Monitoring vegetation dynamics from lunar orbiting satellites

PresentationFUNCTION STATEMENT: Our analyses are based on data from Terra MISR/MODIS, Aqua MODIS, DSCIVR EPIC and EO-1 Hyperion sensors. Science questions as identified in NASA’s Science Plan: Detect and predict changes in Earth’s ecological and chemical cycles, including land cover, biodiversity, and the global carbon cycle.First author draf

Classification-based forest management program and farmers' income : evidence from collective forest area in southern China

Purpose This article's purpose is to examine the effect of a Classification-Based Forest Management (CFM) program on farmers' income and determine whether its effect varies with the degree of farmers' concurrent occupations. Design/methodology/approach The authors use representative panel survey data from Longquan to explore the welfare effects of CFM on farmers. The analysis uses differences-in-differences with propensity score matching (PSM-DID) estimation techniques to deal with endogeneity problems when farmers make the decision to participate in CFM. Findings The results show that CFM has a positive effect on part-time forestry households (where forestry income accounts for between 5 and 50% of total income). In contrast, it has a negative impact on full-time forestry households (forestry income accounts for more than 50%), and no clear effect on nonforestry households whose forestry income is less than 5%. This research also shows that the positive effect of CFM on farmers' total income is mainly due to increase of off-farm income driven by CFM, while the negative effects consist of CFM's reduction of forestry income. Originality/value The extent of CFM's economic benefits to farmers is uncertain and largely unexplored. This paper analyzes the impact of CFM on income structure to explore the mechanisms explaining its effects on farmers' income. There are still challenges in ensuring the reliability and accuracy of CFM assessment. This paper collected natural experimental data and used the estimation technology of PSM-DID to solve the possible endogeneity problems.Peer reviewe

Earth reflectivity from Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Camera (EPIC)

Poster presented at 2017 AGU Fall Meeting, New Orleans, Louisiana. POSTER ID: A33D-2387Earth reflectivity, which is also specified as Earth albedo or Earth reflectance, is defined as the fraction of incident solar radiation reflected back to space at the top of the atmosphere. It is a key climate parameter that describes climate forcing and associated response of the climate system. Satellite is one of the most efficient ways to measure earth reflectivity. Conventional polar orbit and geostationary satellites observe the Earth at a specific local solar time or monitor only a specific area of the Earth. For the first time, the NASA’s Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR) collects simultaneously radiance data of the entire sunlit earth at 8 km resolution at nadir every 65 to 110 min. It provides reflectivity images in backscattering direction with the scattering angle between 168º and 176º at 10 narrow spectral bands in ultraviolet, visible, and near-Infrared (NIR) wavelengths. We estimate the Earth reflectivity using DSCOVR EPIC observations and analyze errors in Earth reflectivity due to sampling strategy of polar orbit Terra/Aqua MODIS and geostationary Goddard Earth Observing System-R series missions. We also provide estimates of contributions from ocean, clouds, land and vegetation to the Earth reflectivity. Graphic abstract shows enhanced RGB EPIC images of the Earth taken on July-24-2016 at 7:04GMT and 15:48 GMT. Parallel lines depict a 2330 km wide Aqua MODIS swath. The plot shows diurnal courses of mean Earth reflectance over the Aqua swath (triangles) and the entire image (circles). In this example the relative difference between the mean reflectances is +34% at 7:04GMT and -16% at 15:48 GMT. Corresponding daily averages are 0.256 (0.044) and 0.231 (0.025). The relative precision estimated as root mean square relative error is 17.9% in this example

The Role of AM Symbiosis in Plant Adaptation to Drought Stress

Symposium paper Part 1: Function and management of soil microorganisms in agro-ecosystems with special reference to arbuscular mycorrhizal fung

Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infraredband SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.Accepted manuscrip

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S leaf area index products in maize crops

Altres ajuts: this research was funded by the Copernicus Global Land Service (CGLOPS-1, 199494-JRC).We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S

Implications of whole-disc DSCOVR EPIC spectral observations for estimating Earth's spectral reflectivity based on low-earth-orbiting and geostationary observations

Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.The NASA/GSFC DSCOVR project is funded by NASA Earth Science Division. W. Song, G. Yan, and X. Mu were also supported by the key program of National Natural Science Foundation of China (NSFC; Grant No. 41331171). This research was conducted and completed during a 13-month research stay of the lead author in the Department of Earth and Environment, Boston University as a joint Ph.D. student, which was supported by the Chinese Scholarship Council (201606040098). DSCOVR EPIC L1B data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would like to thank the editor who handled this paper and the two anonymous reviewers for providing helpful and constructive comments and suggestions that significantly helped us improve the quality of this paper. (NASA Earth Science Division; 41331171 - key program of National Natural Science Foundation of China (NSFC); 201606040098 - Chinese Scholarship Council)Accepted manuscrip

Rheological properties and structural features of coconut milk emulsions stabilized with maize kernels and starch

peer-reviewedIn this study, maize kernels and starch with different amylose contents at the same concentration were added to coconut milk. The nonionic composite surfactants were used to prepare various types of coconut milk beverages with optimal stability, and their fluid properties were studied. The steady and dynamic rheological property tests show that the loss modulus (G″) of coconut milk is larger than the storage modulus (G′), which is suitable for the pseudoplastic fluid model and has a shear thinning effect. As the droplet size of the coconut milk fluid changed by the addition of maize kernels and starch, the color intensity, ζ-potential, interfacial tension and stability of the sample significantly improved. The addition of the maize kernels significantly reduced the size of the droplets (p < 0.05). The potential values of zeta (ζ) and the surface tension of the coconut milk increased. Based on the differential scanning calorimetry (DSC) measurement, the addition of maize kernels leads to an increase in the transition temperature, especially in samples with a high amylose content. The higher transition temperature can be attributed to the formation of some starches and lipids and the partial denaturation of proteins in coconut milk, but phase separation occurs. These results may be helpful for determining the properties of maize kernels in food-containing emulsions (such as sauces, condiments, and beverages) that achieve the goal of physical stability

TRANSOM: An Efficient Fault-Tolerant System for Training LLMs

Large language models (LLMs) with hundreds of billions or trillions of parameters, represented by chatGPT, have achieved profound impact on various fields. However, training LLMs with super-large-scale parameters requires large high-performance GPU clusters and long training periods lasting for months. Due to the inevitable hardware and software failures in large-scale clusters, maintaining uninterrupted and long-duration training is extremely challenging. As a result, A substantial amount of training time is devoted to task checkpoint saving and loading, task rescheduling and restart, and task manual anomaly checks, which greatly harms the overall training efficiency. To address these issues, we propose TRANSOM, a novel fault-tolerant LLM training system. In this work, we design three key subsystems: the training pipeline automatic fault tolerance and recovery mechanism named Transom Operator and Launcher (TOL), the training task multi-dimensional metric automatic anomaly detection system named Transom Eagle Eye (TEE), and the training checkpoint asynchronous access automatic fault tolerance and recovery technology named Transom Checkpoint Engine (TCE). Here, TOL manages the lifecycle of training tasks, while TEE is responsible for task monitoring and anomaly reporting. TEE detects training anomalies and reports them to TOL, who automatically enters the fault tolerance strategy to eliminate abnormal nodes and restart the training task. And the asynchronous checkpoint saving and loading functionality provided by TCE greatly shorten the fault tolerance overhead. The experimental results indicate that TRANSOM significantly enhances the efficiency of large-scale LLM training on clusters. Specifically, the pre-training time for GPT3-175B has been reduced by 28%, while checkpoint saving and loading performance have improved by a factor of 20.Comment: 14 pages, 9 figure