Landsat, Sentinel, MODIS, and SPOT are all commonly used remote sensing platforms for agricultural monitoring.

GIS is used in agriculture for a variety of purposes, including crop mapping, yield estimation, soil analysis, and more.

NDVI, LAI, and FAPAR are all remote sensing techniques used to measure the amount of vegetation in an area.

GIS can be used to improve irrigation management by creating maps of soil moisture content, calculating crop water requirements, and scheduling irrigation events.

Multispectral, hyperspectral, and radar imagery are all commonly used for crop yield estimation.

GIS can be used to support precision agriculture by creating maps of soil fertility, generating yield maps, and guiding variable rate application of inputs.

Remote sensing can be used to detect areas of crop stress, estimate the severity of drought, and forecast drought conditions.

GIS can be used to manage agricultural pests and diseases by mapping their distribution, identifying areas at risk of infestation, and developing management strategies.

Microwave imagery is commonly used for soil moisture monitoring due to its ability to penetrate vegetation and clouds.

GIS can be used to support sustainable agriculture by identifying areas suitable for organic farming, developing land use plans that minimize environmental impact, and promoting the adoption of conservation practices.

Remote sensing can be used to identify different crop types, map land cover and land use patterns, and monitor changes in land use over time.

GIS can be used to improve agricultural marketing and supply chain management by tracking the movement of agricultural products, identifying potential markets, and optimizing transportation routes.

Multispectral, hyperspectral, and thermal imagery are all commonly used for crop health monitoring.

GIS can be used to support agricultural policy and decision-making by providing data for planning and policy development, evaluating the impact of policies, and supporting research and extension activities.

Remote sensing can be used to monitor weather conditions, forecast weather events, and provide early warning of extreme weather events that can affect agriculture.