, urban block, and suburban commercial�industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial�industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%.

Biomass is an important source of renewable energy that has the potential to reduce greenhouse gas emissions. Some food crops, such as corn, can be used as feedstock for bioenergy production. Under limited cultivated land resources, this may lead to increased food prices due to resource competition between food and fuel. Some energy crops, such as switchgrass and hybrid poplar, can be grown on marginal lands which are not profitable for food crops due to low productivity. Thus, identifying and developing marginal land for bioenergy production could relieve the competition on land resources. The focus of this study was to assess the potential of Canadian marginal land for bioenergy production. The area of available marginal land was determined using a GIS overlay of a land suitability rating system (LSRS) and a land cover map of Canada. We assumed that bioenergy crops would essentially replace livestock production on rangelands (grassland and shrubland), but would not affect environmentally sensitive protected land. The production costs of growing energy crops on marginal land were then analyzed using a biomass production cost model. The results showed that about 9.48 million hectares could be identified as available marginal land in Canada. If this land is fully utilized for growing energy crops, the production of bio-fuel would be 33 million tons (under switchgrass) or 380 million tons (under hybrid poplar). The average Canadian production cost of switchgrass is about $318 ha−1, which is slightly lower than that for hybrid poplar ($340 ha−1 for Alberta, and $436 for Ontario). Although economics of plantation hybrid poplar looks positive, such is not the case for switchgrass or coppiced poplar. Growing hybrid poplar as a feedstock for bioenergy production on marginal land is an economically viable agricultural activity when the biomass price is greater than $18 per ton or with subsidies from government. Bioenergy production on marginal land in Canada could be helpful in securing food and energy supply, but would have a negative impact on the livestock industry.

Leaf area index (LAI) is an important indicator of plant growth and biomass accumulation. LAI is often required as an input parameter in many models, especially for crop yield predication and soil moisture retrieval. With recently due to the increasing demand on large-scale monitoring, optical satellite sensors capable of providing frequent LAI estimation over large coverage are favored. This paper reports the results from a study over an agriculture site with spring wheat in western Canada using data acquired from RapidEye and in situ measurements using LAI analyzer and hemispherical photos during the 2011 growing season. The prediction of crop ground cover fraction has yield a coefficient of determination of 0.66. Even better result was achieved for LAI prediction with a coefficient of determination of 0.82. Results suggest that RapdiEye optical satellite data can be a valuable data source for crop ground cover fraction and LAI retrieval.

The availability of advanced satellite radar sensors (C-band RADARSAT-2 and X-band TerraSAR-X) provides significant opportunities for timely monitoring of crop growth. Recent studies revealed that many polarimetric SAR parameters are sensitive to crop Leaf Area Index (LAI). However the reduced swath coverage of fully polarimetric SAR limits the operational application of these modes for large regional monitoring activities. Compact polarimetry mode, on the other hand, permits much larger swath coverage than fully polarimetric SAR. This study investigates the sensitivity of compact polarimetry SAR parameters to crop LAI using simulated data from RADARSAT-2 imagery collected in Canada over two growing seasons. Results revealed that compact polarimetric decomposition parameters associated with volumetric scattering are well correlated with crop LAI. This suggests that compact polarimetric SAR can be an important data source for large scale crop growth monitoring.

Most of China's rice production is located in the southern provinces of the country where frequent cloudy conditions hinder the successful acquisition of optical imagery. Small field sizes and complex planting patterns pose additional challenges to crop mapping using remote sensing approaches. High resolution radar data are most suitable for operational monitoring of crops in this region of China. In this study, the suitability of high-resolution TerraSAR-X StripMap data (6 m resolution) for identification of rice was investigated for a site in Xuwen, Guangdong Province, China. An integrated decision tree and object-oriented classification approach was used. The results showed that higher rice identification accuracies can be obtained using multi-temporal TerraSAR-X data at the tillering, jointing and flowering periods. Both the VV and VH polarizations provided accurate rice identification.

The object of this paper is to investigate the relationship between polarimetric SAR information and LAI. RADARSAT- 2 Fine Quad-pol SLC data with shallower and steeper incidence angles were programmed throughout the 2008 growing season. Optical data were acquired using a hyperspectral CASI airborne sensor as well as the SPOT-4 multi-spectral satellite. The optical data were used to generate LAI map for the entire study site. Backscatter coefficients, ratios of backscatter intensity, three polarimeric variables and three Cloude-pottier Decomposition parameters were extracted from the polarimetric data set. Temporal variations of the backscatter coefficient were analyzed. The results show an increase in backscatter with corn and soybean growth. The statistical analysis quantified the relationship between the radar parameters and LAI revealing a strong sensitivity for some radar configurations. For both corn and soybean, RADARSAT-2 cross-polarization (HV) backscatter at either shallow or steep incidence angles was well correlated with LAI. To avoid sensitivity to sensor calibration and changing target moisture conditions, ratios of backscatter intensity, polarimetric variables and Cloude-pottier Decomposition parameters were investigated. For corn, the ratio of HV/HH and HV/VV as well as pedestal height, total power, correlation coefficient, Entropy and alpha angle were highly correlation with LAI at steeper incidence angle. For soybean, the higher correlations were found with the ratio of HV/HH as well as pedestal height, total power, Entropy and alpha angle at shallow incidence angle. In general, the best results were observed for corn using the FQ6 acquisition. For soybean, the FQ20 data provided the most promising results.

This research outlines a preliminary assessment of the use of TerraSAR-X data for classifying agricultural crop land in Canada. X-Band data were able to identify crops (pasture-forage, soybeans, corn and wheat) to accuracies of 95% once a post-classification filter was applied. These accuracies were achieved using six TerraSAR-X images from 2008 and a decision-tree classification algorithm. Acquisitions began only mid-season and consequently a second full season TerraSAR-X data set is being collected in 2009. C-Band classification accuracies were about 10% lower in comparison. These results clearly demonstrate the potential of X-Band data for crop identification.

Agriculture plays an important role in the global economy, and sustainability of this sector is critical for world food security. Annual information on agricultural land use (crop inventory) would permit efficient and effective delivery of agricultural programs that support sustainability of this resource. Previous research has revealed encouraging results on using space borne satellite data (Landsat, SPOT) for crop mapping at the regional scale. Given Canada's large land mass, for operational crop monitoring satellite data with a wide swath and moderate spatial resolution are needed. This study presents the results on integrating RADARSAT-2 ScanSAR data with AWiFS data to improve crop identification. This study demonstrates that multi-temporal AWiFS data can produce an adequate crop classification, with an overall accuracy of 83%. The addition of ScanSAR data increases the overall classification accuracies. The radar contribution is most pronounced during the earlier season.

Information on agricultural land use (crop inventory) is needed by various organizations on an annual basis. To meet this operational requirement, Agriculture and Agri-Food Canada (AAFC) has carried out a multi-year (2004 - 2007), multi-sensor (Landsat TM, SPOT, RADARSAT-1, ASAR), and multi-site (five provinces: Ontario, Saskatchewan, Alberta, Manitoba, P.E.I.) research activity to develop a robust methodology to inventory crops across Canada's large and diverse agricultural landscapes. Results clearly demonstrated that multi-temporal satellite data can successfully classify crops for a variety of cropping systems across Canada. Overall accuracies of at least 85% were achieved. When available, multi-temporal (2 to 3 scenes acquired at different growth stages) optical data are ideal for crop classification. However due to cloud and haze interference, good optical data are not always obtainable. A SAR-optical combination offers a good alternative. This research has found that when only one optical image is available, the addition of two ASAR images acquired in VV/VH polarization will provide acceptable accuracies. Of particular interest is the observation that with the incorporation of radar, crop inventories can be delivered earlier in the growing season.