Quantitative Monitoring of Complete Rice Growing Seasons Using Sentinel 2 Time Series Images

2018 ◽  
Author(s):  
Emma Madigan ◽  
Yiqing Guo ◽  
Mark Pickering ◽  
Alex Held ◽  
Xiuping Jia
Keyword(s):  
Time Series ◽  
Growing Seasons ◽  
Quantitative Monitoring ◽  
Time Series Images ◽  
Sentinel 2

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

scholarly journals Contribution of free satellite time-series images to mapping plant communities in the Mediterranean Natura 2000 site: the example of Biguglia Pond in Corse (France)

2020 ◽  
Vol 41 (2) ◽  
pp. 181-191
Author(s):  
Sébastien Rapinel ◽  
Clémence Rozo ◽  
Pauline Delbosc ◽  
Frédéric Bioret ◽  
Jan-Bernard Bouzillé ◽  
...  
Keyword(s):  
Time Series ◽  
Plant Communities ◽  
Conservation Status ◽  
Natura 2000 ◽  
Natural Habitats ◽  
Biogeographical Region ◽  
The Mediterranean ◽  
Natura 2000 Site ◽  
Time Series Images ◽  
Sentinel 2

Mapping plant communities, which is essential to assess the conservation status of natural habitats, is currently based mainly on time-consuming field surveys without the use of satellite data. However, free image time-series with high spatial and temporal resolution have been available since 2015. This study assessed the contribution of Sentinel-2 time-series images to mapping the spatial distribution of 18 plant communities within a Natura 2000 site (1978 ha) located on the Mediterranean biogeographical region (Corsica, France). The method was based on random forest modeling of six Sentinel-2 images acquired from 26 February to 24 October 2017, which were calibrated and validated using a field vegetation map. The results showed that the 18 plant communities were modeled correctly, with 72% overall accuracy. The uncertainty map associated with the model indicated areas that required additional field observations.

scholarly journals Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information

2021 ◽  
Vol 13 (14) ◽  
pp. 2790
Author(s):  
Hongwei Zhao ◽  
Sibo Duan ◽  
Jia Liu ◽  
Liang Sun ◽  
Louis Reymondin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Resolution ◽  
Missing Information ◽  
Type Mapping ◽  
Crop Type ◽  
Crop Types ◽  
Optical Imagery ◽  
Time Series Images ◽  
Sentinel 2

Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always needed, which will increase the workload and the risk of error transmission, especially for imagery with high spatial resolution. The development of optical imagery with high temporal and spatial resolution and the emergence of deep learning algorithms provide solutions to this problem. Although the one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) models have been used to classify crop types in previous studies, their ability to identify crop types using optical TSD with missing information needs to be further explored due to their different mechanisms for handling invalid values in TSD. In this research, we designed two groups of experiments to explore the performances and characteristics of the 1D CNN, LSTM, GRU, LSTM-CNN, and GRU-CNN models for crop type mapping using unfilled Sentinel-2 (Sentinel-2) TSD and to discover the differences between unfilled and filled Sentinel-2 TSD based on the same algorithm. A case study was conducted in Hengshui City, China, of which 70.3% is farmland. The results showed that the 1D CNN, LSTM-CNN, and GRU-CNN models achieved acceptable classification accuracies (above 85%) using unfilled TSD, even though the total missing rate of the sample values was 43.5%; these accuracies were higher and more stable than those obtained using filled TSD. Furthermore, the models recalled more samples on crop types with small parcels when using unfilled TSD. Although LSTM and GRU models did not attain accuracies as high as the other three models using unfilled TSD, their results were almost close to those with filled TSD. This research showed that crop types could be identified by deep learning features in Sentinel-2 dense time series images with missing information due to clouds or cloud shadows randomly, which avoided spending a lot of time on missing information reconstruction.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series

2021 ◽  
Vol 13 (2) ◽  
pp. 205
Author(s):  
Philipp Hochreuther ◽  
Niklas Neckel ◽  
Nathalie Reimann ◽  
Angelika Humbert ◽  
Matthias Braun
Keyword(s):  
Time Series ◽  
Observation Interval ◽  
Lake Area ◽  
Climate Data ◽  
Melt Pond ◽  
Automated Processing ◽  
Average Observation ◽  
Average Size ◽  
Lake Size ◽  
Sentinel 2

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

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