scholarly journals A Review of Crop Water Stress Assessment Using Remote Sensing

2021 ◽  
Vol 13 (20) ◽  
pp. 4155
Author(s):  
Uzair Ahmad ◽  
Arturo Alvino ◽  
Stefano Marino
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Land Surface ◽  
Crop Production ◽  
Crop Water ◽  
Ecological Zones ◽  
Sensing Systems ◽  
Equivalent Water Thickness ◽  
Remote Sensing Systems ◽  
Crop Water Stress

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

A novel agricultural drought monitoring framework using remote sensing products

2021 ◽  
Author(s):  
Trupti Satapathy ◽  
Meenu Ramadas ◽  
Jörg Dietrich
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Land Surface ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Crop Water ◽  
Vegetation Health ◽  
Crop Water Stress

<p>Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.</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.

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

Changes in the baseline of the Crop Water Stress Index for lucerne (Medicago sativa) over 3 years

1991 ◽  
Vol 116 (1) ◽  
pp. 63-66 ◽  
Author(s):  
E. A. Rechel ◽  
W. R. DeTar ◽  
D. Ballard
Keyword(s):  
Water Stress ◽  
Crop Production ◽  
Vapour Pressure Deficit ◽  
Water Shortage ◽  
Stress Index ◽  
First Year ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

SUMMARYThe ability to detect and measure water stress accurately is critical for optimizing crop production. The Crop Water Stress Index (CWSI), the linear relationship of the difference between foliage and air temperatures as a function of the air vapour pressure deficit, is one widely used method. Under well-watered conditions, a ‘baseline’ is derived that is crop specific and presumed fairly constant, despite differences in development and physiology. This study reports changes in the baseline of the CWSI for lucerne crops not subjected to water shortage over 3 years. Studies of lucerne in California from April 1986 to October 1988 used the CWSI to plan irrigations. It was necessary to re-establish the baseline periodically throughout the experiment. In the first year it was similar to that reported in the literature, but in the second year it had a statistically significant steeper slope and higher intercept. In the third year, the regression equation was similar to that in the first year. The changes in the baseline are thought to be a result of crop age rather than year-to-year weather fluctuations. The baseline needs to be determined periodically as the crop matures, to ensure accurate interpretation of 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.

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 Crop water stress maps for an entire growing season from visible and thermal UAV imagery

2016 ◽  
Vol 13 (24) ◽  
pp. 6545-6563 ◽  
Author(s):  
Helene Hoffmann ◽  
Rasmus Jensen ◽  
Anton Thomsen ◽  
Hector Nieto ◽  
Jesper Rasmussen ◽  
...  
Keyword(s):  
Water Stress ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Growing Season ◽  
Color Images ◽  
Growth Stages ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Crop Water Stress

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red–green–blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green–red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

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.

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