Exploiting the capabilities of Sentinel-2 and RapidEye for predicting grass nitrogen across different grass communities in a protected area

Chabalala, Yingisani; Adam, Elhadi; Oumar, Zakariyyaa; Ramoelo, Abel

doi:10.1007/s12518-020-00305-8

Cited by 12 publications

(14 citation statements)

References 58 publications

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“…The main objective for combining sensors [45,118,132] is to address cloud contamination, especially in places where cloud poses significant challenges (i.e., tropical rainforest, mountain regions, polar and monsoon areas) [32,74,99,[133][134][135], with multi-temporal sensors approach [99,136,137] or by using SAR imagery [7,132,138,139]. Other objectives include comparing model performances between sensors [7,34,45,132,140,141] and when greater detail is needed for field measurements and species discrimination [114,142,143]. Fused data of 30 cm resolution from UAS and PlanetScope imagery achieved a higher correlation of 87% compared with ground measurement for estimating pastures at the field level (10 ha) than those obtained from Planet (65%) data [114].…”

Section: Description Of Remote Sensing Technologies Usedmentioning

confidence: 99%

Section: Pasture Management Traitsmentioning

confidence: 99%

“…The main goal is to assess pasture quality using remote sensing as proxies to quantify its management traits [38,46,120,132,140,142,158,170,183,[205][206][207]. Nitrogen availability [140,170,202], soil water condition [67,182,207], irrigation [168], mowing [188,208], livestock distribution [188], soil nutrients [77,142], and fertilizer treatment [207] are the major management traits that have been examined by authors and expressed as pasture quality indicators. Pasture quality has been linked with the aggregation of livestock (animal units) to areas with a rich concentration of nitrogen as a proxy for the abundance of vegetation greenness in mapping the spatial distribution of grazing animals [140,208].…”

Section: Pasture Management Traitsmentioning

confidence: 99%

“…Nitrogen availability [140,170,202], soil water condition [67,182,207], irrigation [168], mowing [188,208], livestock distribution [188], soil nutrients [77,142], and fertilizer treatment [207] are the major management traits that have been examined by authors and expressed as pasture quality indicators. Pasture quality has been linked with the aggregation of livestock (animal units) to areas with a rich concentration of nitrogen as a proxy for the abundance of vegetation greenness in mapping the spatial distribution of grazing animals [140,208]. Some studies have used vegetation indices by selecting bands (red, red-edge, NIR, SWIR) of interest with linear regression models to relate them with management indicators [140,170,176,202,208].…”

Section: Pasture Management Traitsmentioning

confidence: 99%

“…Pasture quality has been linked with the aggregation of livestock (animal units) to areas with a rich concentration of nitrogen as a proxy for the abundance of vegetation greenness in mapping the spatial distribution of grazing animals [140,208]. Some studies have used vegetation indices by selecting bands (red, red-edge, NIR, SWIR) of interest with linear regression models to relate them with management indicators [140,170,176,202,208]. Agricultural inputs, such as the irrigation date and LAI, were retrieved from FORMOSAT-2 using spectral indices and integrated into crop models to support water management for grazed pastures in France [176].…”

Section: Pasture Management Traitsmentioning

confidence: 99%

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Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

Ogungbuyi,

Mohammed,

Ara

et al. 2023

Remote Sensing

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The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

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