Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

Abstract High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained in other regions on typical satellite features, such as those from optical sensors, often exhibit low performance when transferred. Here we explore the use of NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles are capable of reliably distinguishing maize, a crop typically above 2m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84\%, and able to transfer across regions with accuracies higher than 82\% compared to 64\% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps.

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Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Remote Sensing ◽

10.3390/rs12172708 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2708 ◽

Cited By ~ 1

Author(s):

Qi Wang ◽

Jiancheng Li ◽

Taoyong Jin ◽

Xin Chang ◽

Yongchao Zhu ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Vegetation Indices ◽

Optical Data ◽

Landsat 8 ◽

Different Sources ◽

Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions

Remote Sensing ◽

10.3390/rs11050542 ◽

2019 ◽

Vol 11 (5) ◽

pp. 542 ◽

Cited By ~ 22

Author(s):

Laura Stendardi ◽

Stein Karlsen ◽

Georg Niedrist ◽

Renato Gerdol ◽

Marc Zebisch ◽

...

Keyword(s):

Time Series ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Optical Data ◽

Radar Signal ◽

Acquisition Time ◽

Soil Conditions ◽

Mountain Regions ◽

Sentinel 2

A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.

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Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Remote Sensing ◽

10.3390/rs13040700 ◽

2021 ◽

Vol 13 (4) ◽

pp. 700

Author(s):

Daniel Kpienbaareh ◽

Xiaoxuan Sun ◽

Jinfei Wang ◽

Isaac Luginaah ◽

Rachel Bezner Kerr ◽

...

Keyword(s):

Land Cover ◽

Temporal Resolution ◽

Satellite Data ◽

Radar Data ◽

Agricultural Landscapes ◽

Optical Data ◽

Smallholder Farming ◽

Crop Type ◽

Crop Types ◽

Sentinel 2

Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.

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In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification

Agriculture ◽

10.3390/agriculture9010017 ◽

2019 ◽

Vol 9 (1) ◽

pp. 17 ◽

Cited By ~ 5

Author(s):

Md. Shahinoor Rahman ◽

Liping Di ◽

Eugene Yu ◽

Chen Zhang ◽

Hossain Mohiuddin

Keyword(s):

Image Classification ◽

Crop Rotation ◽

Ground Truth ◽

The United States ◽

Classification Models ◽

Landsat Images ◽

Ground Truth Data ◽

Rotation Pattern ◽

Crop Type ◽

Crop Types

Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification.

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Hamas and Civil Society in Gaza

10.23943/princeton/9780691159676.001.0001 ◽

2013 ◽

Cited By ~ 13

Author(s):

Sara Roy

Keyword(s):

United States ◽

Political Violence ◽

West Bank ◽

Gaza Strip ◽

Social Institutions ◽

The United States ◽

Great Promise ◽

Religious Congregations ◽

Regional Dynamics ◽

Second Intifada

Many in the United States and Israel believe that Hamas is nothing but a terrorist organization, and that its social sector serves merely to recruit new supporters for its violent agenda. Based on extensive fieldwork in the Gaza Strip and West Bank during the critical period of the Oslo peace process, this book shows how the social service activities sponsored by the Islamist group emphasized not political violence but rather community development and civic restoration. The book demonstrates how Islamic social institutions in Gaza and the West Bank advocated a moderate approach to change that valued order and stability, not disorder and instability; were less dogmatically Islamic than is often assumed; and served people who had a range of political outlooks and no history of acting collectively in support of radical Islam. These institutions attempted to create civic communities, not religious congregations. They reflected a deep commitment to stimulate a social, cultural, and moral renewal of the Muslim community, one couched not only—or even primarily—in religious terms. Vividly illustrating Hamas's unrecognized potential for moderation, accommodation, and change, the book also traces critical developments in Hamas' social and political sectors through the Second Intifada to today, and offers an assessment of the current, more adverse situation in the occupied territories. The Oslo period held great promise that has since been squandered. This book argues for more enlightened policies by the United States and Israel, ones that reflect Hamas' proven record of nonviolent community building. A new afterword discusses how Hamas has been affected by changing regional dynamics and by recent economic and political events in Gaza, including failed attempts at reconciliation with Fatah.

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Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽

10.3390/agronomy11010035 ◽

2020 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

Xiaodong Huang ◽

Beth Ziniti ◽

Michael H. Cosh ◽

Michele Reba ◽

Jinfei Wang ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Canopy Cover ◽

The United States ◽

Optical Data ◽

Study Region ◽

Model Based ◽

Key Indicator ◽

Ground Measurement ◽

L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

Environmental Sciences Proceedings ◽

10.3390/iecg2020-07916 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Negar Tavasoli ◽

Hossein Arefi

Keyword(s):

Machine Learning ◽

Carbon Stock ◽

Vegetation Indices ◽

Forest Biomass ◽

Principal Component ◽

Optical Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Texture Characteristics ◽

Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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Structural and optical properties of amorphous Si–Ge–Te thin films prepared by combinatorial sputtering

Scientific Reports ◽

10.1038/s41598-021-91138-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

C. Mihai ◽

F. Sava ◽

I. D. Simandan ◽

A. C. Galca ◽

I. Burducea ◽

...

Keyword(s):

Optical Properties ◽

Data Storage ◽

Optical Sensors ◽

Material Selection ◽

Functional Materials ◽

Stability Study ◽

Optical Data Storage ◽

Optical Data ◽

Structural And Optical Properties ◽

Topological Constraint

AbstractThe lack of order in amorphous chalcogenides offers them novel properties but also adds increased challenges in the discovery and design of advanced functional materials. The amorphous compositions in the Si–Ge–Te system are of interest for many applications such as optical data storage, optical sensors and Ovonic threshold switches. But an extended exploration of this system is still missing. In this study, magnetron co-sputtering is used for the combinatorial synthesis of thin film libraries, outside the glass formation domain. Compositional, structural and optical properties are investigated and discussed in the framework of topological constraint theory. The materials in the library are classified as stressed-rigid amorphous networks. The bandgap is heavily influenced by the Te content while the near-IR refractive index dependence on Ge concentration shows a minimum, which could be exploited in applications. A transition from a disordered to a more ordered amorphous network at 60 at% Te, is observed. The thermal stability study shows that the formed crystalline phases are dictated by the concentration of Ge and Te. New amorphous compositions in the Si–Ge–Te system were found and their properties explored, thus enabling an informed and rapid material selection and design for applications.

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An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping

Science of Remote Sensing ◽

10.1016/j.srs.2021.100018 ◽

2021 ◽

Vol 3 ◽

pp. 100018 ◽

Cited By ~ 1

Author(s):

Xiao-Peng Song ◽

Wenli Huang ◽

Matthew C. Hansen ◽

Peter Potapov

Keyword(s):

Modis Data ◽

Type Mapping ◽

Crop Type ◽

Sentinel 2

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Private Wealth, Philanthropy, and Social Development

The China Nonprofit Review ◽

10.1163/18765149-12341314 ◽

2016 ◽

Vol 8 (2) ◽

pp. 215-248 ◽

Cited By ~ 1

Author(s):

Francis Barchi ◽

Guosheng Deng ◽

Chien-Chung Huang ◽

Carolyn Isles ◽

Juliann Vikse

Keyword(s):

Social Development ◽

Charitable Giving ◽

The United States ◽

Educational Opportunities ◽

Great Promise ◽

Income Gap ◽

Supportive Environment ◽

Performance Based Funding ◽

Opportunity Gaps

Growing income inequality has created excessive barriers to social mobility for the poor. Charitable giving by the wealthy can reduce the societal effects of the income gap by reinvestment in public goods such as education. We apply the cipp model to investigate the role of philanthropy in advancing educational opportunities in the United States and China, using historical and contemporary cases of individual philanthropists. The findings suggest that the cases reviewed in this study created mechanisms by which to contribute to social development in their respective countries. Government policies play a key role in philanthropic development, particularly in China. It is important to continue to foster a supportive environment for the sector to grow, which may include expanding tax incentives, emphasizing performance-based funding, and eliminating administrative barriers. In short, educational philanthropy holds great promise for reducing opportunity gaps and economic inequality, by providing the key to success for future generations.

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