Agricultural Systems, Год журнала: 2025, Номер 227, С. 104338 - 104338
Опубликована: Апрель 6, 2025
Язык: Английский
Agricultural Systems, Год журнала: 2025, Номер 227, С. 104338 - 104338
Опубликована: Апрель 6, 2025
Язык: Английский
Remote Sensing of Environment, Год журнала: 2023, Номер 295, С. 113714 - 113714
Опубликована: Июль 18, 2023
Язык: Английский
Процитировано
80IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2024, Номер 62, С. 1 - 11
Опубликована: Янв. 1, 2024
Solar-induced
chlorophyll
fluorescence
(SIF)
shows
potential
in
exploring
plant
responses
to
environmental
changes
caused
by
extreme
climatic
factors.
However,
how
accurately
assess
climate
stresses
(especially
the
low-temperature
stress)
suffered
on
crops
at
regional
scale
a
systematic
approach
has
not
been
extensively
explored.
In
this
study,
we
developed
vegetation
stress
index
(CVSI)
and
quantify
impacts
of
large
scales
combining
TROPOspheric
Monitoring
Instrument
(TROPOMI)
SIF
land
surface
temperature
(LST)
data
through
an
easy-to-operate
approach.
This
was
employed
identify
conditions
Henan
Province's
winter
wheat
2018.
Results
indicate
that,
influenced
characteristics,
northern
part
Province
experienced
more
severe
than
those
southern
part.
The
daily
average
values
reductions
0.74,
0.45,
0.61,
0.86
mW
Язык: Английский
Процитировано
36International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 127, С. 103643 - 103643
Опубликована: Янв. 9, 2024
Corn is the dominant crop planted in Northeast China, and its accurate timely mapping important for food security agricultural management China. However, absence of enough labels challenging corn a regional area using machine learning methods or deep methods. In this study, an efficient way automatic labelling areas by combining Global Ecosystem Dynamics Investigation (GEDI) data Sentinel-2 images proposed. We explore height vertical structure differences between other crops derived from GEDI features generate automatically referencing type products transferring models historical years. The trained networks points decision trees Random Forest (RF) classifier can be transferred to arbitrary target are combined perform wall-to-wall random forest algorithm GEDI-based labels. This approach used map China 2019 2022, classification results validated independent collected field campaigns 2023, published maps, official statistics. Our reveal that our proposed method achieves high accuracy robustness with average overall 0.91 testing spatial-type stratified sampling. correlation coefficient (R2) classified result statistical reach 0.96 0.98, respectively. These demonstrate potential label collection vegetation difference provide new on large-scale.
Язык: Английский
Процитировано
13Remote Sensing, Год журнала: 2024, Номер 16(2), С. 277 - 277
Опубликована: Янв. 10, 2024
Timely and accurate rice spatial distribution maps play a vital role in food security social stability. Early-season mapping is of great significance for yield estimation, crop insurance, national policymaking. Taking Tongjiang City Heilongjiang Province with strong heterogeneity as study area, hierarchical K-Means binary automatic classification method based on phenological feature optimization (PFO-HKMAR) proposed, using Google Earth Engine platform Sentinel-1/2, Landsat 7/8 data. First, SAR backscattering intensity time series reconstructed used to construct optimize polarization characteristics. A new index named VH-sum built, which defined the summation VH specific periods temporal changes characteristics different land cover types. Then comes selection, optimization, reconstruction optical Finally, PFO-HKMAR established Simple Non-Iterative Clustering. can achieve early-season one month before harvest, overall accuracy, Kappa, F1 score reaching 0.9114, 0.8240 0.9120, respectively (F1 greater than 0.9). Compared two datasets Northeast China ARM-SARFS, scores are improved by 0.0507–0.1957, 0.1029–0.3945, 0.0611–0.1791, respectively. The results show that be promoted enable mapping, provide valuable timely information stakeholders decision makers.
Язык: Английский
Процитировано
10Remote Sensing, Год журнала: 2024, Номер 16(8), С. 1376 - 1376
Опубликована: Апрель 13, 2024
Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather all-day imaging capability to supply dense observations in early crop season. This study applied local window attention transformer (LWAT) time-series SAR data, including RCM classification. The performance this integration was evaluated over crop-dominated regions (corn, soybean wheat) southwest Ontario, Canada. Comparative analyses against several machine learning deep methods revealed superiority LWAT, achieving an impressive F1-score 97.96% a Kappa coefficient 97.08% northern region F1-scores 98.07% 97.02% southern when leveraging from respectively. Additionally, by incremental procedure, evolution accuracy determined Sentinel-1 analyzed, which demonstrated that performed better at beginning season could achieve comparable achieved utilizing both datasets. Moreover, stem elongation corn identified crucial phenological stage acquire acceptable maps explores potential provide reliable prior information enough assist in-season production forecasting decision making.
Язык: Английский
Процитировано
9ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 213, С. 14 - 32
Опубликована: Май 25, 2024
Язык: Английский
Процитировано
9Scientific Data, Год журнала: 2024, Номер 11(1)
Опубликована: Май 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.
Язык: Английский
Процитировано
8Remote Sensing of Environment, Год журнала: 2024, Номер 314, С. 114387 - 114387
Опубликована: Авг. 28, 2024
Язык: Английский
Процитировано
7Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 109949 - 109949
Опубликована: Янв. 23, 2025
Язык: Английский
Процитировано
1Remote Sensing, Год журнала: 2025, Номер 17(6), С. 978 - 978
Опубликована: Март 11, 2025
Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on large scale. The method combines unsupervised supervised machine learning algorithms. To the accuracy regression model, used K-Means algorithm in conjunction with morphological operations identify growth areas. Input data consisted monthly NDVI from Sentinel-2 VH bands Sentinel-1 (covering year 2021). results were validated 221 field survey samples an F1 score 0.95. monitor disease severity, we compared seven models: CART decision trees (CART), Gradient Tree Boosting (GTB), Random Forest (RF), single optical Time series model (TS–RF), radar (STS–RF), multi-source (MSTS–GTB), (MSTS–RF). MSTS–RF was best performer, validation RMSE 20.50 R² 0.71. input spectral indices (NDVI, NDWI, NDBI, etc.), features (VH-band VV-band), texture features, synthesized as time May September 2021. feature importance analysis highlights key identification: NIR band (B8) Sentinel-2, DVI, SAVI, Sentinel-1. Notably, blue (458–523 nm) critical during month May. These are related vegetation health soil moisture early detection. presents first large-scale map distribution China 10 m 26.52. provides valuable support agricultural management, demonstrating effectiveness practical potential proposed monitoring.
Язык: Английский
Процитировано
1