Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

Characteristics of hail hazard in South Africa based on satellite detection of convective storms

Accurate estimates of hail risk to fixed and mobile assets such as crops, infrastructure and vehicles are required for both insurance pricing and preventive measures. Here we present an event catalog to describe hail hazard in South Africa guided by 14 years of geostationary satellite observations of convective storms. Overshooting cloud tops have been detected, grouped and tracked to describe the spatio-temporal extent of potential hail events. It is found that hail events concentrate mainly in the southeast of the country, along the Highveld and the eastern slopes. Events are most frequent from mid-November through February and peak in the afternoon, between 13 and 17 UTC. Multivariate stochastic modeling of event properties yields an event catalog spanning 25 000 years, aiming to estimate, in combination with vulnerability and exposure data, hail damage for return periods of 200 years.

Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

Cloud Seeding and Crops Yields: Evaluation of the North Dakota Cloud Modification Project

The North Dakota Cloud Modification Project was established in 1951 to reduce severe hail damage and increase precipitation in specific counties in North Dakota. Every year, participating counties receive cloud seeding treatment during the months of June, July, and August. Although some atmospheric studies have examined the efficacy of the treatment, few have used statistical procedures to determine how the program affected crop yields and crop losses. We use the panel nature of historical cloud seeding participation and crop data to estimate a two-way fixed effects regression with county-specific time trends to examine the effect of cloud seeding on wheat and barley yields. In addition, we use federal crop insurance data to estimate the effect of cloud seeding on losses for those same crops. Our evaluation indicates that the cloud seeding program had significant positive effects on crop yields and improved loss ratios.

Recovery measures and field management strategies after hailstone damage in upland cotton

Background Hailstorm might damage cotton plants severely and cause heavy economic loss in field production. It hailed vehemently three times in 2015 and 2016 in Yellow River Basin Cotton Region and damaged cotton seedling and buds. The apical buds, leaves and stems were damaged as well as boll branches and few flower buds in our experiments. Serials strategies were performed to recover and rescue the cotton plants. Based on evaluating the hailstone damage, we fertilized 112.50 kg/ha Urea fertilizer (N content ≥ 46.4%) and intertilled the field timely. Results The recovery of plant individuals sprouted new buds and many new leaves after 12 DAH. Then the unnecessary shoots were pruned to adjust the development of fruit branches. Normally three new boll branches (NBB) in the major stem were reserved and eight subsequent emerging boll branches (EBB) in cotton plants. Five accessions with varied recovery ability and with different yield potential were sampled to compare the yield after hail damage, Sumian 20, 11–0710 and 11–0516 increased, while the other two accessions decreased relative to the normal production without hail damage. BC and RIL populations of upland cotton were used to evaluate the damage ratio of yield, which resulted in yield loss ranged 13.45%-20.27%. Fiber length, fibre elongation, fibre uniformity, and fiber elongation decreased slightly in the five accessions and in two populations. Conclusions The present study indicated that different accessions showed varied recovery ability for yield production, but all of them with a decreased ratio less than 20%. In addition, there was no significant effect on fiber quality in different cotton varieties. These results proved that the cotton plants can compensate by proper field managements, and remedial output could be obtained after hail damage.