Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between -1.1 and 0.99 and -1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions

Relating Biomass and Leaf Area Index to Non-destructive Measurements in Order to Monitor Changes in Arctic Vegetation

This paper reports an alternative method for seasonal and long-term monitoring of biomass and the leaf area index (LAI) at Arctic tundra sites. Information related to the historical and projected change in abundance and distribution of biomass and LAI is required to address numerous environmental and resource management issues. Observations of earth from satellites could potentially be used to derive seasonal and long-term changes in biomass and the LAI. To realize this potential, seasonal and long-term ground monitoring data for validation are essential; however, the conventional destructive sampling method for measuring biomass and the LAI does not allow repetitive measurements at the same plots and thus is not suitable for monitoring change over time. Alternative methods, such as sampling nearby similar plots, can be laborious and easily subject to large sampling errors, especially in Arctic tundra sites with low vegetation cover. In this study, we developed a practical method for relating non-destructive measurements (percent cover and mean height) to biomass and the LAI for 13 major Arctic plant groups, or seven plant functional types, on the basis of measurements at 196 plots across Canada’s Arctic tundra ecosystems. Using the method at the plant group level to estimate plot total vascular aboveground biomass, foliage biomass, and LAI, we had r2 = 0.91–0.95 and relative mean absolute error of 25–29%. By this method, one could monitor seasonal and long-term changes in biomass and the LAI through repeated, non-destructive observations of percent cover and mean height at the same permanent plots.Cette communication présente une méthode de rechange en vue de la surveillance saisonnière et à long terme de la biomasse et de l’indice de surface foliaire (LAI) de sites de toundra de l’Arctique. Afin de relever divers enjeux relatifs à la gestion de l’environnement et des ressources, il faut recueillir des données se rapportant au changement historique et projeté en matière d’abondance et de répartition de la biomasse et du LAI. On pourrait éventuellement recourir aux observations de la Terre à partir de satellites afin de déceler les changements saisonniers et à long terme caractérisant la biomasse et le LAI. Pour en arriver là, il est essentiel de disposer de données saisonnières et à long terme au sol à des fins de validation. Cependant, la méthode d’échantillonnage destructeur classique permettant de mesurer la biomasse et le LAI ne permettent pas la prise de mesures répétitives aux mêmes sites et par conséquent, elle ne convient pas à la surveillance du changement qui s’exerce au fil du temps. D’autres méthodes, telles que l’échantillonnage de sites semblables dans les environs, peuvent s’avérer laborieuses et facilement faire l’objet d’importantes erreurs d’échantillonnage, surtout aux sites de toundra de l’Arctique dont la couverture végétale est basse. Dans le cadre de cette étude, nous avons mis au point une méthode pratique pour établir un rapport entre les mesures non destructives (pourcentage de couverture et hauteur moyenne) et la biomasse et le LAI de 13 groupes végétaux importants de l’Arctique, ou sept types végétaux fonctionnels en fonction de la mesure de 196 sites à la grandeur des écosystèmes de toundra de l’Arctique canadien. En nous appuyant sur la méthode des groupes végétaux pour estimer la biomasse vasculaire totale à ciel ouvert des sites, la biomasse foliaire et le LAI, nous avions r2 = 0,91–0,95 et une erreur absolue relative moyenne de 25 à 29%. Au moyen de cette méthode, il serait possible de surveiller les changements saisonniers et à long terme en matière de biomasse et de LAI grâce à des observations répétées et non destructives du pourcentage de la couverture et de la hauteur moyenne aux mêmes sites permanents

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space

Evaluation of the Algorithms and Parameterizations for Ground Thawing and Freezing Simulation in Permafrost Regions

Ground thawing and freezing depths (GTFDs) strongly influence the hydrology and energy balances of permafrost regions. Current methods to simulate GTFD differ in algorithm type, soil parameterization, representation of latent heat, and unfrozen water content. In this study, five algorithms (one semiempirical, two analytical, and two numerical), three soil thermal conductivity parameterizations, and three unfrozen water parameterizations were evaluated against detailed field measurements at four field sites in Canada’s discontinuous permafrost region. Key findings include: (1) de Vries’ parameterization is recommended to determine the thermal conductivity in permafrost soils; (2) the three unfrozen water parameterization methods exhibited little difference in terms of GTFD simulations, yet the segmented linear function is the simplest to be implemented; (3) the semiempirical algorithm reasonably simulates thawing at permafrost sites and freezing at seasonal frost sites with site-specific calibration. However, large interannual and intersite variations in calibration coefficients limit its applicability for dynamic analysis; (4) when driven by surface forcing, analytical algorithms performed marginally better than the semiempirical algorithm. The inclusion of bottom forcing improved analytical algorithm performance, yet their results were still poor compared with those achieved by numerical algorithms; (5) when supplied with the optimal inputs, soil parameterizations, and model configurations, the numerical algorithm with latent heat treated as an apparent heat capacity achieved the best GTFD simulations among all algorithms at all sites. Replacing the observed bottom temperature with a zero heat flux boundary condition did not significantly reduce simulation accuracy, while assuming a saturated profile caused large errors at several sites

OR-051 Exploration of Potential Integrated Biomarkers for Sports Monitoring Based on Metabolic Profiling

Objective Metabolomic analysis is extensively applied to identify sensitive and specific biomarkers capable of reflecting pathological processes and physical responses or adaptations. Exercise training leads to profound metabolic changes, manifested as detectable alterations of metabolite levels and significant perturbations of metabolic pathways in sera, urine, and rarely, in saliva. Several metabolites have been exploited as biomarkers for generally evaluating physical states in almost all sports. However, alterations of metabolic profile caused by specific sports would be heterogeneous. Thus, developments of new techniques are eagerly required to identify characteristic metabolites as unique biomarkers for specifically accessing training stimulus and sports performances. In the present work, we conducted both metabolic profiling and a binary logistic regression model (BRM) of biological fluids derived from rowing ergometer test with the following aims: 1) to examine changes of metabolite profiles and identify characteristic metabolites in the samples of sera, urine, and saliva; 2) to screen out potential integrated biomarkers for sports-specific monitoring. Methods A total of 11 rowers (6 male, 5 female; aged 15±1 years; 4±2 years rowing training) underwent an indoor 6000m rowing ergometer test. Samples of sera, urine and saliva were collected before and immediately after the test. 1D 1H NMR spectra were recorded with a Bruker Avance III 650 MHz NMR spectrometer. NMR spectra were processed and aligned, resonances of metabolites were assigned and confirmed, and metabolite levels were calculated based on NMR integrals. Multivariate statistical analysis was carried out using partial least-squares discrimination analysis (PLS-DA) to distinguish metabolic profiles between the groups. The validated PLS-DA model gave the variable importance in the projection (VIP) for a given metabolite. Moreover, inter-group comparisons of metabolite levels were quantitatively conducted using the paired-sample t-test. Then, we identified characteristic metabolites with VIP>1 in PLS-DA and p<0.05 in t-test. Furthermore, we screened out potential biomarkers based on the characteristic metabolites identified from the three types of biological fluids using the BRM (stepwise). Results The rowing training induced profound changes of metabolic profiles in serum and saliva samples rather than in urine samples. Totally, 44 metabolites were assigned in which 19, 20, and 19 metabolites were identified from serum, urine and saliva samples, respectively. Seven metabolites were shared by the three types of samples. Moreover, five characteristic metabolites (pyruvate, lactate, succinate, N-acetyl-L-cysteine, and acetone) were identified from the serum samples. The elevated levels of pyruvate, lactate and succinate suggested that, the rowing training evidently promoted both oxidative phosphorylation and glycolysis pathways. Furthermore, three characteristic metabolites (tyrosine, formate, and methanol) were identified from the saliva samples. Given that tyrosine is the precursor of dopamine, the increased level of salivary tyrosine in all rowers experiencing the test, suggesting that salivary tyrosine could be explored as a potential indicator closely related to nervous fatigue in the test. On the other hand, PLS-DA did not show observable distinction of metabolic profiles between the urine samples before and immediately after the test. Moreover, 20 urinary metabolites did not display detectable altered levels. We then established the BRM with the identified characteristic metabolites, from which we selected one optimal regression model based on serum pyruvate and salivary tyrosine (adjusted R square was 0.935, P<0.001), indicating that the two selected metabolites would efficiently reflect the metabolic alterations in the test. Conclusions As far as the 6000m rowing ergometer test is concerned, serum samples could be a preferred resource for assessing the changes of energy metabolism in the test, while urine samples might have a relatively lower sensitivity to exercise-induced metabolic responses. Even though metabolite levels in saliva samples are generally lower than those in serum and urine samples, some salivary metabolites potentially have higher sensitivities to exercise-induced metabolic responses. Thus, the integration of multiple biomarkers identified from different type of species could potentially provide more sensitive and specific manners to monitor physical states in sports and exercise. This work may be of benefit to the exploration of integrated biomarkers for sports-specific monitoring

Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

Early warning information on crop yield and production are very crucial for both farmers and decision-makers. In this study, we assess the skill and the reliability of the Integrated Canadian Crop Yield Forecaster (ICCYF), a regional crop yield forecasting tool, at different temporal (i.e. 1–3 months before harvest) and spatial (i.e. census agricultural region – CAR, provincial and national) scales across Canada. A distinct feature of the ICCYF is that it generates in-season yield forecasts well before the end of the growing season and provides a probability distribution of the forecasted yields. The ICCYF integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms. The model was evaluated against yield survey data of spring wheat, barley and canola during the 1987–2012 period. Our results showed that the ICCYF performance exhibited a strong spatial pattern at both CAR and provincial scales. Model performance was better from regions with a good coverage of climate stations and a high percentage of cropped area. On average, the model coefficient of determination at CAR level was 66%, 51% and 67%, for spring wheat, barley and canola, respectively. Skilful forecasts (i.e. model efficiency index & gt; 0) were achieved in 70% of the CARs for spring wheat and canola, and 43% for barley (low values observed in CAR with small harvested area). At the provincial scale, the mean absolute percentage errors (MAPE) of the September forecasts ranged from 7% to 16%, 7% to 14%, and 6% to 14% for spring wheat, barley and canola, respectively. For forecasts at the national scale, MAPE values (i.e. 8%, 5% and 9% for the three respective crops) were considerably smaller than the corresponding historical coefficients of variation (i.e. 17%, 10% and 17% for the three crops). Overall, the ICCYF performed better for spring wheat than for canola and barley at all the three spatial scales. Skilful forecasts were achieved by mid-August, giving a lead time of about 1 month before harvest and about 3–4 months before the final release of official survey results. As such, the ICCYF could be used as a complementary tool for the traditional survey method, especially in areas where it is not practical to conduct such surveys

Characterizing the Effect of Water Content on Small-Strain Shear Modulus of Qiantang Silt

Due to the impact of natural and artificial influence, such as waves, tides, and artificial dewatering, the small-strain shear modulus of soils may vary with the water content of soil, causing deformation of excavations and other earth structures. The present study used a resonant column device to investigate the effects of water content, void ratio, and confining pressure on the small-strain shear modulus of a silt extracted from an excavation site near Qiantang River in Hangzhou, China. The test results revealed that the effects of the three factors are not coupled and can be characterized by three individual equations. In particular, the small-strain shear modulus decreases with increasing water content under otherwise similar conditions, which can be characterized by a power function. The classical Hardin’s equation is modified to consider the effect of water content by introducing an additional function of water content

Estimating Regional Scale Hydroclimatic Risk Conditions from the Soil Moisture Active-Passive (SMAP) Satellite

Satellite soil moisture is a critical variable for identifying susceptibility to hydroclimatic risks such as drought, dryness, and excess moisture. Satellite soil moisture data from the Soil Moisture Active/Passive (SMAP) mission was used to evaluate the sensitivity to hydroclimatic risk events in Canada. The SMAP soil moisture data sets in general capture relative moisture trends with the best estimates from the passive-only derived soil moisture and little difference between the data at different spatial resolutions. In general, SMAP data sets overestimated the magnitude of moisture at the wet extremes of wetting events. A soil moisture difference from average (SMDA) was calculated from SMAP and historical Soil Moisture and Ocean Salinity (SMOS) data showed a relatively good delineation of hydroclimatic risk events, although caution must be taken due to the large variability in the data within risk categories. Satellite soil moisture data sets are more sensitive to short term water shortages than longer term water deficits. This was not improved by adding “memory” to satellite soil moisture indices to improve the sensitivity of the data to drought, and there is a large variability in satellite soil moisture values with the same drought severity rating

Salt-tolerance identification and quality evaluation of Abelmoschus manihot (L.) Medik

Abelmoschus manihot (L.) Medik. is a medicinal and edible plant. To evaluate its suitability for cultivation on the coastal saline-alkali land in northern China for high quality functional products, salt-tolerance identification and flavonoid contents were evaluated under saline treatments. Results showed that the salt-tolerance threshold of A. manihot ranged from 4.1 to 6.9 g L−1; however, low soil salt content (<3 g L−1) had the best growth and accumulation of total flavonoids. Sixteen kinds of common functional components such as hyperoside, rutoside, and quercetin were found. Of these components, the four (myricetin-3-0-glucoside, rutoside, quercetin-3′-0-glucoside, and gossypetin-8-0-β-d-glucuronic acid) with the highest content were chosen as the quality evaluation indexes. High levels of quality and yield occurred at a soil salt content of 3 g L−1. Our results suggested that soil salt content should not exceed 3 g L−1 in field cultivation for high quality and high yield of A. manihot.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada

This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016. Using a general cropland mask, the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August, whereas a negative correlation was observed in spring. Using crop-specific masks, the time of the strongest positive linear correlation for winter wheat was found between mid-May and early June, corresponding to peak growth stages of the crop. EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation, with considerable inter-annual variation. A multiple linear regression model was established for county-level yield estimation using EVI2 at peak growth stages and the year as independent variables. The model accounted for the spatiotemporal variability of grain yields of about 30% and 47% for winter wheat, 63% and 65% for corn, and 59% and 64% for soybean using the general cropland mask and crop-specific masks, respectively. A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops, as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data