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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 35627

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Prof. Dr. Nicolas R. Dalezios

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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
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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

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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 2700 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 (11 papers)

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Research

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17 pages, 4692 KiB

Open AccessArticle

Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

by Andres F. Almeida-Ñauñay, María Villeta, Miguel Quemada and Ana M. Tarquis

Remote Sens. 2022, 14(3), 565; https://doi.org/10.3390/rs14030565 - 25 Jan 2022

Cited by 10 | Viewed by 2978

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 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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22 pages, 26327 KiB

Open AccessArticle

Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

by Katerina Trepekli and Thomas Friborg

Remote Sens. 2021, 13(17), 3538; https://doi.org/10.3390/rs13173538 - 06 Sep 2021

Cited by 5 | Viewed by 2591

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 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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22 pages, 5982 KiB

Open AccessArticle

Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

by Peijuan Wang, Yuping Ma, Junxian Tang, Dingrong Wu, Hui Chen, Zhifeng Jin and Zhiguo Huo

Remote Sens. 2021, 13(6), 1177; https://doi.org/10.3390/rs13061177 - 19 Mar 2021

Cited by 12 | Viewed by 2335

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, 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 (T_min) 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 T_min 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 T_min observations showed that the accuracy of estimated T_min 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 T_min estimation. Based on the T_min 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 T_min 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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24 pages, 3890 KiB

Open AccessArticle

Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

by Ernesto Sanz, Antonio Saa-Requejo, Carlos H. Díaz-Ambrona, Margarita Ruiz-Ramos, Alfredo Rodríguez, Eva Iglesias, Paloma Esteve, Bárbara Soriano and Ana M. Tarquis

Remote Sens. 2021, 13(5), 840; https://doi.org/10.3390/rs13050840 - 24 Feb 2021

Cited by 20 | Viewed by 3598

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. 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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21 pages, 36439 KiB

Open AccessArticle

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

by David Andrés Rivas-Tabares, Antonio Saa-Requejo, Juan José Martín-Sotoca and Ana María Tarquis

Remote Sens. 2021, 13(4), 568; https://doi.org/10.3390/rs13040568 - 05 Feb 2021

Cited by 10 | Viewed by 2891

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 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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22 pages, 6456 KiB

Open AccessArticle

Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data

by Nari Kim, Kwangjin Kim, Soobong Lee, Jaeil Cho and Yangwon Lee

Remote Sens. 2020, 12(21), 3642; https://doi.org/10.3390/rs12213642 - 06 Nov 2020

Cited by 16 | Viewed by 3338

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 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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25 pages, 8135 KiB

Open AccessArticle

Performances of Vegetation Indices on Paddy Rice at Elevated Air Temperature, Heat Stress, and Herbicide Damage

by Jae-Hyun Ryu, Hoejeong Jeong and Jaeil Cho

Remote Sens. 2020, 12(16), 2654; https://doi.org/10.3390/rs12162654 - 18 Aug 2020

Cited by 28 | Viewed by 4017

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 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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20 pages, 5199 KiB

Open AccessArticle

Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

by Astrid Vannoppen, Anne Gobin, Lola Kotova, Sara Top, Lesley De Cruz, Andris Vīksna, Svetlana Aniskevich, Leonid Bobylev, Lars Buntemeyer, Steven Caluwaerts, Rozemien De Troch, Natalia Gnatiuk, Rafiq Hamdi, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck and Piet Termonia

Remote Sens. 2020, 12(14), 2206; https://doi.org/10.3390/rs12142206 - 10 Jul 2020

Cited by 24 | Viewed by 5132

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 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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16 pages, 1656 KiB

Open AccessArticle

Testing Proximal Optical Sensors on Quinoa Growth and Development

by Jorge Alvar-Beltrán, Carolina Fabbri, Leonardo Verdi, Stefania Truschi, Anna Dalla Marta and Simone Orlandini

Remote Sens. 2020, 12(12), 1958; https://doi.org/10.3390/rs12121958 - 17 Jun 2020

Cited by 12 | Viewed by 2703

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 (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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Review

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27 pages, 5276 KiB

Open AccessReview

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

by Nildson Rodrigues de França e Silva, Michel Eustáquio Dantas Chaves, Ana Cláudia dos Santos Luciano, Ieda Del’Arco Sanches, Cláudia Maria de Almeida and Marcos Adami

Remote Sens. 2024, 16(5), 863; https://doi.org/10.3390/rs16050863 - 29 Feb 2024

Viewed by 1092

Abstract

The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data. Full article

(This article belongs to the Special Issue Remote Sensing for Agrometeorology)

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13 pages, 6123 KiB

Open AccessTechnical Note

Estimation of Hail Damage Using Crop Models and Remote Sensing

by Stefano Gobbo, Alessandro Ghiraldini, Andrea Dramis, Nicola Dal Ferro and Francesco Morari

Remote Sens. 2021, 13(14), 2655; https://doi.org/10.3390/rs13142655 - 06 Jul 2021

Cited by 12 | Viewed by 3051

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 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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