Improved leaf area index estimation by considering both temporal and spatial variations

Variations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI. The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r2 value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling

The Application of Next Generation Phenotyping Tools to a Wheat Breeding Programme

With the advent of high-throughput genotyping modern plant breeding has reached a new frontier of high-volume, high-density, yet low-cost, genomic data. Previously the acquisition of this data has been a logistical bottleneck within breeding programmes, yet with genomic data now abundantly available to breeding programmes, it has been speculated that the collection of phenotype data will become the next operational bottleneck. That being the inability to phenotype all material for all desired traits within a programme . The journey to improve the collection of phenotypic data is well underway, with focus being placed upon next generation phenotyping (NGP) technologies, such as high-throughput field phenotyping systems, to aid in the pairing of genotype to phenotype. Numerous sensors and methods of deployment have been investigated for application within small-plot field trials and suggested as tools for wheat and other field-crop breeding programmes, though few have explored how these can be deployed at scale or the suitability of collected data for use by breeders. This thesis investigates the deployment of commercially available digital cameras and LiDAR sensors within large-scale wheat breeding field trials, assessing the suitability of collected data for its application within the analytical pipelines of breeding programmes. Digital cameras were deployed opportunistically within large-scale wheat breeding trials, and through basic open-source image analysis methods, were capable of objectively assessing colour-based traits traditionally scored with visual assessment, producing levels of heritability similar to or greater than traditional methods. As part of this process a tractor-based high-throughput phenotyping platform was developed for the deployment of digital cameras, leveraging upon infrastructure present within the breeding programme and enabling images to be captured at a speed of 7,400 plots per hour. Given the success of digital cameras to measure colour-based traits, digital cameras were also deployed manually at a small scale to measure above ground biomass, plant height and harvest index, using photogrammetric techniques. Though data capture and processing methods were low-throughput, correlations between digital and manually collected measurements were strong (up to r = 0.94), highlighting the potential of the three-dimensional point cloud data type. To further this investigation LiDAR sensors were deployed on the high-throughput phenotyping platform to collect point cloud data of wheat plots from multiple field sites and collection dates. Processed point cloud data correlated strongly to traditional measurements of above ground biomass and canopy height and was shown to be highly repeatable and suitable for integration in routine breeding analyses. The findings of this work demonstrate that commercially available digital cameras and Li- DAR sensors can be deployed within large-scale wheat breeding trials, in a high-throughput, non-destructive and non-disruptive manner, for the accurate and repeatable measurement of traits which are traditionally subjective, laborious and/or destructive. Investigation of these measurements showed their suitability for inclusion within routine breeding analyses, giving breeders confidence in the data collected by next generation phenotyping technologies. The findings of this work are not only relevant to wheat breeders, but also to breeders of other field-crops and scientists conducting field research at a large scale.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 202

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects