|
china |
7 |
|
public health |
6 |
|
climate change |
5 |
|
remote sensing |
5 |
|
air pollution |
4 |
|
extreme weather |
4 |
|
human mobility |
4 |
|
urbanization |
4 |
|
classification |
3 |
|
cropland loss |
3 |
|
environmental kuznets curve |
3 |
|
google earth |
3 |
|
human settlements |
3 |
|
land use change |
3 |
|
land use classification |
3 |
|
landsat data |
3 |
|
machine learning |
3 |
|
modis |
3 |
|
rural development |
3 |
|
urban greenspace |
3 |
|
accuracy |
2 |
|
air pollution exposure |
2 |
|
area of interest |
2 |
|
assessment |
2 |
|
big data |
2 |
|
biodiversity and ecosystem functioning |
2 |
|
block-level mapping |
2 |
|
building scale |
2 |
|
china hj-1a |
2 |
|
city meta unit |
2 |
|
climate |
2 |
|
cloud removal |
2 |
|
comparison |
2 |
|
continuous cloud-free landsat images |
2 |
|
conversion coefficients |
2 |
|
cooling efficiency |
2 |
|
cropland fragmentation |
2 |
|
data fusion |
2 |
|
data model |
2 |
|
disease transmission |
2 |
|
duration from covid-19 onset to diagnosis confirmation |
2 |
|
dynamic assessment |
2 |
|
earth |
2 |
|
enrichment planting |
2 |
|
ensemble learning |
2 |
|
environmental exposure |
2 |
|
environmental health |
2 |
|
environmental inequality |
2 |
|
environmental injustice |
2 |
|
exposure assessment |
2 |
|
exposure risk |
2 |
|
forest degradation |
2 |
|
forest restoration |
2 |
|
geo-spatial big data |
2 |
|
geospatial big data |
2 |
|
greenspace exposure |
2 |
|
healthy city |
2 |
|
heat anomalies |
2 |
|
hierarchical intervention network |
2 |
|
hospital bed shortage |
2 |
|
human behavior |
2 |
|
imported coronavirus disease 2019 (covid-19) |
2 |
|
india |
2 |
|
international collaboration |
2 |
|
invariant similar pixels |
2 |
|
land cover classification |
2 |
|
land grid |
2 |
|
land parcel |
2 |
|
land use |
2 |
|
landsat |
2 |
|
mobile phone data |
2 |
|
mobile phone location data |
2 |
|
multifaceted associations |
2 |
|
naipsentinel-1/2 |
2 |
|
open big data |
2 |
|
pm 2.5 |
2 |
|
population-weighted exposure |
2 |
|
poyang lake |
2 |
|
pre-selection of temporal change |
2 |
|
prediction modes |
2 |
|
random forest |
2 |
|
reproductive number |
2 |
|
sabah biodiversity experiment |
2 |
|
sample collection |
2 |
|
satellite remote sensing |
2 |
|
satellites |
2 |
|
selective logging |
2 |
|
sparse representation |
2 |
|
spatial heterogeneity |
2 |
|
spatial resolution |
2 |
|
spatial-temporal-spectral-angular |
2 |
|
spatially and temporally weighted regression (stwr) |
2 |
|
spatiotemporal difference |
2 |
|
spatiotemporal fusion |
2 |
|
spatiotemporal heterogeneity |
2 |
|
spatiotemporal ndvi fusion |
2 |
|
sustainable development |
2 |
|
temporal and angular features |
2 |
|
thermal comfort |
2 |
|
thermal mitigation |
2 |
|
unified fusion |
2 |
|
urban environment |
2 |
|
urban land use |
2 |
|
urban land use type |
2 |
|
urban tree growth |
2 |
|
wetland cover change |
2 |
|
worldwide chinese |
2 |
|
xiamen |
2 |
|
aerosol optical depth (aod) |
1 |
|
almond |
1 |
|
almond orchard |
1 |
|
aod |
1 |
|
artificial intelligence |
1 |
|
bayes theorem |
1 |
|
bloom intensity |
1 |
|
california |
1 |
|
causality model |
1 |
|
central valley |
1 |
|
change detection |
1 |
|
china hj-1a ccd/hsi |
1 |
|
comparisons |
1 |
|
conceptual framework |
1 |
|
coronavirus disease 2019 |
1 |
|
coupled simulation model |
1 |
|
crop dynamics |
1 |
|
crop growth trend |
1 |
|
ctm |
1 |
|
data analysis |
1 |
|
data reconstruction |
1 |
|
data science |
1 |
|
decision tree |
1 |
|
difference-in-differences |
1 |
|
digital twin model |
1 |
|
disease duration |
1 |
|
disease x |
1 |
|
drought |
1 |
|
dynamic exposure |
1 |
|
epidemic |
1 |
|
epidemiology |
1 |
|
euluc |
1 |
|
field survey |
1 |
|
fine spatiotemporal resolution |
1 |
|
fire |
1 |
|
fire behavior |
1 |
|
fire management |
1 |
|
floodplain lakes |
1 |
|
forest loss |
1 |
|
forest types |
1 |
|
gan |
1 |
|
geospatial |
1 |
|
geostatistical analysis |
1 |
|
google earth engine |
1 |
|
greenspace change |
1 |
|
greenspace changes |
1 |
|
hantaan virus (htnv) |
1 |
|
hemorrhagic fever with renal syndrome (hfrs) |
1 |
|
heterogeneity changes mapping |
1 |
|
himawari-8 |
1 |
|
hydrological connectivity |
1 |
|
hyperspectral imagery (hsi) |
1 |
|
index |
1 |
|
indonesia |
1 |
|
interaction mechanism |
1 |
|
lake-floodplain system |
1 |
|
land concessions |
1 |
|
land use mapping |
1 |
|
landscape heterogeneity |
1 |
|
light interceptioon |
1 |
|
location-based data |
1 |
|
maximum entropy model |
1 |
|
meteorological condition |
1 |
|
mixed land use |
1 |
|
moderate resolution imaging spectroradiometer (modis) |
1 |
|
multi-scale remote sensing |
1 |
|
multiple cropping system |
1 |
|
ndvi |
1 |
|
net photosynthesis (psnnet) |
1 |
|
ningbo |
1 |
|
nitrogen management |
1 |
|
noise removal |
1 |
|
northern california |
1 |
|
npp-viirs |
1 |
|
nutrient management |
1 |
|
old and new urban area |
1 |
|
olympic sustainability |
1 |
|
parcel segmentation |
1 |
|
penalized linear discriminant analysis (plda) |
1 |
|
phenology |
1 |
|
planting year |
1 |
|
pois |
1 |
|
policy-driven moratorium |
1 |
|
principal components transformation |
1 |
|
prunus dulcis |
1 |
|
rodent population dynamics |
1 |
|
samples |
1 |
|
seasonal isolated lake |
1 |
|
segmentation |
1 |
|
social sensing |
1 |
|
spatial-hyperspectral fusion |
1 |
|
spatiotemporal pattern |
1 |
|
spectral unmixing |
1 |
|
statistical model |
1 |
|
super resolution |
1 |
|
surface water connectivity |
1 |
|
temporal linear unmixing |
1 |
|
time series |
1 |
|
time series analysis |
1 |
|
time-series ndvi |
1 |
|
timing of inundation |
1 |
|
unsupervised classification |
1 |
|
urban |
1 |
|
urban core |
1 |
|
urban human settlements |
1 |
|
urban planning |
1 |
|
urban sprawl |
1 |
|
urbanisation |
1 |
|
validation |
1 |
|
visible infrared imaging radiometer suite (viirs) |
1 |
|
water volume |
1 |
|
wetland cover changes |
1 |
|
wetland coverage |
1 |
|
wildfire |
1 |
|
wildland-urban interface (wui) |
1 |
|
yield gap |
1 |
|
yield prediction |
1 |
|
yield variation |
1 |
