AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

Magno, Ramona; Rocchi, Leandro; Dainelli, Riccardo; Matese, Alessandro; Gennaro, Salvatore Filippo Di; Chen, Chi Farn; Son, N. T.; Toscano, Piero

doi:10.3390/rs13061219

Cited by 16 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloudy weather causes the multi-spectral instrument of Sentinel-2 to capture the reflectance of clouds. As a result, the true reflectance of the land’s surface is not captured, and the tile is characterized by foggy pixels [ 66 ]. Foggy pixels have very high reflectance relative to the land’s surface reflectance.…”

Section: Methodsmentioning

confidence: 99%

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Khan

Gillani

Jamal

et al. 2023

Sensors

View full text Add to dashboard Cite

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

show abstract

Section: Methodsmentioning

confidence: 99%

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Khan

Gillani

Jamal

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…For the identification of cloud shadows, the concepts obtained in Magno et al (2021), the "Cloud Shadow Index" -CSI, systematized in Equation 1, and filtered at the thresholds of interest for identifying shadows, thanks to the specific behavior of the "Shortwave Infrared" band of the "Multispectral Instrument" sensor…”

Section: Spatial Filtering Of Scenesmentioning

confidence: 99%

“…The product developed with the expertise of Magno et al (2021) provides, within the threshold of interest, excellent accuracy in the selection of shaded regions. As found by Magno et al (2021), the shaded regions could be identified, by recording the spectral behavior of the dry and wet threshold.…”

Section: Spatial Filtering Of Scenesmentioning

confidence: 99%

Determination of the critical threshold for the occurrence of fires in the Parque Nacional de Brasília (Brazil) through temporal analysis using spectral índices

Silva¹,

Baptista²

2023

Sociedade &Amp; Natureza

View full text Add to dashboard Cite

The Brazilian Savannah is one of the largest biomes in Brazil. Unfortunately, human pressure is aggravating the degradation processes of the biome and, together with the drought processes, fire events are a major concern. Monitoring tools must be designed, especially those involving the concepts of remote sensing, in anticipation of the fire phenomenon, mitigating the devastating effects. Therefore, the present research aims to determine the critical threshold for the occurrence of fires through the diagnosis of the conditions of greenness, humidity and senescence of the vegetation using the temporal analysis of Sentinel-2 images in the Parque Nacional de Brasília. Therefore, it is necessary to quantitatively determine each condition through the analysis of past events, determining the criticality threshold that conditions the region to present conditions that favor the onset of a fire, as well as the construction of an algorithm, in the Google Earth Engine. The study area is the Parque Nacional de Brasília, with the image data collection Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C inside the Google Earth Engine. The algorithm aims to build a masking system to remove materials from the scenes, as well as the calculation of the NDVI, NDII, PSRI and dNBR indices, extracting the data through the mask of burned pixels. It was possible to identify six periods of fire occurrence, the data extraction allowed the statistical determination of the threshold, which was 0,580 for the NDVI, 0,015 for the NDII and 0,150 for the PSRI.

show abstract

“…This problem is globally present, not only for the ocean-linked subjects but for every study using satellite images, such as land monitoring [117,118] and forest monitoring [119,120]. Thus, several algorithms have been developed in the literature to face this issue [121][122][123][124][125][126][127][128]. One widely used algorithm for cloud and cloud shadow detection is Function of mask, known as Fmask, for images from Landsat and Sentinel-2 satellites [129][130][131].…”

Section: Clouds and Cloud Shadowsmentioning

confidence: 99%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

View full text Add to dashboard Cite

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

show abstract

AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

Cited by 16 publications

References 35 publications

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Determination of the critical threshold for the occurrence of fires in the Parque Nacional de Brasília (Brazil) through temporal analysis using spectral índices

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Contact Info

Product

Resources

About