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This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.

A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.

Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCI_min and VCI_min are found to be the most proper indices to predict rice yield.

The results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.

This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years.

The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal.

The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone.

Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

This paper aims at providing the evidence about how carbon sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO₂ rates through the adoption of appropriate cropping systems such as agroforestry.

Stratified randomly selected plots were used to collect data on tree diameter at breast height (DBH). Composite soil samples were collected from three soil depths for soil carbon analysis. Above ground biomass estimation was made using an allometric equation. The spectral signature of each plot was extracted to study the statistical relationship between carbon stock and selected vegetation indices.

There was a significant difference in vegetation and soil carbon stocks among the different land use/land cover types (P < 0.05). The potential carbon stock was highest in the vegetation found in sparsely cultivated land (13.13 ± 1.84 tons ha⁻¹) and in soil in bushland (19.21 ± 3.79 tons ha⁻¹). Carbon sequestration potential of the study area significantly increased (+127174.5 tons CO₂e) as a result of conversion of intensively cultivated agricultural lands to agroforestry systems. The amount of sequestered carbon was found to be dependent on species diversity, tree density and tree size. The vegetation indices had a better correlation with soil and total carbon.

The paper has addressed an important aspect in curbing greenhouse gases in integrated land systems. The paper brings a new empirical insight of carbon sequestration potentials of agroforestry systems with a focus on drylands.

Satellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.

In this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.

The results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.

To the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

To investigate whether the desertification risk index (DRI) which was originally developed for the coastal area of Turkey in a previous work, could be used as an effective desertification indicator in other Mediterranean areas such as the Lebanon.

The calculation of the DRI is based on the use of climatic factors and the normalized difference vegetation index (NDVI). As a result, climatic data were obtained and spatial interpolation techniques were applied to derive temperature and precipitation maps within a GIS environment while the NDVI was derived from satellite imagery. Spatial models were employed in order to produce the DRI map of Lebanon. Geographical analysis and standard statistical techniques were employed to investigate the relationships between: desertification risk and two topographic factors, namely, elevation and distance from the sea and desertification risk and the type of land cover. The accuracy of the index was assessed by comparison with recently published official maps and documents.

The paper demonstrates the efficiency of a desertification index to identify areas at risk. The DRI map proved to be accurate when compared to the map of desertification prone areas recently produced by the Lebanese Ministry of Agriculture. The areas with the highest degree of desertification risk are located in the North‐Eastern part of the country, in the area of the Bekaa Valley. This is in agreement with the reports of the United Nations Convention for combating desertification. A strong correlation was found between desertification risk and distance from the sea (the larger the distance the higher the risk) while shrubland appears to be the land cover type with the highest risk of desertification.

This research work demonstrates how satellite imagery and modern spatial analysis techniques could provide an essential alternative to traditional methods.

Shadegan County as a wetland area was selected because of its susceptibility to flooding hazards and inundation. The purpose of this paper is to analyze flooding hazard based on the analytical hierarchy process methodology.

The eight influencing factors (slope, distance from wetland, distance from river, drainage density, elevation, curve number, population density and vegetation density) were considered for flood mapping within the Shadegan County using analytical hierarchical process, geographical information system and remote sensing. The validation of the map was conducted based on the comparison of the historical flood inundation of April 21, 2019.

The results showed that around 32.65% of the area was under high to very high hazard zones, whereas 44.60% accounted for moderate and 22.75% for very low to the low probability of flooding. The distance from Shadegan Wetland has been gained high value and most of the hazardous areas located around this wetland. Finally, the observed flood density in the different susceptibility zones for the very high, high, moderate, low and very low susceptible zones were 0.35, 0.22, 0.15, 0.19, and 0.14, respectively.

To the best of the authors’ knowledge, the flood susceptibility map developed here is one of the first studies in a built wetland area which is affected by anthropogenic factors. The flood zonation map along with management and restoration of wetland can be best approaches to reduce the impacts of floods.

The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area.

The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented.

Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse.

Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict.

The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business.

Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation.

Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions.

To describe the function and use of the GreenSeeker™ active remote sensor used to detect crop nitrogen status.

In this paper, the GreenSeeker active remote sensor and its use in irrigated maize production systems will be described. A brief discussion of the science of using remote sensing for studying plants is presented. Additionally, a summary of observations collected from field trials is presented.

The GreenSeeker active sensor has tremendous potential for accurately characterizing crop variability for site‐specific N rate determinations in the Western Great Plains region of the United States.

This paper discusses the GreenSeeker active sensor for detecting crop variability. Data from the GreenSeeker can be used to make site‐specific nitrogen fertilizer applications which may lead to improved nitrogen use efficiency.