Defining irrigation thresholds in remote sensing-based decision support systems: a review of crop models mechanistic descriptions of crop water stress

2020 ◽  
Author(s):  
Massimo Tolomio ◽  
Raffaele Casa
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Water Stress ◽  
Biomass Growth ◽  
Hydrological Models ◽  
Crop Water ◽  
Stress Indices ◽  
Crop Models ◽  
Wilting Point ◽  
Crop Water Stress

<p>Irrigation management decision support systems based on remote sensing and hydrological models need to find a balance between simplicity and accuracy in the definition of crop water stress thresholds when irrigation should be triggered. Among the most widely used crop models, which synthesize current mechanistic knowledge of crop water stress processes, there is a wide range of complexity that is worth exploring in order to improve the formalisms of current hydrological models.</p><p>In the present work, some of the most widely used crop models (chosen among those freely available and well documented) were examined in their description of crop water stress processes and irrigation thresholds definition. They are: APSIM, AQUACROP, CROPSYST, CROPWAT, DAISY, DSSAT, EPIC, STICS and WOFOST. Model manuals and scientific papers were reviewed to identify differences and similarities in the water stress functions related to crop growth.</p><p>A strict categorization of the model features is inappropriate, since the functions utilized are always at least slightly different and the models may focus on different features of the agroecosystem. Nevertheless, major similarities and differences among the models were found:</p><ol><li><em>The function of biomass growth.</em> AQUACROP and CROPWAT (both developed by FAO) are water-driven models (growth is directly related to transpiration). DAISY, DSSAT, EPIC, STICS and WOFOST are radiation-driven models (growth is related to radiation). APSIM and CROPSYST calculate both water- and radiation-driven biomass and keep the most limiting of these.</li> <li><em>The main variable used to calculate water stress indices.</em> AQUACROP, CROPWAT and WOFOST use stress coefficients that depend directly on the depletion status of plant available water (difference between field capacity and wilting point). CROPSYST, DAISY, DSSAT and EPIC calculate water stress on the ratio between actual transpiration (limited by roots and soil characteristics) and potential transpiration (weather-dependent). APSIM uses both approaches, depending on the specific crop and growth process targeted. STICS expresses the transpiration rate as a function of the available water content (in m<sup>3</sup>/m<sup>3</sup> above wilting point), and from this it calculates water stress indices.</li> <li><em>The influence of water stress indices on vegetative growth.</em> Water stress in CROPWAT, DAISY and WOFOST affects biomass growth, whereas in APSIM, AQUACROP, CROPSYST, DSSAT, EPIC and STICS multiple indices affect biomass growth and leaf expansion in different ways. The rationale behind the last approach is that as soil water uptake becomes more difficult, water stress slows down cells division and expansion (reducing the leaf expansion rate) before photosynthesis is reduced by stomatal closure.</li> </ol><p>The models were then calibrated for the maize and tomato crops using field and remote sensing data on crop yield, soil moisture, evapotranspiration (ET) and leaf area index (LAI), for two locations, respectively in Northern and Southern Italy (Calcinato and Capitanata). Simulations were then carried out and compared in terms of the optimal irrigation amounts calculated by the different models and predicted yields.</p>

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

Assessing canopy temperature-based water stress indices for soybeans under subhumid conditions

2020 ◽  
Author(s):  
Angela Morales Santos ◽  
Reinhard Nolz
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Water Status ◽  
Canopy Temperature ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Stress Indices ◽  
Soil Water Status ◽  
Crop Water Stress

<p>Sustainable irrigation water management is expected to accurately meet crop water requirements in order to avoid stress and, consequently, yield reduction, and at the same time avoid losses of water and nutrients due to deep percolation and leaching. Sensors to monitor soil water status and plant water status (in terms of canopy temperature) can help planning irrigation with respect to time and amounts accordingly. The presented study aimed at quantifying and comparing crop water stress of soybeans irrigated by means of different irrigation systems under subhumid conditions.</p><p>The study site was located in Obersiebenbrunn, Lower Austria, about 30 km east of Vienna. The region is characterized by a mean temperature of 10.5°C with increasing trend due to climate change and mean annual precipitation of 550 mm. The investigations covered the vegetation period of soybean in 2018, from planting in April to harvest in September. Measurement data included precipitation, air temperature, relative humidity and wind velocity. The experimental field of 120x120 m<sup>2</sup> has been divided into four sub-areas: a plot of 14x120 m<sup>2</sup> with drip irrigation (DI), 14x120 m<sup>2</sup> without irrigation (NI), 36x120 m<sup>2</sup> with sprinkler irrigation (SI), and 56x120 m<sup>2</sup> irrigated with a hose reel boom with nozzles (BI). A total of 128, 187 and 114 mm of water were applied in three irrigation events in the plots DI, SI and BI, respectively. Soil water content was monitored in 10 cm depth (HydraProbe, Stevens Water) and matric potential was monitored in 20, 40 and 60 cm depth (Watermark, Irrometer). Canopy temperature was measured every 15 minutes using infrared thermometers (IRT; SI-411, Apogee Instruments). The IRTs were installed with an inclination of 45° at 1.8 m height above ground. Canopy temperature-based water stress indices for irrigation scheduling have been successfully applied in arid environments, but their use is limited in humid areas due to low vapor pressure deficit (VPD). To quantify stress in our study, the Crop Water Stress Index (CWSI) was calculated for each plot and compared to the index resulting from the Degrees Above Canopy Threshold (DACT) method. Unlike the CWSI, the DACT method does not consider VPD to provide a stress index nor requires clear sky conditions. The purpose of the comparison was to revise an alternative method to the CWSI that can be applied in a humid environment.</p><p>CWSI behaved similar for the four sub-areas. As expected, CWSI ≥ 1 during dry periods (representing severe stress) and it decreased considerably after precipitation or irrigation (representing no stress). The plot with overall lower stress was BI, producing the highest yield of the four plots. Results show that DACT may be a more suitable index since all it requires is canopy temperature values and has strong relationship with soil water measurements. Nevertheless, attention must be paid when defining canopy temperature thresholds. Further investigations include the development and test of a decision support system for irrigation scheduling combining both, plant-based and soil water status indicators for water use efficiency analysis.</p>

Crop Water Stress Detection Using Remote Sensing

2005 ◽  
Author(s):  
M. Susan Moran ◽  
Pablo J. Zarco-Tejada ◽  
Thomas R. Clarke
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

Modeling of reference temperatures for calculating crop water stress indices from infrared thermography

2020 ◽  
Vol 233 ◽  
pp. 106070 ◽  
Author(s):  
Magalie Poirier-Pocovi ◽  
Astrid Volder ◽  
Brian N. Bailey
Keyword(s):  
Water Stress ◽  
Infrared Thermography ◽  
Crop Water ◽  
Stress Indices ◽  
Crop Water Stress

scholarly journals Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

2019 ◽  
Vol 11 (10) ◽  
pp. 1240 ◽  
Author(s):  
Max Gerhards ◽  
Martin Schlerf ◽  
Kaniska Mallick ◽  
Thomas Udelhoven
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Water Status ◽  
Thermal Infrared ◽  
Hyperspectral Remote Sensing ◽  
Plant Responses ◽  
Plant Water ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

Remote sensing and machine learning for crop water stress determination in various crops: a critical review

2020 ◽  
Vol 21 (5) ◽  
pp. 1121-1155 ◽  
Author(s):  
Shyamal S. Virnodkar ◽  
Vinod K. Pachghare ◽  
V. C. Patil ◽  
Sunil Kumar Jha
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Stress ◽  
Critical Review ◽  
Stress Determination ◽  
Crop Water ◽  
Crop Water Stress
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