Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery

Abstract

Grasslands are important for their ecological values and for agricultural activities such as livestock production worldwide. Efficient grassland management is vital to these values and activities, and remote sensing technologies are increasingly being used to characterize the spatiotemporal variation of grasslands to support those management practices. For this study, Sentinel-2 satellite imagery was used as an input to develop an open-source and automated monitoring system (Sen2Grass) to gain field-specific grassland information on the national and regional level for any given time range as of January 2016. This system was implemented in a cloud-computing platform (StellaSpark Nexus) designed to process large geospatial data streams from a variety of sources and was tested for a number of parcels from the Haus Riswick experimental farm in Germany. Despite outliers due to fluctuating weather conditions, vegetation index time series suggested four distinct growing cycles per growing season. Established relationships between vegetation indices and grassland yield showed poor to moderate positive trends, implying that vegetation indices could be a potential predictor for grassland biomass and chlorophyll content. However, the inclusion of larger and additional datasets such as Sentinel-1 imagery could be beneficial to developing more robust prediction models and for automatic detection of mowing events for grasslands.

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

“…About one-third of the earth's terrestrial surface and 70% of the global agricultural area is covered by grassland ecosystems [1], making them one of the most common land use types worldwide [2,3]. Apart from providing forage for livestock production [2,4], grasslands fulfil several functions and ecosystem services related to soil quality (carbon sequestration and controlling erosion and wildfires), hydrological balance, and climate change [4,5], which make them essential [1].…”

“…Apart from providing forage for livestock production [2,4], grasslands fulfil several functions and ecosystem services related to soil quality (carbon sequestration and controlling erosion and wildfires), hydrological balance, and climate change [4,5], which make them essential [1]. In addition to preserving biodiversity and supporting ecological processes (such as carbon storage, water purification, or erosion control) grasslands are vital for global food security [2], since they provide an important setting for agricultural activities such as livestock production [3].…”

“…The reference measurement method for CP and NDF determinations consists of the cutting and drying of pasture samples to get the actual dry biomass per area unit, followed by specific laboratory analysis [12]. This procedure, carried out at a fine scale and based on field measurements [13], was developed for researchers and although these data are very informative, the whole process of collecting samples and processing them is laborious, destructive, expensive, and not routinely used by farmers [3]. Moreover, the limited number of samples that can be effectively processed in reference methods, based on sampling design distributions and intensity, reduces the possibility of assessing the spatial variability of pastoral resources [12,13].…”

“…Moreover, the limited number of samples that can be effectively processed in reference methods, based on sampling design distributions and intensity, reduces the possibility of assessing the spatial variability of pastoral resources [12,13]. Therefore, an alternative approach for detailed spatial and temporal pasture monitoring is proposed based on innovative tools, such as remote sensing (data from satellite and airborne platforms) and proximal sensing (field-specific sensors) [2,3], contributing to establish sustainable grassland management systems [5].…”

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

“…Precise sensors [218] and UAV-based [219] data acquisition, robotic automation [220,221], embedded software [222] and cloud-based [223] remote monitoring and control towards high throughput smart farming. Future challenges of IoT in agriculture [224] 5…”