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Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging
Key message We compared (lognormal) universal kriging with the area-based approach for estimation of forest inventory variables using LiDAR data as...
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Designing a rain gauge network: utilizing satellite-derived precipitation data with geostatistical multivariate techniques
Although numerous studies have investigated the validity of satellite-derived precipitation datasets, there has been a lack of emphasis on their...
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Downscaling crop production data to fine scale estimates with geostatistics and remote sensing: a case study in map** cotton fibre quality
PurposeA generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and...
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Spatial Modeling of Some Selected Soil Nutrients Using Geostatistical Approach for Jhandutta Block (Bilaspur District), Himachal Pradesh, India
In India, the concept of site-specific nutrient management is very important to increase the productivity particularly under rainfed farming systems....
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Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards
Continuous canopy status monitoring is an essential factor to support and precisely apply orchard management actions such as pruning, pesticide and...
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A comparison between mixed support kriging and block cokriging for modelling and combining spatial data with different support
The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical...
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Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?
In the last decades, geostatistics has been widely used for precision agriculture (PA) producing quite exciting results. Research on this topic is...
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Data Fusion in a Data-Rich Era
The application of precision agriculture requires the estimation of space-time variability at a very fine scale. A very wide variety of both remote...
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Spatio-temporal analysis of air pollution in North China Plain
Accompanying China’s rapid industrialization, a vast area of the country, particularly the Bei**g–Tian**–Hebei (BTH) region, has significantly...
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Spatial and dynamic distribution of Chrysoperla spp. and Leucoptera coffeella populations in coffee Coffea arabica L
Leucoptera coffeella (Guérin-Mèneville, 1842) (Lepidoptera: Lyonetiidae) is one of the main pests of coffee. Controlling this insect requires...
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Taking into account change of support when merging heterogeneous spatial data for field partition
The paper describes a geostatistical approach for combining multi-source data with different support for field delineation into homogeneous soil...
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Insights for improving bacterial blight management in coffee field using spatial big data and machine learning
Pseudomonas syringae pv. garcae , the causal agent of coffee disease bacterial blight, causes losses in nurseries and coffee fields. In this work, the...
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A review of methods for scaling remotely sensed data for spatial pattern analysis
ContextLandscape ecologists have long realized the importance of scale when studying spatial patterns and the need for a science of scaling. Remotely...
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Impact of Drought on Ecohydrology of Southern California Grassland and Shrubland
Through their rooting profiles and water demands, plants affect the distribution of water in the soil profile. Simultaneously, soil water content...
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Spatial distribution and influencing factors of urban soil organic carbon stocks in **'an City, China
Urban soil organic carbon (SOC) plays an important role in urban ecosystem services. Rapid urbanization and anthropogenic disturbances have created...
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Impact of sensor placement in soil water estimation: a real-case study
One important problem in implementing a closed-loop irrigation system is the determination of the optimal locations to install the sensors, both...
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Knowledge Is Power: Where Digital Soil Map** Needs Geopedology
Much of current digital soil map** (DSM) practice relies on terrain, climate and remote sensing-derived covariates. These are easy to obtain and...
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Space-time multi-level modeling for zooplankton abundance employing double data fusion and calibration
An important objective for marine biologists is to forecast the distribution and abundance of planktivorous marine predators. To do so, it is...
