Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review DOI Open Access
Roxana Vidican, Anamaria Mălinaş, Ovidiu RANTA

et al.

Published: Nov. 15, 2023

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, need for climate-resilient systems, resource inequity, and protection the small-scale farmers’ practices, all issues integral to security environmental health. Remote sensing technologies can assist precision agriculture effectively address these complex problems, by providing farmers with high-resolution lens. use vegetation indices (VIs) essential component remote sensing, which combine variability spectral reflectance value (derived from data) growth stage crops. Currently wide array VIs available that could be used provide classification evaluation state health However precisely this high number leads difficulties in selecting best VI combination specific objective. Without thorough documentation analysis appropriate VIs, users might confronted using data or even very low accuracy results. Thus, objective review conduct critical existing art on most important features related effective discrimination monitoring crops (wheat, corn, sunflower, soybean, rape, potatoes, forage crops), grasslands meadows. This highly useful stakeholders involved activities (from farmers, researchers up institutions dealing centralization crops).

Language: Английский

A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images DOI
Hui Chen, Huapeng Li,

Zhao Liu

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 295, P. 113679 - 113679

Published: June 15, 2023

Language: Английский

Citations

42
Recent soybean subsidy policy did not revitalize but stabilize the soybean planting areas in Northeast China DOI
Yuanyuan Di, Nanshan You, Jinwei Dong

et al.

European Journal of Agronomy, Journal Year: 2023, Volume and Issue: 147, P. 126841 - 126841

Published: April 14, 2023

Language: Английский

Citations

26
Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images DOI
Yufeng Peng, Bingwen Qiu, Zhenghong Tang

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 303, P. 114016 - 114016

Published: Jan. 26, 2024

Language: Английский

Citations

11
Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review DOI Creative Commons
Roxana Vidican, Anamaria Mălinaş, Ovidiu RANTA

et al.

Agronomy, Journal Year: 2023, Volume and Issue: 13(12), P. 3040 - 3040

Published: Dec. 12, 2023

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient systems, resource inequity, and the protection small-scale farmers’ practices. These issues are integral to security environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. use vegetation indices (VIs) essential component remote sensing, which combines variability spectral reflectance value (derived from data) growth stage crops. A wide array VIs be used classify crops evaluate their state However, precisely this high number leads difficulty selecting best VI combination specific objectives. Without thorough documentation analysis appropriate VIs, users might find it difficult data or obtain results very low accuracy. Thus, objective review conduct critical existing art on effective discrimination monitoring several important (wheat, corn, sunflower, soybean, rape, potatoes, forage crops), grasslands meadows. This could highly useful all stakeholders involved activities. current has shown that appear suitable mapping crops, meadows pastures. Sentinel-1 Sentinel-2 were most utilized sources, while some frequently EVI, LAI, NDVI, GNDVI, PSRI, SAVI. In studies, needed employed achieve good prediction yields. main using related variation characteristics during period similarities signatures various semi-natural further studies establish models satellite would prove have greater accuracy provide more relevant information efficient

Language: Английский

Citations

21
Mapping annual 10-m soybean cropland with spatiotemporal sample migration DOI Creative Commons
Hongchi Zhang,

Zihang Lou,

Dailiang Peng

et al.

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 2, 2024

Abstract China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering collating ground survey data different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This uses feature space six integrals from crops’ phenological curves concavity-convexity index to distinguish non-soybean samples cropland. Using limited number actual our method, we extracted features optical time-series images throughout growing season. cloud rain-affected were supplemented with SAR data. then used random forest algorithm classification. Consequently, developed 10-meter resolution ChinaSoybean10 maps ten primary soybean-producing provinces 2019 2022. map showed an overall accuracy about 93%, aligning significantly statistical yearbook data, confirming reliability. research aids growth monitoring, yield estimation, strategy development, resource management, scarcity mitigation, promotes sustainable agriculture.

Language: Английский

Citations

8
Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine DOI Creative Commons

Hongwei Qi,

Ximin Qian,

Songhao Shang

et al.

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: Feb. 2, 2024

Accurate acquisition of spatial and temporal distribution information for cropping systems is important agricultural production food security. The challenges extracting about in regions with smallholder farms are considerable, given the varied crops, complex patterns, fragmentation cropland frequent reclamation abandonment. This study presents a specialized workflow to solve this problem farms, which utilizes field samples Sentinel-2 data extract system over multiple years. involves four steps: 1) processing simulate crop growth curves Savitzky‒Golay filter computing feature variables classification, including phenology indices, spectral bands, time series vegetation indices; 2) mapping annual croplands one-class support vector machine; 3) various single cropping, intercropping, double harvest, fallow by decision tree K-means clustering; 4) crops random forest where Jeffries-Matusita distance was used select appropriate indices. applied Hetao irrigation district Inner Mongolia Autonomous Region, China from 2018 2021. overall accuracies were 0.98, 0.96, 0.97 cropland, type mapping, respectively. results indicated that area has low continuity dominated patterns. Furthermore, wheat cultivation decreased, vegetable expanded. Overall, proposed facilitated accurate demonstrated effectiveness available imagery classifying on Google Earth Engine.

Language: Английский

Citations

7
A lightweight CNN-Transformer network for pixel-based crop mapping using time-series Sentinel-2 imagery DOI
Yumiao Wang, Luwei Feng, Weiwei Sun

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109370 - 109370

Published: Aug. 28, 2024

Language: Английский

Citations

7
From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2 DOI Creative Commons
Bingwen Qiu,

Duoduo Lin,

Chongcheng Chen

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 113, P. 103006 - 103006

Published: Sept. 1, 2022

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking smallholder farmer systems. Mapping national-scale remains challenging due to the spectral confusion with abandoned vegetated land, their high heterogeneity over large areas. This study proposed large-area mapping framework automatically identifying by fusing Vegetation-Soil-Pigment indices Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were highlighted consistently higher values cropping activities. The VSPS algorithm was exploited China without regional adjustments using Sentinel-2 Sentinel-1 images. Agriculture illustrated great has experienced tremendous changes such as non-grain orientation cropland abandonment. Yet, little known about locations extents cultivated field crops scale. Here, we produced first 20 m map fallow/abandoned found that 77 % (151.23 million hectares) 2020. We mountainous hilly regions far more than expected, which significantly underestimated commonly applied VImax-based approach based MODIS method illustrates robust generalization capabilities, obtained an overall accuracy 94 4,934 widely spread reference sites. capable detecting full consideration diversity systems complexity cropland. processing codes Google Earth Engine hoped stimulate operational agricultural finer resolution from

Language: Английский

Citations

26
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021 DOI Creative Commons
Qinghang Mei,

Zhao Zhang,

Jichong Han

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(7), P. 3213 - 3231

Published: July 10, 2024

Abstract. Soybean, an essential food crop, has witnessed a steady rise in demand recent years. There is lack of high-resolution annual maps depicting soybean-planting areas China, despite China being the world's largest consumer and fourth-largest producer soybean. To address this gap, we developed novel Regional Adaptation Spectra-Phenology Integration method (RASP) based on Sentinel-2 remote sensing images from Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select specific spectra indices that differentiate soybeans most effectively other crops across regions. These features were then input for unsupervised classifier (K-means), likely type was determined by cluster assignment dynamic time warping (DTW). For first time, generated dataset with high spatial resolution 10 m, spanning 2017 2021 (ChinaSoyArea10m). The R2 values between mapping results census at both county prefecture levels consistently around 0.85 2017–2020. Moreover, overall accuracy field level 2017, 2018, 2019 77.08 %, 85.16 86.77 respectively. Consistency improved (R2 increased 0.53 0.84) compared existing m crop-type Northeast (Crop Data Layer, CDL) samples supervised classification methods. ChinaSoyArea10m very spatially consistent two datasets (CDL GLAD (Global Land Analysis Discovery) maize–soybean map). provides important information sustainable soybean production management as well agricultural system modeling optimization. can be downloaded open-data repository (DOI: https://doi.org/10.5281/zenodo.10071427, Mei et al., 2023).

Language: Английский

Citations

6
Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms DOI Creative Commons
Nada Mzid, Olfa Boussadia, Rossella Albrizio

et al.

Agronomy, Journal Year: 2023, Volume and Issue: 13(3), P. 716 - 716

Published: Feb. 27, 2023

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development semiarid regions. objective this study was to achieve best estimation electrical conductivity variables from salt-affected soils south Mediterranean region using Sentinel-2 multispectral imagery. In order realize goal, test carried out (EC) data collected central Tunisia. Soil leaf were measured an olive orchard over two growing seasons under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, vegetation indices tested experimental area estimate both EC imagery on Google Earth Engine platform. Subsequently, models calibrated by employing machine learning (ML) techniques 12 bands images. prediction accuracy assessed k-fold cross-validation computing statistical metrics. results revealed that algorithms, together with data, could advance mapping conductivity.

Language: Английский

Citations

11