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Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies. [ABSTRACT FROM AUTHOR]

Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet's PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model's. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r2: 0.61; RMSE: 0.29 kg.m−2). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers' management decisions. [ABSTRACT FROM AUTHOR]

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

Several recent studies have found that retrievals of land surface temperature (LST) from remote sensing measurements depend upon the angle of observation. To understand, predict, and ultimately correct this sensitivity, simple but physically based models of LST angular anisotropy are needed. In this study, we describe and evaluate the modified geometric projection (MGP) model, a highly parameterized model of scene thermal infrared (TIR) radiance applicable to both homogeneous and discontinuous canopy environments. Based on geometric optics modeling, MPG assumes that the angular anisotropy of TIR radiance over discontinuous canopies is due strictly to the different proportions of scene endmembers (e.g., sunlit tree crowns, background shadows) visible to a sensor at different sun-view geometries. We tested MGP against DART, a rigorous three-dimensional radiative transfer model, and against field-measured data from a southern Africa savanna. For a prescribed set of canopy conditions, MGPs estimates of observable endmember fractions and scene temperatures in the solar principal plane compared well with estimates from DART. We also parameterized MGP with field-measured endmember data for an acacia/combretum savanna near Skukuza, South Africa. We angularly integrated the MGP-predicted radiances and compared the results with measurements of scene hemispherical exitance from a tower-based pyrgeometer. The modeled exitances exhibited the normal diurnal behavior. Model predictions generally agreed with the pyrgeometer measurements; however, model accuracy decreased as the difference in endmember temperatures increased. These tests suggest that the assumptions inherent in the MGP model do not seriously impact the accuracy of the simulated radiances. We conclude that the MGP model accurately captures the predominate thermal emission directionality resulting from discontinuous canopy structure, and could therefore be applied at continental and global scales. Index Terms--African savanna, angular effects, geometric optics (GO), land surface temperature (LST), thermal infrared (TIR), vegetation structure.

In the context of increasing environmental change, monitoring crop conditions throughout the growing season is critical for agricultural management, risk mitigation and early assessment of food, feed, fuel and fiber production. Satellite Earth observations can provide frequent and spatially continuous measures of cropping systems to support informed agricultural decisions. However, current analysis-ready satellite data are based on optical observational systems that do not provide land surface information under cloudy conditions, and therefore cannot ensure a continuous and operational monitoring of dynamic systems such as crops. To fill this gap, Planet developed the Biomass Proxy product which provides a timely and analysis-ready relative measure of the above-ground crop biomass daily at 10 m spatial resolution. Based on Earth observations from Sentinel-1 and Sentinel-2 constellations, the Biomass Proxy is a cloud-free vegetation monitoring product for agricultural management at scales varying from the intra-field to the national scale. The objective of this study was to quantify the sensitivity of Planet's Biomass Proxy to crop biomass dynamics and yield. Field data representative of various agricultural systems and climates from the University of Nebraska-Lincoln and Michigan State University were used to characterize the relationships between the Biomass Proxy and the biomass of corn, winter wheat and soybean fields. Results demonstrated the sensitivity of the Biomass Proxy to changes in crop fresh biomass throughout the growing season, and the potential of the product to detect rapid changes in plant growth due to agricultural practices or environmental stresses, such as nitrogen or water deficiencies. The Biomass Proxy was highly correlated with the plant fresh biomass (R2 > 0.9 for corn) allowing for near real-time monitoring of crop growth and management decisions such as irrigation water, fertilizer and fungicide applications depending on the crop. Using regressions, the Biomass Proxy was able to explain 80 % of the yield variance of agricultural fields one to two months before harvest, allowing marketing and logistic decisions to be made with a better knowledge of the crop status. The Biomass Proxy is a unique satellite-based product used to characterize the various components of agricultural ecosystems, early detect crop growth anomalies, or assess crop yield. The Biomass Proxy provides real time information on crop conditions and environmental threats, helping mitigate risks by supporting agricultural decisions. • Planet's Biomass Proxy is a cloud-free vegetation monitoring product for agriculture. • The Biomass Proxy is a satellite-based relative measure of crop biomass. • It can early detect crop growth anomalies due to water shortage. • It explained 80 % of corn yield in Nebraska 8 weeks before harvest. [ABSTRACT FROM AUTHOR]