Shifts in the timing of phenological events across many taxa and ecosystems are a result of climate change. Within a trophic network, phenological mismatches between interlinked species can have negative impacts for biodiversity, ecosystems, and the trophic network. Here we developed interaction indices that quantify the level of synchrony and asynchrony among groups of species in three interlinked trophic levels, as well as accounting for a dynamic representation of meteorology. Insect first flight, vegetation green-up and arrival of migrant birds were the phenological indicators, obtained from a combination of spatially and temporally explicit species observations from citizen science programmes and remote sensing platforms (i.e., Landsat). To determine phenological shifts in interlinked taxa we created and applied several phenological indices of synchrony-asynchrony, combining information from the phenological events and critical time windows of meteorological variables. To demonstrate our method of incorporating a meteorological component in our new interaction index, we implemented the relative sliding time window analysis, a stepwise regression model, to identify critical time windows preceding the phenological events on a yearly basis. The new indices of phenological change identified several asynchronies within trophic levels, allowing exploration of potential interactions based on synchrony among interlinked species. Our novel index of synchrony-asynchrony including a meteorological dimension could be highly informative and should open new pathways for studying synchrony among species and interaction networks.

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000-2500 nm shortwave infrared "SWIR2" range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.

Although wildfires in Ireland are not extensive, information on their impacts in terms of atmospheric emissions and pollutants, and habitat losses is essential. Current ground-based wildfire data are limited by their incompleteness, inconsistency in reporting, and a lack of timeliness. Additional data on fire alerts are drawn from international satellite derived databases such as NASA’s Fire Information for Resource Management System (FIRMS) and the European Forest Fire Information System (EFFIS) to produce a more consistent national summary. However, these databases exploit thermal anomalies derived from low spatial resolution satellite imagery, which can result in a large number of omissions of small, short-lived fires, especially when extensive cloud-cover persists, as is common in Ireland. To overcome these limitations, a new approach is proposed whereby data from the Copernicus Atmosphere Monitoring Service (CAMS) are used to identify atmospheric pollutant anomalies that may be associated with a wildfire, with Sentinel-2 pre- and post-fire imagery providing a more detailed account of the area burned and the vegetation cover affected. An inventory of fire events in Ireland reported by local and social media and the FIRMS and EFFIS databases from 2015-2020 was compiled. The average hourly concentration of selected pollutants (CO, O3, PM2.5, PM10, SO2, NOx) was derived from the CAMS European air quality analysis product at the location of each fire shortly before, during, and after the event. The average concentrations for the same period from the years excluding the year of the fire being studied were compared to the pollutant concentrations observed during the event. Preliminary results suggest that the concentration of PM2.5, PM10, SO2, and NOx show the clearest deviations from the baseline during the occurrence of a fire. Clear-sky Sentinel-2 images preceding and after selected fires were identified, and a number of different indices (NBR, dNBR, RdNBR, dMIRBI) calculated and combined to delineate burn event areas. Post-processing was undertaken to remove errors due to water, shadow and cloud cover, and eliminate features less than 0.4ha in size. Preliminary results show that burn scars can be clearly distinguished and their areas calculated, including fire events omitted from the 2015-2020 inventory. However, false alarms arise from natural land cover change, especially agricultural activity, and attempts to exclude these are being explored using the national mapping agency’s object-oriented digital mapping data model, PRIME2. Further analysis of the Sentinel-2 imagery to map the habitats burned is in progress, with a particular focus on identifying the location of gorse (Ulex europaeus), which is highly flammable in dry summer conditions due to the presence of deadwood. Atmospheric chemistry colleagues are undertaking a field campaign during 2021 to monitor the air quality during a burn event, along with laboratory measurements in a burn chamber, from which emissions factors for gorse can be calculated. Subsequently, it is hoped that detailed estimates of emissions from upland wildfires can be derived leading to improved national GHG inventories, and an assessment of these events made in terms of atmospheric impacts on population centres and environmental impacts on habitats and biodiversity.

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.

Many remote sensing analytical data products are most useful when they are in an appropriate regional or national projection, rather than globally based projections like Universal Transverse Mercator (UTM) or geographic coordinates, i.e., latitude and longitude. Furthermore, leaving data in the global systems can create problems, either due to misprojection of imagery because of UTM zone boundaries, or because said projections are not optimised for local use. We developed the open-source Irish Earth Observation (IEO) Python module to maintain a local remote sensing data library for Ireland. This pure Python module, in conjunction with the IEOtools Python scripts, utilises the Geospatial Data Abstraction Library (GDAL) for its geoprocessing functionality. At present, the module supports only Landsat TM/ETM+/OLI/TIRS data that have been corrected to surface reflectance using the USGS/ESPA LEDAPS/ LaSRC Collection 1 architecture. This module and the IEOtools catalogue available Landsat data from the USGS/EROS archive, and includes functions for the importation of imagery into a defined local projection and calculation of cloud-free vegetation indices. While this module is distributed with default values and data for Ireland, it can be adapted for other regions with simple modifications to the configuration files and geospatial data sets.

Sustainable agricultural management is essential not only to maintain productivity of current farmlands, but also to conserve natural environments. Records of agricultural activities are required to assist rapid assessment of agricultural lands, and thus, designation of management plans and policies. By the 1980s, when unfavorable environmental impacts of conventional tillage practices were widely recognized, agronomists introduced conservation tillage to benefit soils and agricultural environments, and soon began programs to monitor adoption of conservation tillage practices. The role of remote sensing in acquiring this information has been increasing because remote sensing technologies can provide the broad scope and the ability to collect sequential imagery to estimate trends and patterns of adoption of alternative tillage practices. This review encompasses comparisons of remote sensing techniques with more conventional methods for surveying and estimating tillage status, applications of remote sensing technologies, data processing and analysis, validation and field data collection, impacts of terrain, spectral and spatial resolution, timing and temporal detail, and prospects of future instruments. (C) 2014 Elsevier B.V. All rights reserved.

Accurate, site-specific tillage information forms an important dimension for development of effective agricultural management practices and policies. Landsat Thematic Mapper (TM) imagery provides the opportunity for systematic mapping of tillage practices via crop residue (plant litter or senescent or nonphotosynthetic vegetation) cover (CRC) estimation at broad scales because of its repetitive coverage of the Earth's land areas over several decades. This study evaluated the effectiveness of a multitemporal approach using the minimum values of Normalized Difference Tillage Index (minNDTI) for assessing CRC at multiple locations over several years. Local models, were generated for each dataset. In addition, we tested the feasibility of a regional model in mapping CRC. Results show that the minNDTI method was able to estimate CRC, and a regional model is possible. We found that in addition to the known impact of emergent green vegetation, soil moisture and organic carbon (C) can also confound the NDTI signal, thereby underestimating CRC for low-lying wet and dark areas. Accuracy of the minNDTI technique is comparable to the hyperspectral Cellulose Absorption Index (CAI) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Shortwave Infrared Normalized Difference Residue Index (SINDRI) for tillage classification. The minNDTI technique is currently the best for monitoring CRC and tillage practices from space, opening the door for generating field-level tillage maps at broad spatial and temporal scales.

Quantification of dry plant matter (crop residue, senesced foliage, non-photosynthetic vegetation, or plant litter) surface cover (f R) is important for assessing agricultural tillage practices, carbon sequestration, rangeland health, or brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that can remotely estimate f R. CAI and SINDRI utilize three and two spectral bands, respectively, so SINDRI is expected to be less expensive to implement in future satellite sensors. We assessed the contrast of CAI and SINDRI with respect to soil reflectance spectra. Estimating f R with CAI is possible for all soils. However, a number of soil samples had positive SINDRI values due to various soil minerals, such as gibbsite and antigorite, which would be interpreted as high f R, and could limit its usefulness in some areas. Therefore, SINDRI is less applicable for estimating f R, even with reduced implementation costs.

Overall impact of GRACEnet management strategies for enhancing soil C sequestration and reducing greenhouse gases emissions requires extending results from small plot or field experiments to regional and national scales. This spatial scaling task is not trivial because the mechanisms controlling carbon, water, and energy exchanges are nonlinear and interact with each other. Remote sensing offers the only practical method to account for the spatial and temporal variability inherent across agricultural landscapes. In this chapter, the fundamental spectral properties of vegetation and soils are reviewed and potential synergies of in situ and remotely sensed measurements for providing frequent, spatially explicit information about agricultural landscapes are examined. Data fusion and assimilation techniques for merging data acquired at different spatial and temporal resolutions and techniques for creating synthetic datasets with high spatial and temporal resolutions are discussed. The next step for verifying GRACEnet practices will be to link process models with these enhanced datasets to reliably describe ecosystem functions at various scales.

Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes.

The quantification of surficial crop residue (nonphotosynthetic vegetation) cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that have shown promise for remote estimation of crop residue cover. CAI and SINDRI utilize three and two spectral bands, respectively, rendering the latter less expensive to implement in future satellite sensors. This study shows that while CAI always contrasts well among soils, crop residues, and live vegetation, this is not always the case for SINDRI. A small number of surficial soil samples had positive SINDRI values that have reduced contrasts among crop residues. Some of these soils were biased by SINDRI-positive component minerals. As such, SINDRI is less applicable for remote crop residue cover estimation, even with reduced implementation costs.

Physically-based spectral indices that detect absorption features associated with cellulose and lignin are linearly related to crop residue cover, a key indicator of soil tillage intensity, and are robust across diverse agricultural landscapes.

Conservation tillage practices often leave appreciable amounts of crop residues on soil surfaces after harvesting and generally improve soil structure, enhance soil organic C (SOC) content, and reduce soil erosion. Remote sensing methods have shown great promise in efficiently estimating crop residue cover, and thus inferring soil tillage intensity. Furthermore, these tillage intensity estimates can be used in soil C models. Reflectance spectra of more than 4200 soils and 80 crop residues were measured in the laboratory across the 350‐ to 2500‐nm wavelength region. Six remote sensing spectral indices were used to estimate crop residue cover: the Cellulose Absorption Index (CAI), the Lignin–Cellulose Absorption Index (LCA), the Normalized Difference Tillage Index (NDTI), the Normalized Difference Senescent Vegetation Index (NDSVI), and the Normalized Difference Indices 5 and 7 (NDI5 and NDI7, respectively). Soil mineralogy and SOC affected these spectral indices for crop residue cover more than soil taxonomic order, which generally had little effect on spectral reflectance. The values of the spectral indices for soils were similar within Land Resource Regions and, specifically, for Major Land Resource Areas. The CAI showed the best separation between soils and residues, followed by LCA and NDTI. Although NDSVI, NDI5, and NDI7 had significant overlaps between soil and residue index values, assessments of crop residue cover classes may be possible with local calibrations. Future satellite sensors should include appropriate bands for assessing crop residue and nonphotosynthetic vegetation.

Unlike traditional ground-based methodology, remote sensing allows for the rapid estimation of crop residue cover (fR). While the Cellulose Absorption Index (CAI) is ideal for fR estimation, a new index, the Shortwave Infrared Normalized Difference Residue Index (SINDRI), utilizing ASTER bands 6 and 7, is proposed for future multispectral sensors and would be less costly to implement. SINDRI performed almost as well as CAI and better than other indices at five locations in the USA on multiple dates. A minimal upgrade from one broad band to two narrow bands would provide fR data for carbon cycle modeling and tillage verification.