scholarly journals Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

2021 ◽  
Vol 13 (24) ◽  
pp. 5074
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
David M. Johnson ◽  
Robert Seffrin ◽  
Brian Wardlow ◽  
...  
Keyword(s):  
Time Series ◽  
State Level ◽  
Growing Season ◽  
Absolute Difference ◽  
Corn Belt ◽  
Spatial And Temporal Variability ◽  
Field Scale ◽  
Near Surface ◽  
The Mean ◽  
Sentinel 2

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

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.

scholarly journals Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3524
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
W. Dean Hively
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Cover Crops ◽  
Cover Crop ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Agricultural Landscapes ◽  
Absolute Difference ◽  
Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

scholarly journals ROTATION FORESTS AND RANDOM FOREST CLASSIFIERS FOR MONITORING OF VEGETATION IN PAYS DE BREST (FRANCE)

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 727-732
Author(s):  
S. Niculescu ◽  
J. Xia ◽  
D. Roberts ◽  
A. Billey
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Random Forest ◽  
Canonical Correlation ◽  
Growing Season ◽  
Radar Images ◽  
Using Data ◽  
Source Of Information ◽  
Forest Rotation ◽  
Sentinel 2

Abstract. Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. The latest advances, in particular the arrival of new image acquisition programs, are changing the temporal approach to monitoring vegetation. The latest European satellites launched, delivering an image every 5 days for each point on the globe, allow the end of a growing season to be monitored. The main objective of this work is to identify and map the vegetation in the Pays de Brest area by using a multi sensors stacking of Sentinel-1 and Sentinel-2 satellites data via Random Forest, Rotation forests (RoF) and Canonical Correlation Forests (CCFs). RoF and CCF create diverse base learners using data transformation and subset features. Twenty four radar images and optical dataa representing different dates in 2017 were processed in time series stacks. The results of RoF and CCF were compared with the ones of RF.

scholarly journals Improved soil moisture estimation with Sentinel-1 for arable land at the field scale

2021 ◽  
Author(s):  
John Beale ◽  
Toby Waine ◽  
Ron Corstanje ◽  
Jonathan Evans
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Mean Absolute Error ◽  
Arable Land ◽  
Growing Season ◽  
Absolute Error ◽  
Temporal Analysis ◽  
Spectral Unmixing ◽  
Field Scale ◽  
The Mean

<p>The change detection method of multi-temporal analysis is often used to estimate surface soil moisture from Sentinel-1 SAR data. The underlying assumptions that vegetation cover and soil surface roughness vary significantly more slowly with time than soil moisture are problematic in areas under cultivation, which are characterised by seasonal cycles of rapid crop growth, senescence, harvesting and tillage. The issue becomes more acute when data is processed at the field scale. Other areas, where the vegetation cover is persistently high, also exhibit poor sensitivity of SAR backscatter to soil moisture. In general, the mean absolute error appears to be related to the relative fractions of photosynthetically active and inactive vegetation, and bare soil. Optical indices derived from Sentinel-2 data may be used with spectral unmixing to estimate these fractions as time series at field scale. Combined with knowledge of land use, confidence levels may be assigned to each field. The soil moisture may then be estimated by two dimensional interpolation using inverse distance squared weighting across a range of neighbouring fields within a local zone.  During the peak growing season, the mean absolute error in the soil moisture estimate for wheat fields is significantly reduced, in one example from around 20% volumetric water content to less than 5%. This will benefit users of such products in agriculture, for example, in determining actual soil moisture deficit in the growing season.</p>

scholarly journals Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model

2020 ◽  
Vol 12 (22) ◽  
pp. 3773
Author(s):  
Rahul Raj ◽  
Bagher Bayat ◽  
Petr Lukeš ◽  
Ladislav Šigut ◽  
Lucie Homolová
Keyword(s):  
Time Series ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temporal Variations ◽  
Forest Stand ◽  
Evergreen Forest ◽  
Landsat 8 ◽  
Data Set ◽  
Scope Model ◽  
Sentinel 2

Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.

scholarly journals Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel

2019 ◽  
Vol 11 (14) ◽  
pp. 1710 ◽  
Author(s):  
V.S. Manivasagam ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
Transformation Model ◽  
Absolute Difference ◽  
Surface Reflectance ◽  
Vegetation Monitoring ◽  
Blue Band ◽  
Spectral Angle Mapper ◽  
Transformation Functions ◽  
Sentinel 2

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 provides global coverage, whereas VENμS covers selected regions, including parts of Israel. To facilitate the combination of these sensors into a unified time-series, a transformation model between them was developed using imagery from the region of interest. For this purpose, same-day acquisitions from both sensor types covering the surface reflectance over Israel, between April 2018 and November 2018, were used in this study. Transformation coefficients from VENμS to Sentinel-2 surface reflectance were produced for their overlapping spectral bands (i.e., visible, red-edge and near-infrared). The performance of these spectral transformation functions was assessed using several methods, including orthogonal distance regression (ODR), the mean absolute difference (MAD), and spectral angle mapper (SAM). Post-transformation, the value of the ODR slopes were close to unity for the transformed VENμS reflectance with Sentinel-2 reflectance, which indicates near-identity of the two datasets following the removal of systemic bias. In addition, the transformation outputs showed better spectral similarity compared to the original images, as indicated by the decrease in SAM from 0.093 to 0.071. Similarly, the MAD was reduced post-transformation in all bands (e.g., the blue band MAD decreased from 0.0238 to 0.0186, and in the NIR it decreased from 0.0491 to 0.0386). Thus, the model helps to combine the images from Sentinel-2 and VENμS into one time-series that facilitates continuous, temporally dense vegetation monitoring.

scholarly journals Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1597
Author(s):  
Shangharsha Thapa ◽  
Virginia E. Garcia Millan ◽  
Lars Eklundh
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Vegetation Phenology ◽  
Change Analysis ◽  
Near Surface ◽  
Holistic Understanding ◽  
Sentinel 2

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

scholarly journals Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images

2020 ◽  
Vol 12 (13) ◽  
pp. 2140 ◽  
Author(s):  
Tianwei Ren ◽  
Zhe Liu ◽  
Lin Zhang ◽  
Diyou Liu ◽  
Xiaojie Xi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Classification Accuracy ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Maize Production ◽  
Seed Supply ◽  
Real Time Identification ◽  
Sentinel 2

Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

scholarly journals Comparison of Convective Boundary Layer Characteristics from Aircraft and Wind Lidar Observations

2019 ◽  
Vol 36 (7) ◽  
pp. 1381-1399 ◽  
Author(s):  
Bianca Adler ◽  
Olga Kiseleva ◽  
Norbert Kalthoff ◽  
Andreas Wieser
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Vertical Velocity ◽  
Convective Boundary Layer ◽  
Absolute Difference ◽  
Convective Boundary ◽  
Prevailing Wind Direction ◽  
Wind Lidar ◽  
The Mean ◽  
Aircraft Data

AbstractDuring the Convective Storm Initiation Project experiment, which was conducted in summer 2005 in southern England, vertical velocity in the convective boundary layer (CBL) was measured simultaneously with a research aircraft and a wind lidar. The aircraft performed horizontal flight legs approximately parallel to the prevailing wind direction and centered over the lidar. This measurement setup allows for the comparing of CBL characteristics (CBL depth zi, integral length scale lw, spectral peak wavelength λm, and vertical velocity variance ) from temporal (lidar) and spatial (aircraft) measurements. For this, the lidar time series are transferred into space using the mean wind. While the statistics of the aircraft data are all based on the 34-km flight legs, the averaging interval for the lidar is either 1 h or a longer period that corresponds to the 34-km leg. Although the lw and λm values from aircraft and lidar measurements are in the same range (100–200 and 500–2000 m) and agree well on the average, the correlation for individual legs is very low (R2 < 0.17). One possible explanation is the large uncertainty that arises from the transfer of the lidar time series to space. For , the agreement between aircraft and lidar is better for individual legs (R2 ≥ 0.63), but the mean absolute difference in is about 2.5 times as large as the statistical error. We examine the nonstationarity and heterogeneity for the lidar and aircraft samples and can exclude these as the major sources for the large differences between lidar and aircraft data.

scholarly journals Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

2018 ◽  
Vol 9 (2) ◽  
pp. 879-894 ◽  
Author(s):  
Ragnhild Bieltvedt Skeie ◽  
Terje Berntsen ◽  
Magne Aldrin ◽  
Marit Holden ◽  
Gunnar Myhre
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Deep Ocean ◽  
Mean Value ◽  
Transient Climate Response ◽  
Surface Temperature Change ◽  
Near Surface ◽  
The Mean

Abstract. Inferred effective climate sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. In our main analysis, the mean value of the estimated ECSinf is 2.0 ∘C, with a median value of 1.9 ∘C and a 90 % credible interval (CI) of 1.2–3.1 ∘C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. The transient climate response (TCR) is estimated to have a mean value of 1.4 ∘C (90 % CI 0.9–2.0 ∘C), and in our main analysis the posterior aerosol effective radiative forcing is similar to the range provided by the IPCC. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep-ocean OHC or using only one OHC dataset (instead of four in the main analysis) both give an enhancement of the mean ECSinf by about 50 % from our best estimate.

Sign in / Sign up

Export Citation Format

Share Document