Remote sensing, crop yield estimation and agricultural vulnerability assessment: A case of Southern Alberta

Abstract

This research investigates the relationship between agricultural production and the occurrence of meteorological droughts over time. A remote sensing approach is developed to estimate the yield of cereal crops based on the remotely sensed data in the study area. The yield estimates from remotely sensed imageries provide a primary data source to measure agricultural well-being and quantify agricultural vulnerability to drought. The drought condition as the stressor to agricultural production systems is characterized using the standard precipitation index (SPI). The results of the study indicate that crop production system in Southern Alberta is very vulnerable to drought. About half of the study area is associated with a high to extremely high vulnerability. If the drought trend in the recent past repeats itself in the near further, it can be expected that crop production in these areas will be seriously threatened.

Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

“…The technological advancement has in recent years boosted the role of remote sensing in the applicative aspect across all geosciences fields. In the area of hydrology alone, numerous applications have been developed: snow cover estimation [1], detection of the components of the energy budget [2], hydrological model calibration [3], crop yield [4], and river discharge estimation [5].…”

Brief Probe into the Key Factors that Influence Beijing Agricultural Drought Vulnerability

“…The main Vulnerability research bodies are diverse, such as ecosystem, the forests [2] and wetlands [3], etc. ;water resources system [4]; human-environment system [5], urban social living system [6], there are more widely agricultural system [7][8][9][10][11], etc. ; current research also focus on vulnerability caused by climate change [6,[12][13][14][15]; as well as vulnerability studies on earthquake disasters [16], flood disasters [17][18][19][20] and caused by land use changes [21], etc.…”

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

“…Vegetation index is one of the main variables used in modeling the relationship between remote sensing data and grain yield. Various plant diversity indicators have been developed from visible satellite sensors that can provide a lot of information about plant health and biodiversity (Xu et al, 2011). There is a relationship between vegetation indicators (NDVI) and seasonal initial yield, because of this relationship vegetation density can be used as an indirect measure of initial grain yield through clear formation activity of agricultural plants during a certain period before harvest (Adeniyi et al, 2020).…”