How to account for irrigation withdrawals in a watershed model

In agricultural areas, the downstream flow can be highly influenced by human activities during low-flow periods, especially during dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in terms of their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for growth, whereas the second relies on high-resolution data from the Sentinel-2 satellite to monitor plant growth. Both are applied at the plot scale in a watershed of 800 km2 that is characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However, both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed us to simulate the downstream flow with a good daily accuracy, especially during low-flow periods.</p

A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period

The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1&thinsp;km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5&thinsp;km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.</p

A proposed methodology for the correction of the Leaf Area Index measured with a ceptometer for pinus and eucalyptus forests = Proposta de uma methodologia para a correcao do indice de area foliar medido pelo ceptometro em provoamentos de pinus e eucalyptus

Leaf area index (LAI) is an important parameter controlling many biological and physiological processes associated with vegetation on the Earth's surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle and rainfall interception. LAI can be measured indirectly by sunfleck ceptometers in an easy and non-destructive way but this practical methodology tends to underestimated when measured by these instruments. Trying to correct this underestimation, some previous studies heave proposed the multiplication of the observed LAI value by a constant correction factor. The assumption of this work is LAI obtained from the allometric equations are not so problematic and can be used as a reference LAI to develop a new methodology to correct the ceptometer one. This new methodology indicates that the bias (the difference between the ceptometer and the reference LAI) is estimated as a function of the basal area per unit ground area and that bias is summed to the measured value. This study has proved that while the measured Pinus LAI needs a correction, there is no need for that correction for the Eucalyptus LAI. However, even for this last specie the proposed methodology gives closer estimations to the real LAI values

Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images.[Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health.[Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes.The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.Peer reviewe

Modeling Radiative Transfer in Heterogeneous 3-D Vegetation Canopies

The DART (discrete anisotropic radiative transfer) model simulates radiative transfer in heterogeneous 3-D scenes that may comprise different landscape features; i.e., leaves, grass, trunks, water, soil. The scene is divided into a rectangular cell matrix, i.e., building block for simulating larger scenes. Cells are parallelipipedic. Their optical properties are represented by individual scattering phase functions that are directly input into the model or are computed with optical and structural characteristics of elements within the cell. Radiation scattering and propagation are simulated with the exact kernel and discrete ordinate approaches; any set of discrete direction can be selected. In addition to topography and hot spot, leaf specular and first-order polarization mechanisms are modeled. Two major iterative steps are distinguished: 1) Cell illumination with direct sun radiation: Within cell multiple scattering is accurately simulated. 2) Interception and scattering of previously scattered radiation: Atmospheric radiation, possibly anisotropic, is input at this stage. Multiple scattering is stored as spherical harmonics expansions, for reducing computer memory constraints. The model iterates on step 2, for all cells, and stops with the energetic equilibrium. Two simple accelerating techniques can be used: 1) Gauss Seidel method, i.e., simulation of scattering with radiation already scattered at the iteration stage, and (2) decrease of the spherical harmonics expansion order with the iteration order. Moreover, convergence towards the energetic equilibrium is accelerated with an exponential fitting technique. This model predicts the bidirectional reflectance distribution function of 3-19 canopies. Radiation components associated with leaf volume and surface mechanisms are distinguished. It gives also the radiation regime within canopies, for further determination of 3-D photosynthesis rates and primary production. Accurate modeling of multiple scattering within cells, combined with the fact that cells can have different x,y,z dimensions, is well adapted to remote sensing based studies, i.e., scenes with large dimensions. The model was successfully tested with homogeneous covers. Preliminary comparisons of simulated reflectance images with remotely acquired spectral images of a 3-D heterogeneous forest cover stressed the usefulness of the DART model for conducting studies with remotely acquired information

Land surface remote sensing in agriculture and forest

Agriculture brings with it a number of issues: agricultural production and food security, water and soil resource conservation, limiting the impact of farming on the quality of our environment (water, air, soil). In the context of global changes and the challenges they pose for agriculture sustainability, our ability to characterize how croplands function in terms of water, carbon and particle fluxes is crucial. Developments of agro-ecosystems modeling, including their interactions with the atmosphere and the anthropogenic factors are valuable tools for progress in this direction. Remote sensing, with the high variety of spectral ranges and the fine spatial and temporal resolution currently available, is a tool of great value for various applications in agriculture. The availability of robust inverse methods that allow surface biophysical variables to be assessed, combined with modeling approaches, makes it a high performance tool. The major contributions of remote sensing include : - its ability to cover large stretches of land and to provide information on the various land uses and practices generated by agriculture. These uses and practices are important to know, both for census purposes (agricultural statistics, agri-environmental monitoring, etc.) and for modeling the behavior of agro-hydrosystems ; - providing frequent variables characterizing soil and vegetation properties that allows us to monitor the status of crops, their production potential, their irrigation requirements. This monitoring is a highly strategic issue, both for forecasting purposes, food security and good resource management ; - the possibility, from the same information, of assessing the contribution of agricultural lands to net emissions of CO2 and other greenhouse gases (GHGs) ; this assessment is essential for proposing alternative agricultural scenarios for mitigating the contribution of croplands to climate change ; - thanks to the fine spatial and temporal resolution of information, the possibility of providing a decision support for farming activities according to the intra-field variability (precision farming). This dimension represents an important lever for enabling agricultural systems to achieve better efficiency and economical use of inputs for an agriculture that respects the environment