scholarly journals INTER-ANNUAL DIFFERENCE OF LAND SURFACE PHENOLOGY (LSP) TRANSACTION DATES USING TIME SERIES LANDSAT IMAGES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 721-725
Author(s):  
L. Li ◽  
J. Li ◽  
H. Yang ◽  
Z. Jiang ◽  
L. Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Ground Vegetation ◽  
Growth Monitoring ◽  
Landsat Images ◽  
Land Surface Phenology ◽  
Vegetation Growth ◽  
Good Proxy

Abstract. Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring. Time series Landsat images, with the advantages of long observations and high spatial resolution, have been widely used in LSP identification. However, LSP transaction dates, such as start of season (SOS) and end of season (EOS), are highly influenced by the coarse temporal resolution. In this study, we compare the inter-annual difference of LSP SOS from 5 years interval, 10 years interval and all years interval Landsat images, and improve the SOS estimated model by considering the accumulated growing degree-days (AGDD) of soil temperature and soil moisture. Results indicate that LSP SOS can serve as a good proxy for reflecting ground vegetation phenology, especially using 5 years interval Landsat images. Soil temperature and soil moisture have certain influence on SOS estimation, and the R-squared value reached 0.9 after model adjustment. This study can provide guidance for estimating suitable inter-annual LSP transaction dates under different sceneries in the future.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Diandong Ren
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Initial Guess ◽  
Surface Model ◽  
Soil Moisture Condition ◽  
Wide Range ◽  
Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia

2022 ◽  
Vol 114 ◽  
pp. 103804
Author(s):  
Issam Touhami ◽  
Hassane Moutahir ◽  
Dorsaf Assoul ◽  
Kaouther Bergaoui ◽  
Hamdi Aouinti ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Climatic Variables ◽  
Mediterranean Forest ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series

scholarly journals Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

2019 ◽  
Vol 11 (2) ◽  
pp. 181 ◽  
Author(s):  
Daniel Sousa ◽  
Christopher Small
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Mixture Models ◽  
Upper Bound ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Spatial Extent ◽  
Landsat Images ◽  
Rice Agriculture ◽  
Field Station

Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

scholarly journals Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

2019 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Lan H. Nguyen ◽  
Geoffrey M. Henebry
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Quadratic Model ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Land Surface Phenology ◽  
Land Cover Map ◽  
Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

2019 ◽  
Vol 32 (21) ◽  
pp. 7521-7537 ◽  
Author(s):  
Daniel Fiifi Tawia Hagan ◽  
Guojie Wang ◽  
X. San Liang ◽  
Han A. J. Dolman
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Information Flow ◽  
Air Temperature ◽  
Land Surface ◽  
Full Range ◽  
Accurate Estimation ◽  
Time Varying ◽  
Long Time Series ◽  
Long Time

Abstract The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

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