Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands

Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phenology and physiology is important in land-atmosphere carbon models being used to predict these exchanges. To better estimate time-varying exchanges of carbon, water, and energy of croplands using the Simple Biosphere (SiB) model, we developed crop-specific phenology models and coupled them to SiB. The coupled SiB-phenology model (SiBcrop) replaces remotely-sensed NDVI information, on which SiB originally relied for deriving Leaf Area Index (LAI) and the fraction of Photosynthetically Active Radiation (fPAR) for estimating carbon dynamics. The use of the new phenology scheme within SiB substantially improved the prediction of LAI and carbon fluxes for maize, soybean, and wheat crops, as compared with the observed data at several AmeriFlux eddy covariance flux tower sites in the US mid continent region. SiBcrop better predicted the onset and end of the growing season, harvest, interannual variability associated with crop rotation, day time carbon uptake (especially for maize) and day to day variability in carbon exchange. Biomass predicted by SiBcrop had good agreement with the observed biomass at field sites. In the future, we will predict fine resolution regional scale carbon and other exchanges by coupling SiBcrop with RAMS (the Regional Atmospheric Modeling System)

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination

Simple Biosphere Model version 4.2 (SiB4) technical description

The Simple Biosphere Model (SiB4) is a mechanistic, prognostic land surface model that integrates heterogeneous land cover, environmentally responsive prognostic phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. By combining biogeochemical, biophysical, and phenological processes, SiB4 predicts vegetation and soil moisture states, land surface energy and water budgets, and the terrestrial carbon cycle. Rather than relying on satellite data, SiB4 fully simulates the terrestrial carbon cycle by using the carbon fluxes to determine the above and belowground biomass, which in turn feeds back to impact carbon assimilation and respiration. Every 10-minute time-step, SiB4 computes the albedo, radiation budget, hydrological cycle, layered temperatures, and soil moisture, as well as the resulting energy exchanges, moisture fluxes, carbon fluxes, and carbon pool transfers. Photosynthesis depends directly on environmental factors (humidity, moisture, and temperature) and aboveground biomass; and carbon uptake is determined using enzyme kinetics and stomatal physiology. Carbon release and pool transfers depend on assimilation rate, day length, moisture, phenology, temperature, and pool size. Once daily the net assimilated carbon is allocated to the live pools depending on phenology, soil moisture, and temperature; all live and dead pools are updated, including any necessary carbon transfers between pools; and the land surface state and related properties are revised. The new LAI and pools are then used for sub-hourly assimilation and respiration, completing the carbon cycle and providing self-consistent predicted vegetation states, soil hydrology, carbon pools, and land-atmosphere exchanges

Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop

Solar-induced Fluorescence (SIF) has an advantage over greenness-based Vegetation Indices in detecting drought. This advantage is the mechanistic coupling between SIF and Gross Primary Productivity (GPP). Under water stress, SIF tends to decrease with photosynthesis, due to an increase in non-photochemical quenching (NPQ), resulting in rapid and/or sustained reductions in the fluorescence quantum efficiency (phi F). Water stress also affects vegetation structure via highly dynamic changes in leaf angular distributions (LAD) or slower changes in leaf area index (LAI). Critically, these responses are entangled in space and time and their relative contribution to SIF, or to the coupling between SIF and GPP, is unclear. In this study, we quantify the relative effect of structural and photosynthetic dynamics on the diurnal and spatial variation of canopy SIF in a potato crop in response to a replicated paired-plot water stress experiment. We measured SIF using two platforms: a hydraulic lift and an Unmanned Aerial Vehicle (UAV) to capture temporal and spatial variation, respectively. LAD parameters were estimated from point clouds and photographic data and used to assess structural dynamics. Leaf phi F estimated from PAM fluorescence measurements were used to represent variations in photosynthetic regulation. We also measured foliar pigments, operating quantum yield of photosystem II (PSII), photosynthetic gas exchange, stomatal conductance and LAI. We used a radiative transfer model (SCOPE) to provide a means of decoupling structural and photosynthetic factors across the diurnal and spatial domains. The results demonstrate that diurnal variation in SIF is driven by photosynthetic and structural dynamics. The influence of phi F was prominent in the diurnal SIF response to water stress, with reduced fluorescence efficiencies in stressed plants. Structural factors dominated the spatial response of SIF to water stress over and above phi F. The results showed that the relationship between SIF and GPP is maintained in response to water stress where adjustments in NPQ and leaf angle co-operate to enhance the correlation between SIF and GPP. This study points to the complexity of interpreting and modelling the spatiotemporal connection between SIF and GPP which requires simultaneous knowledge of vegetation structural and photosynthetic dynamics.Peer reviewe

Study on Regional Responses of Pan-Arctic Terrestrial Ecosystems to Recent Climate Variability Using Satellite Remote Sensing

I applied a satellite remote sensing based production efficiency model (PEM) using an integrated AVHRR and MODIS FPAR/LAI time series with a regionally corrected NCEP/NCAR reanalysis surface meteorology and NASA/GEWEX shortwave solar radiation inputs to assess annual terrestrial net primary productivity (NPP) for the pan-Arctic basin and Alaska from 1983 to 2005. I developed a satellite remote sensing based evapotranspiration (ET) algorithm using GIMMS NDVI with the above meteorology inputs to assess spatial patterns and temporal trends in ET over the pan-Arctic region. I then analyzed associated changes in the regional water balance defined as the difference between precipitation (P) and ET. I finally analyzed the effects of regional climate oscillations on vegetation productivity and the regional water balance. The results show that low temperature constraints on Boreal-Arctic NPP are decreasing by 0.43% per year ( P \u3c 0.001), whereas a positive trend in vegetation moisture constraints of 0.49% per year ( P = 0.04) are offsetting the potential benefits of longer growing seasons and contributing to recent drought related disturbances in NPP. The PEM simulations of NPP seasonality, annual anomalies and trends are similar to stand inventory network measurements of boreal aspen stem growth ( r = 0.56; P = 0.007) and atmospheric CO2 measurement based estimates of the timing of growing season onset (r = 0.78; P \u3c 0.001). The simulated monthly ET results agree well (RMSE = 8.3 mm month-1; R2 = 0.89) with tower measurements for regionally dominant land cover types. Generally positive trends in ET, precipitation and available river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying. Increasing water deficits occurred in some boreal and temperate grassland regions, which agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Climate oscillations including Arctic Oscillation and Pacific Decadal Oscillation influence NPP by regulating seasonal patterns of low temperature and moisture constraints to photosynthesis. The pan-Arctic water balance is changing in complex ways in response to climate change and variability, with direct linkages to terrestrial carbon and energy cycles. Consequently, drought induced NPP decreases may become more frequent and widespread, though the occurrence and severity of drought events will depend on future water cycle patterns