scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

scholarly journals Development of a remote sensing-based rice yield forecasting model

2016 ◽  
Vol 14 (3) ◽  
pp. e0907 ◽  
Author(s):  
Mostafa K. Mosleh ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Decision Makers ◽  
Coefficient Of Determination ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

scholarly journals ANALISIS TUTUPAN LAHAN DENGAN MENGGUNAKAN METODE BREAK FOR ADDITIVE SEASONAL AND TREND (BFAST) BERDASARKAN INDEKS VEGETASI

2017 ◽  
Author(s):  
Seminar Nasional Multidisiplin Ilmu 2017 ◽  
Ramos Lumban Tobing
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Trend Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Metode penginderaan jarak jauh (Remote Sensing) telah banyak digunakan dalam berbagai bidang termasuk diantaranya bidang tutupan lahan/vegetasi termasuk perkebunan. Produk dari penginderaan jauh tersebut banyak tersedia diantaranya NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Indeks) yang merupakan indikator proxy dari suatu lokasi atau kondisi tutupan lahan lokasi tersebut. Dari beberapa penilitian, NDVI telah banyak digunakan namun EVI masih belum banyak digunakan. Kami membandingkan pengaruh dari penggunaan NDVI dan EVI pada jumlah dan waktu perubahan yang terekam dengan menggunakan metode BFAST (Breaks For Additive Seasonal and Trend). Data yang digunakan adalah MODIS (Moderate Resolution Imaging Spectroradiometer)16 harian NDVI dan EVI berupa gambar komposit (06 April 2000 s.d. 16 November 2014) dari empat piksel (pixel 293,294,295 dan 296) disekitar menara fluks Aek Loba.Hasil penelitian menunjukkan bahwa EVI untuk pemantauan tutupan lahan di kawasan perkebunan tropis yang ditutupi oleh awan intens lebih baik dari NDVI itu. Meskipun demikian, penelitian lebih lanjut dengan meningkatkan resolusi spasial dari citra satelit untuk aplikasi NDVI sangat dianjurkan

scholarly journals Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem

2018 ◽  
Vol 10 (12) ◽  
pp. 2061 ◽  
Author(s):  
José Melendo-Vega ◽  
M. Martín ◽  
Javier Pacheco-Labrador ◽  
Rosario González-Cascón ◽  
Gerardo Moreno ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Proximal Sensing ◽  
Modis Ndvi ◽  
Reflectance Factor ◽  
Moderate Resolution Imaging Spectroradiometer

The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles.

scholarly journals Assessment of land degradation ‘on the ground’ and from ‘above’

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Oliver K. Kirui ◽  
Alisher Mirzabaev ◽  
Joachim von Braun
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hot Spot ◽  
Ground Level ◽  
Eastern Africa ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ground Truthing

AbstractAssessments of land degradation vary in methodology and outcome. The objective of this study is to identify the state, extent and patterns of land degradation in Eastern Africa (Ethiopia, Kenya, Malawi and Tanzania). More recently (2000s), satellite-based imagery and remote sensing have been utilized to identify the magnitude and processes of land degradation at global, regional and national levels. This involves the use of Normalized Difference Vegetation Index (NDVI) derived from Advanced Very High Resolution Radiometer data and the use of high-quality satellite data from Moderate Resolution Imaging Spectroradiometer. This study is the first in Eastern Africa to complement remote sensing with ground-level assessments in evaluating the extent of land degradation at national and regional scales. The results based on NDVI measures show that land degradation occurred in about 51%, 41%, 23% and 22% of the terrestrial areas in Tanzania, Malawi, Ethiopia and Kenya, respectively, between the 1982 and 2016 periods. Some of the key hot spot areas include west and southern regions of Ethiopia, western part of Kenya, southern parts of Tanzania and eastern parts of Malawi. To evaluate the accuracy of the NDVI observations, ground-truthing was carried out in Tanzania and Ethiopia through focus group discussions (FGDs). The FGDs indicate an agreement with remotely sensed information on land degradation in seven sites out of eight in Tanzania and five sites out of six in Ethiopia. Given the significant magnitude of land degradation, appropriate action is needed to address it.

scholarly journals Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

2011 ◽  
Vol 15 (1) ◽  
pp. 223-239 ◽  
Author(s):  
M. C. Anderson ◽  
W. P. Kustas ◽  
J. M. Norman ◽  
C. R. Hain ◽  
J. R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Balance Model ◽  
Surface Model ◽  
Surface Moisture ◽  
The Us ◽  
Polar Orbiting Satellite

Abstract. Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

scholarly journals A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

2018 ◽  
Vol 15 (19) ◽  
pp. 5779-5800 ◽  
Author(s):  
Yao Zhang ◽  
Joanna Joiner ◽  
Seyed Hamed Alemohammad ◽  
Sha Zhou ◽  
Pierre Gentine
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Clear Sky ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Regression Slopes ◽  
The Individual ◽  
Sky Conditions

Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

scholarly journals Use of aerial image in the estimation of volume and biomass of Eucalyptus sp. forest stand

2020 ◽  
pp. 286-294
Author(s):  
Thallita R. S. Mendes ◽  
Eder P. Miguel ◽  
Pedro G. A. Vasconcelos ◽  
Marco B. X. Valadão ◽  
Alba V. Rezende ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Basal Area ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Fit Statistics ◽  
Remote Sensing Techniques ◽  
The Individual

Assessing forest stands is crucial for managing and planning the use of these resources. Forest inventory is the instrument that provides information about the stand situation, which can be costly and time consuming. In order to facilitate and reduce the time spent obtaining these data, the main objective of this work was to evaluate the accuracy of volume and biomass estimates per unit area with data from remote sensing. Forty sample units were allocated and georeferenced, in which all trees with diameter at breast height (DBH) ≥ 5 cm were inventoried. Sequentially, the cubage was performed in order to obtain individual biomass, volume, and adjustment of the individual models. With data from georeferenced images of the study area, the vegetation indices MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were obtained. The volume and biomass estimation using remote sensing variables were carried out through the adjustment of sigmoidal models by regression analysis, which used a combination of the average values of the vegetation indices and the basal area of the plot/hectares as an independent variable. The fit statistics and the accuracy of the tested models presented consistent results to estimate forest production. The results showwd that indices derived from remote sensing techniques associated with forest variables information could accurately estimate the volume and biomass of Eucalyptus spp. plantations.

scholarly journals Albedo Impacts of Changing Agricultural Practices in the United States through Space-Borne Analysis

2020 ◽  
Vol 12 (18) ◽  
pp. 2887
Author(s):  
Jon Starr ◽  
Jianglong Zhang ◽  
Jeffrey S. Reid ◽  
David C. Roberts
Keyword(s):  
United States ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Practices ◽  
The United States ◽  
Growing Period ◽  
Field Crops ◽  
Sensing Applications ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.

scholarly journals Mapping daily evapotranspiration at field to global scales using geostationary and polar orbiting satellite imagery

2010 ◽  
Vol 7 (4) ◽  
pp. 5957-5990 ◽  
Author(s):  
M. C. Anderson ◽  
W. P. Kustas ◽  
J. M. Norman ◽  
C. R. Hain ◽  
J. R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Balance Model ◽  
Surface Model ◽  
Surface Moisture ◽  
The Us ◽  
Polar Orbiting Satellite

Abstract. Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (soil moisture, advection, air temperature) are affecting plant stress. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5–10 km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI), spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions of 30 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe and, Africa and other continents with geostationary satellite coverage.

Why Is Remote Sensing of Amazon Forest Greenness So Challenging?

2012 ◽  
Vol 16 (7) ◽  
pp. 1-14 ◽  
Author(s):  
Arindam Samanta ◽  
Sangram Ganguly ◽  
Eric Vermote ◽  
Ramakrishna R. Nemani ◽  
Ranga B. Myneni
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Effects ◽  
Amazon Forest ◽  
Surface Reflectance ◽  
Wet Season ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Reflectance Data

Abstract The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October–March), cloud contamination of greenness data is pervasive; nearly the entire region lacks uncorrupted observations. Even in the dry season (July–September), nearly 60%–66% of greenness data are corrupted, mainly because of biomass burning aerosol contamination. Under these conditions, spectrally varying residual atmospheric effects in surface reflectance data introduce artifacts into greenness indices; NDVI is known to artificially decrease, whereas EVI, given its formulation and use of blue channel surface reflectance data, shows artificial enhancement, which manifests as large patches of enhanced greenness. These issues render remote sensing of Amazon forest greenness a challenging task.

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