Contribution of remote sensing and auxiliary variables in the study of the evolution of periods of droughts

2021 ◽  
Author(s):  
Nesrine Farhani ◽  
Gilles Boulet ◽  
Julie Carreau ◽  
Zeineb Kassouk ◽  
Michel Le Page ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Surface Temperature ◽  
Global Radiation ◽  
Meteorological Variables ◽  
Stress Index ◽  
Plant Water ◽  
Stochastic Weather Generator ◽  
Vegetation Fraction ◽  
Observation Period

<p>In semi-arid areas, plant water use and plant water stress can be derived over large<br>areas from remotely sensed evapotranspiration estimates. Those can help us to monitor the<br>impact of drought on the agro- and ecosystems. Both variables can be simulated by a dual<br>source energy balance model that relies on meteorological variables (air temperature, relative<br>humidity, wind speed and global radiation) and remote sensing data (surface temperature,<br>NDVI, albedo and LAI). Surface temperature acquired in the Thermal InfraRed (TIR) domain<br>is particularly informative for monitoring agrosystem health and adjusting irrigation<br>requirements. However, available meteorological observations period may often be<br>insufficient to account for the variability present in the study area. Statistical downscaling<br>methods applied to reanalysis data can serve to generate surrogate series of meteorological<br>variables that either fill the gaps in the observation period or extend the observation period in<br>the past. For this aim, a stochastic weather generator (SWG) is adapted in order to compute<br>temporal extension of multiple meteorological variables. This surrogate series is then used to<br>constrain the dual-source model Soil Plant Atmosphere and Remote Evapotranspiration<br>(SPARSE). Stress index anomalies retrieved from SPARSE are then compared to anomalies in<br>other wave lengths in order to assess their capacity to detect incipient water stress and early<br>droughts at the kilometer resolution. Those are the root zone soil moisture at low resolution<br>derived from the microwave domain, and active vegetation fraction cover deduced from<br>NDVI time series.</p>

scholarly journals Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms

2018 ◽  
Vol 10 (7) ◽  
pp. 1139 ◽  
Author(s):  
Max Gerhards ◽  
Martin Schlerf ◽  
Uwe Rascher ◽  
Thomas Udelhoven ◽  
Radoslaw Juszczak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Surface Temperature ◽  
Early Morning ◽  
Stress Index ◽  
Diurnal Changes ◽  
Stress Symptoms ◽  
Highly Sensitive ◽  
Normalised Difference Vegetation Index

High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.

On the use of different approaches based on photochemical reflectance index and surface temperature to monitor the water status of winter wheat in semi-arid regions

2020 ◽  
Author(s):  
Zoubair Rafi ◽  
Valérie Le Dantec ◽  
Olivier Merlin ◽  
Said Khabba ◽  
Patrick Mordelet ◽  
...  
Keyword(s):  
Water Stress ◽  
Winter Wheat ◽  
Surface Temperature ◽  
Water Status ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Temperature Index ◽  
Water Stress Index ◽  
Semi Arid

&lt;p&gt;Agriculture is considered to be the human activity that consumes the most mobilized water on a global scale. However, crops planted in semi-arid areas regularly face periods of moderate to extreme water stress. Such water stress periods have a considerable impact on the seasonal yield of these crops. In order to participate in a more rational irrigation water management, monitoring of the rapid changes in plant water status is necessary. For this purpose, the combination of two different wavelength ranges will be explored : an index based on Xanthophyll cycle (Photochemical Reflectance Index, PRI) and a commonly-used index from thermal infrared spectral range (LST). An experiment on winter wheat was carried out over two agricultural campaigns (2016 to 2018) in the Haouz basin, which is located in the Marrakech region, to better assimilate the temporal dynamics of PRI and surface temperature. In this study, four different approaches are proposed to study the functioning of wheat : 1- an approach based on solar angle to remove the structure effect (PRI&lt;sub&gt;0&lt;/sub&gt;) from the PRI signal and to derive a water stress index PRI&lt;sub&gt;j&lt;/sub&gt;, 2- an approach based on global radiation (R&lt;sub&gt;g&lt;/sub&gt;) to extrapolate a theoretical PRI (PRI&lt;sub&gt;th&lt;/sub&gt;) for R&lt;sub&gt;g&lt;/sub&gt; equal to zero and to calculate a water stress index PRI&lt;sub&gt;lin&lt;/sub&gt;, 3- an approach that determines an optimal PRI (PRI&lt;sub&gt;pot&lt;/sub&gt;) on the basis of the available water content (AWC) criterion in order to derive a stress index I-PRI and 4- an energy balance approach to extract dry and wet surface temperatures in order to establish a normalized surface temperature index (T&lt;sub&gt;norm&lt;/sub&gt;). The results of this work show a strong correlation between the PRI&lt;sub&gt;0&lt;/sub&gt; and the Leaf Area Index with a coefficient of determination equal to 0.92, indicating that it is possible to isolate the structural effects of wheat on the PRI signal. In addition, over the range of variation in AWC, a significant correlation with PRI&lt;sub&gt;j&lt;/sub&gt;, PRI&lt;sub&gt;jlin&lt;/sub&gt; and I-PRI was observed with coefficients of determination of 0.71, 0.42 and 0.24, respectively. In contrast to the T&lt;sub&gt;norm&lt;/sub&gt;, which varies only for values of AWC below 30%, a coefficient of determination of 0.22 is obtained. Finally, the PRI allows us to acquire early and complete information on the response of wheat to change in AWC as opposed to the surface temperature index, revealing the potential of the PRI to monitor the water status of plants and their responses to changing environmental conditions.&lt;/p&gt;

scholarly journals Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

2021 ◽  
Vol 13 (14) ◽  
pp. 2775
Author(s):  
Suyoung Park ◽  
Dongryeol Ryu ◽  
Sigfredo Fuentes ◽  
Hoam Chung ◽  
Mark O’Connell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Data Acquisition ◽  
Irrigation Scheduling ◽  
Physiological Parameters ◽  
Acquisition Time ◽  
Plant Water ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Plant Water Stress

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

scholarly journals Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

2018 ◽  
Vol 380 ◽  
pp. 17-22 ◽  
Author(s):  
Gilles Boulet ◽  
Emilie Delogu ◽  
Sameh Saadi ◽  
Wafa Chebbi ◽  
Albert Olioso ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Stress Levels ◽  
Dual Source ◽  
Energy Balance Models

Abstract. EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.

scholarly journals The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

2015 ◽  
Vol 19 (11) ◽  
pp. 4653-4672 ◽  
Author(s):  
G. Boulet ◽  
B. Mougenot ◽  
J.-P. Lhomme ◽  
P. Fanise ◽  
Z. Lili-Chabaane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Series Resistance ◽  
Water Status ◽  
Soil Evaporation ◽  
Stress Levels ◽  
Dual Source ◽  
Rainfed Wheat

Abstract. Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution.

AIRBORNE REMOTE SENSING TO DETECT PLANT WATER STRESS IN FULL CANOPY COTTON

2006 ◽  
Vol 49 (3) ◽  
pp. 655-665 ◽  
Author(s):  
W. R. DeTar ◽  
J. V. Penner ◽  
H. A. Funk
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Plant Water ◽  
Airborne Remote Sensing ◽  
Plant Water Stress

Thermal remote sensing of plant water stress in natural ecosystems

2020 ◽  
Vol 476 ◽  
pp. 118433
Author(s):  
Na Liu ◽  
Zijuan Deng ◽  
Hailong Wang ◽  
Zidong Luo ◽  
Hugo A. Gutiérrez-Jurado ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Plant Water ◽  
Natural Ecosystems ◽  
Thermal Remote Sensing ◽  
Plant Water Stress

scholarly journals CROP WATER STRESS INDEX (CWSI) ESTIMATION USING MODIS DATA

2010 ◽  
Vol 3 (-) ◽  
Author(s):  
M.Rokhis Khomarudin ◽  
Parwati Sofan
Keyword(s):  
Water Stress ◽  
Surface Temperature ◽  
Land Surface ◽  
Crop Productivity ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Modis Land Surface Temperature ◽  
Water Stress Index ◽  
Crop Water Stress

Crop Water Stress Index (CWSI) is an index which is used to explain the amount of crop water defisiency based on canopy surface temperature. Many researches of CWSI have been done for arranging irigation water system in several crops at different areas. Beside its application in irigation system, CWSI is also known as one of parameters that can influence crop productivity. Regarding the above explanation, it is implied that CWSI is important for monitoring crop drought, arranging irigation water, and estimating crop productivity. This research is proposed to estimate CWSI using MODIS (Moderate Resolution Imaging Spectroradiometer) data which is related to Normalized Difference Vegetation Index (NDVI) and Soil Moisture Storage (ST) in paddy field. The interest area is in East Java wich is the driest area in Java Island. MODIS land surface temperature is used to estimate CWSI, while MODIS reflectance 500 m is used to estimate NDVI. They were downloaded from NASA website. Data period was from June 15th to June 30 th, 2004. Based on the correlation between NDVI and CWSI, we can estimate NDVI value when paddy water stress occured. The result showed that the largest paddy area in East Java which has high water stress is located in Bojonegoro District. The water stress areain Bojonegoro Distric increase from June 15th to June 30th, 2004. The high to medium water stress level in East Java were predicted as bare land. The CWSI has negative correlation with NDVI and ST. The CWSI 0.6 are obtained in NDVI 0.5 with ST less than 50 percent. This showed that the paddy water stress began at NDVI 0.5 and ST 50 percent. Coefficient of correlation between CWSI and NDVI is 0.58, while CWSI and ST is 0.71. The correlation model between CWSI, NDVI and ST is statistically significant. Keywords: CWSI,NDVI, ST, MODIS Land Surface Temperature, Water Stress.

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