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Special Issue "Remote Sensing for Agrometeorology"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 13923

Special Issue Editor

Prof. Dr. Nicolas R. Dalezios
E-Mail Website
Guest Editor
Department of Civil Engineering, University of Thessaly, Pedion Areos, 38334 Volos, Greece
Interests: agrometeorological and hydrological modeling; stochastic and systems hydrology; environmental remote sensing; environmental hazards risk management and climate variability/change: impacts-mitigation-adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that agrometeorology is a multi-disciplinary scientific field, which is currently receiving significant attention mainly due to climate change, among other reasons. Agrometeorology deals with meteorological, hydrological, edaphological, and biological factors and parameters, which affect agricultural production, and examines the inter-relationship between agriculture, forestry, and the environment. Agrometeorology is a horizontal science, which applies atmospheric and soil physics to agriculture and combines physical and biological sciences constituting a significant link between them. Similarly, at present, remote sensing is a very fast evolving scientific and technological field with steadily increasing reliability and more new systems every year with continuously improving temporal and spatial resolution.

The aim of this Special Issue is to foster advances in remote sensing science and technology for a range of practical applications and research investigations in agrometeorology. I would like to encourage both theoretical and applied research contributions, furthering knowledge on the use of this remote sensing science and technology in all disciplines of contemporary agrometeorology. Such contributions can be focused on various aspects, including, but not limited to, active and passive remote sensing data and methods (e.g., satellites, weather radar, SAR, UAV, sensors), applications in environmental hazards affecting agriculture, agrometeorological simulation and modeling, decision support systems in agrometeorology, climate change: impact-mitigation-adaptation, precision agriculture, agroclimatic classification, software tool development for data collection and processing, as well as their applications.

Prof. Nicolas R. Dalezios
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Remote sensing for agrometeorological simulation and modeling.
  • Remote sensing for monitoring and forecasting of agricultural production.
  • Remote sensing for agrohydrological simulation and modeling.
  • Weather radar applications in agrometeorology, agrohydrology, and agriculture.
  • Weather modification (hail suppression, precipitation enhancement) for agriculture.
  • Remote sensing for hydrometeorological hazards in agriculture (floods and excess rain, hail, storms, droughts, desertification).
  • Remote sensing for biophysical hazards in agriculture (frost, heatwaves, wildfires, biohazards).
  • Remote sensing for micrometeorology (e.g., canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases).
  • Remote sensing for biometeorology (e.g., the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology).
  • Remote sensing for agrometeorological plant protection.
  • Remote sensing for aerobiology (e.g., pollen dispersion, spores, insects, pesticides).
  • Remote sensing for climate change and agriculture: impact-mitigation-adaptation.
  • Remote sensing for agroclimatic and hydroclimatic classification of plant cultivation zones.
  • Renewable energy aspects referring to meteorological and remote sensing analysis.
  • Remote sensing for precision agriculture.
  • Remote sensing for decision support systems (DSS) in agrometeorology.
  • Remotely sensed management of agrometeorological information.
  • Sensoring systems (active-passive sensors, e.g., satellites, weather radar, SAR, UAV).
  • Software tool development for data collection and processing in agrometeorology.

Published Papers (10 papers)

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Article
Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands
Remote Sens. 2022, 14(3), 565; https://doi.org/10.3390/rs14030565 - 25 Jan 2022
Viewed by 898
Abstract
Drought is a significant challenge to semiarid Mediterranean grasslands, Increasing the accuracy of monitoring allows improving the conservation and management of these vital ecosystems. Meteorological drought is commonly monitored by the Standard Precipitation Index (SPI) or the Standard Precipitation Evapotranspiration Index (SPEI). On [...] Read more.
Drought is a significant challenge to semiarid Mediterranean grasslands, Increasing the accuracy of monitoring allows improving the conservation and management of these vital ecosystems. Meteorological drought is commonly monitored by the Standard Precipitation Index (SPI) or the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agriculture drought is estimated by the Vegetation Health Index (VHI). This work aims to optimise the correlation between both drought types using the best transformation of VHI and the most appropriate time scale. Two drought-vulnerable Mediterranean grasslands were selected to evaluate the performance of the drought indexes. The SPI and the SPEI were calculated using data obtained from nearby weather stations. MODIS data were used to calculate the VHI. This index was standardised, naming it as SVHI. Our results revealed that SPEI was better correlated with VHI compared to SPI. In addition, SVHI obtained better results in the critical vegetation phases than VHI. Overall, SPEI and SVHI were the best correlated indexes. The quarterly scale showed stronger relationships than the monthly scale and the most correlated time frame were Mediterranean spring and autumn. This fact suggests that SPEI and SVHI could provide a plus point for increasing the precision of vegetation monitoring during these periods. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR
Remote Sens. 2021, 13(17), 3538; https://doi.org/10.3390/rs13173538 - 06 Sep 2021
Cited by 1 | Viewed by 653
Abstract
The aerodynamic roughness length (Z0) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR [...] Read more.
The aerodynamic roughness length (Z0) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z0 maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z0 maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z0 values from a nearby eddy covariance tower (Z0_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z0 from UAV-LiDAR-driven models converged with Z0_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z0 conditioned by vertical and horizontal vegetation density. The Z0 calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z0_EC. Deriving Z0 in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products
Remote Sens. 2021, 13(6), 1177; https://doi.org/10.3390/rs13061177 - 19 Mar 2021
Cited by 2 | Viewed by 906
Abstract
Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, [...] Read more.
Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands
Remote Sens. 2021, 13(5), 840; https://doi.org/10.3390/rs13050840 - 24 Feb 2021
Cited by 7 | Viewed by 1399
Abstract
Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. [...] Read more.
Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain
Remote Sens. 2021, 13(4), 568; https://doi.org/10.3390/rs13040568 - 05 Feb 2021
Cited by 2 | Viewed by 1293
Abstract
Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These [...] Read more.
Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data
Remote Sens. 2020, 12(21), 3642; https://doi.org/10.3390/rs12213642 - 06 Nov 2020
Cited by 5 | Viewed by 1467
Abstract
Evapotranspiration (ET) is an important component of the Earth’s energy and water cycle via the interaction between the atmosphere and the land surface. The reference evapotranspiration (ET0) is particularly important in the croplands because it is a convenient and reasonable method [...] Read more.
Evapotranspiration (ET) is an important component of the Earth’s energy and water cycle via the interaction between the atmosphere and the land surface. The reference evapotranspiration (ET0) is particularly important in the croplands because it is a convenient and reasonable method for calculating the actual evapotranspiration (AET) that represents the loss of water in the croplands through the soil evaporation and vegetation transpiration. To date, many efforts have been made to retrieve ET0 on a spatially continuous grid. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) product is provided with a reasonable spatial resolution of 500 m and a temporal resolution of 8 days. However, the applicability to the local-scale variabilities due to complex and heterogeneous land surfaces in countries like South Korea is not sufficiently validated. Meanwhile, the AI approaches showed a useful functionality for the ET0 retrieval on the local scale but have rarely demonstrated a substantial product for a spatially continuous grid. This paper presented a retrieval of the daily reference evapotranspiration (ET0) over a 500 m grid for croplands in South Korea using machine learning (ML) with satellite images and numerical weather prediction data. In a blind test for 2013–2019, the ML-based ET0 model produced the accuracy statistics with a root mean square error of 1.038 mm/day and a correlation coefficient of 0.870. The results of the blind test were stable irrespective of location, year, and month. This outcome is presumably because the input data of the ML-based ET0 model were suitably arranged spatially and temporally, and the optimization of the model was appropriate. We found that the relative humidity and land surface temperature were the most influential variables for the ML-based ET0 model, but the variables with lower importance were also necessary to consider the nonlinearity between the variables. Using the daily ET0 data produced over the 500 m grid, we conducted a case study to examine agrometeorological characteristics of the croplands in South Korea during the period when heatwave and drought events occurred. Through the experiments, the feasibility of the ML-based ET0 retrieval was validated, especially for local agrometeorological applications in regions with heterogeneous land surfaces, such as South Korea. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Performances of Vegetation Indices on Paddy Rice at Elevated Air Temperature, Heat Stress, and Herbicide Damage
Remote Sens. 2020, 12(16), 2654; https://doi.org/10.3390/rs12162654 - 18 Aug 2020
Cited by 10 | Viewed by 1580
Abstract
Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify [...] Read more.
Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify the crop response against elevated air temperatures, heat stress, and herbicide damage. The spectral reflectance, yield components, and growth parameters such as plant height, leaf area index (LAI), and above-ground dry matter of paddy rice, which was cultivated in a temperature gradient field chamber to simulate global warming conditions, were observed from 2016 to 2018. The relationships between the vegetation indices and the crop parameters were assessed considering stress conditions. The normalized difference vegetation index (NDVI) represented the changes in plant height (R-square = 0.93) and the LAI (R-square = 0.901) before the heading stage. Furthermore, the NDVI and the cumulative growing degree days had a Sigmoid curve and an R-square value of 0.937 under the normal growth case, but it decreased significantly in the herbicide damage case. This characteristic was useful for detecting the damaged crop growth condition. Additionally, to estimate the grain yield of paddy rice, the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index was better: R-square = 0.912; root mean square error = 95.69 g/m2. Photochemical reflectance index was sensitive to physiological stress caused by the heatwave, and it decreased in response to extremely high air temperatures. These results will contribute towards determining vegetation indices under stress conditions and how to effectively utilize them. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia
Remote Sens. 2020, 12(14), 2206; https://doi.org/10.3390/rs12142206 - 10 Jul 2020
Cited by 10 | Viewed by 2402
Abstract
Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could [...] Read more.
Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Article
Testing Proximal Optical Sensors on Quinoa Growth and Development
Remote Sens. 2020, 12(12), 1958; https://doi.org/10.3390/rs12121958 - 17 Jun 2020
Cited by 5 | Viewed by 1200
Abstract
Proximal optical sensors (POSs) are effective devices for monitoring the development of crops and the nitrogen (N) status of plants. POSs are both useful and necessary in facilitating the reduction of N losses into the environment and in attaining higher nitrogen use efficiency [...] Read more.
Proximal optical sensors (POSs) are effective devices for monitoring the development of crops and the nitrogen (N) status of plants. POSs are both useful and necessary in facilitating the reduction of N losses into the environment and in attaining higher nitrogen use efficiency (NUE). To date, no comparison of these instruments has been made on quinoa. A field experiment conducted in Tuscany, Italy, with different POSs, has assessed the development of quinoa with respect to N status. Three sets of POSs were used (SPAD-502, GreenSeeker, and Canopeo App.) to monitor quinoa development and growth under different types of fertilizers (digestate and urea) and levels of N fertilization (100, 50, and 0 kg N ha−1). The present findings showed that in-season predictions of crop biomass at harvest by SPAD-502 and GreenSeeker optical sensors were successful in terms of the coefficient of determination (R2 = 0.68 and 0.82, respectively) and statistical significance (p < 0.05), while the Canopeo App. was suitable for monitoring the plant´s canopy expansion and senescence. The relative error (RE%) showed a remarkably high performance between observed and predicted values, 5.80% and 4.12% for GreenSeeker and SPAD-502, respectively. Overall, the POSs were effective devices for monitoring quinoa development during the growing season and for predicting dry biomass at harvest. However, abiotic stresses (e.g., heat-stress conditions at flowering) were shown to reduce POSs’ accuracy when estimating seed yields at harvest, and this problem will likely be overcome by advancing the sowing date. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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Technical Note
Estimation of Hail Damage Using Crop Models and Remote Sensing
Remote Sens. 2021, 13(14), 2655; https://doi.org/10.3390/rs13142655 - 06 Jul 2021
Cited by 1 | Viewed by 932
Abstract
Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers and the insurance market ask for a reliable, objective, and less labor-intensive [...] Read more.
Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers and the insurance market ask for a reliable, objective, and less labor-intensive method to determine crop hail losses. Integrating remote sensing and crop modeling provides a unique opportunity for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. To this end, a study was conducted on eight distinct maize fields for a total of 90 hectares. Five fields were damaged by the hailstorm that occurred on 13 July 2019 and three were not damaged. Soil and plant samples were collected to characterize the experimental areas. The Surface Energy Balance Algorithm for Land (SEBAL) was deployed to determine the total aboveground biomass and obtainable yield at harvest, using Landsat 7 and 8 satellite images. Modeled hail damages (HDDSSAT1, coupling SEBAL estimates of obtainable yield and DSSAT-based potential yield; HDDSSAT2, coupling yield map at harvest and the Decision Support System for Agrotechnology Transfer (DSSAT)-based potential yield) were calculated and compared to the estimates of the insurance company (HDinsurance). SEBAL-based biomass and yield estimates agreed with in-season measurements (−4% and +0.5%, respectively). While some under and overestimations were observed, HDinsurance and HDDSSAT1 averaged similar values (−4.9% and +3.4%) compared to the reference approach (HDDSSAT2). Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
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