Feng et al. (2025). Globally increased cropland soil exposure to climate extremes in recent decades

, Xiaoxin Zhang

8

, Jonas Ardö

7

, Jie Zhou

9

,

Wenxuan Shao

1

, Yujie Dou

1

, Yirong Sang

1

& Feng Tian

1,2,3,4

Soil quality is fundamental to nutrient-rich food production and the sustain-
ability of terrestrial ecosystems. However, inappropriate agricultural practices
often lead to persistent soil exposure to air and sunlight, which increases soil
organic matter losses and erosion risks, particularly under climate extremes.
Here, we provide a satellite-based mapping of daily soil exposure occurrence
across global croplands from 2001 to 2022 and evaluate the associated
degradation risks caused by extreme climate events. We

ﬁ

nd that while 57% of

global croplands experienced a reduction in soil exposure duration in the past
two decades, 86% are increasingly subjected to climate extremes. The areas
exposed to increasing climate extremes tend to have higher soil organic car-
bon levels, indicating an intensi

ﬁ

ed degradation risk of global nutrient-rich

cropland soils. Our study offers spatio-temporally explicit insights into global
cropland soil exposure and its vulnerability to climate extremes, providing
evidence to support improvements in sustainable agriculture practices.

Soil is the foundation for food production and carbon sequestration

–

.

Upon decades of accelerated human exploitation, soil quality has been
decreasing rapidly worldwide

, currently ranging from levels of only

fair to poor or even very poor

5

. Nearly half of global croplands suffer

from varying degrees of degradation, mainly due to improper agri-
cultural practices, among which conventional tillage often leave soil
directly exposed to sunlight and air during cropping phases of initial
growth, post-harvest, and fallowing

6

. The absence of protective

cover, such as vegetation canopy, crop residue, and snow cover,
increases the risk of soil erosion and losses of soil organic carbon and
microorganisms, particularly under climate extremes

9

.

To restore degraded soils and ensure agricultural sustainability, a

wide range of conservation practices, including reduced tillage, cover

cropping, and mulching, have been implemented through soil con-
servation projects such as the European Union

’

s Soil Strategy for 2030

and the Environmental Quality Incentives Program. Additionally, per-
ennial crops, by simulating the vegetation of natural ecosystems, are
expected to provide year-round protection for the soil and promote
the formation of soil organic matter

,

10

. The effectiveness of these

agricultural practices has been evaluated through site-based
studies

11

–

13

. At a broader scale, cropland soil exposure level has been

recognized as an indicator of soil health status

–

16

. However, the

accuracy of existing studies in capturing variations in soil exposure has
been constrained, particularly when cropping phases shift rapidly.
Furthermore, we still lack a quantitative, spatiotemporally continuous
evaluation of soil exposure across global cropland areas.

Received: 10 October 2024

Accepted: 25 April 2025

Check for updates

1

Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University,

Wuhan, China.

2

Perception and Effectiveness Assessment for Carbon-neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for

Carbon Neutrality, Wuhan University, Wuhan, China.

3

Hubei Luojia Laboratory, Wuhan University, Wuhan, China.

4

State Key Laboratory of Information

Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.

5

Department of Geography and Spatial Information Techniques,

Ningbo University, Ningbo, China.

6

Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.

7

Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.

8

Future Urbanity & Sustainable Environment (FUSE) Lab, Division

of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.

9

Key

Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University,
Wuhan, China.

e-mail:

tian.feng@whu.edu.cn

| (2025) 16:4354

1

12

3

4

5

6

78

9

0

()

:,;

12

3

4

5

6

7

8

9

0

()

:,;

Changes in frequency and intensity of climate extremes are

exerting substantial impacts on soil dynamics

17

. Heatwaves, char-

acterized by high air temperatures and land surface solar radiation
loads, can either accelerate soil microbial respiration or impair
microbial survival, resulting in faster carbon losses

18

19

. Heavy rainfall

and strong winds contribute to soil erosion, depleting topsoil and
nutrients

20

. These effects are especially pronounced under bare soil

conditions. The impact of climate extremes on soil conditions is
globally heterogeneous

17

21

. Therefore, a spatiotemporally continuous

assessment of soil degradation risks under climate extremes is needed
for ef

ﬁ

cient protection and management of global cropland soils.

Here, we quantify the daily bare soil fraction across global crop-

lands at a spatial resolution of 500 m using satellite observations from
Moderate Resolution Imaging Spectroradiometer (MODIS) and
Sentinel-2 (Methods, Supplementary Figs. 1

–

4). We further calculate

the annual duration of cropland soil exposure from 2001 to 2022,
based on which we evaluate temporal trends of soil degradation risks
due to direct exposure to climate extremes.

Results

Global pattern of cropland soil exposure duration

From 2001 to 2022, soils in global croplands were on average exposed
(i.e., left without any crops, crop residue, snow, or water cover,
Methods) for approximately 147 days per year (Figs.

a, b, and Sup-

plementary Data 1). About 5% of all cropland experiences soil exposure
exceeding eight months, predominantly located in the Sahel region of

Africa and the Great Indian Desert, with the longest durations of soil
exposure in Niger and Sudan, lasting 251 and 224 days, respectively.
(Fig.

a, and Supplementary Data 1). In contrast, around 12% of global

croplands are exposed for less than three months per year, mainly
found in America, Europe, Southeast Asia, and Northeastern South
Asia (Fig.

a). The Philippines and Indonesia in Southeast Asia show the

shortest cropland soil exposure periods, with average durations of 68
and 76 days, respectively (Supplementary Data 1). Among the top 10
countries by cropland area in 2022, Nigeria has the longest average soil
exposure duration at 189 days, while Canada has the shortest at
97 days (Fig.

c).

The distribution pattern intricately covaries with local climate

conditions (Fig.

d). In particular, croplands in arid and semi-arid cli-

mate zones tend to have longer periods of soil exposure, whereas
those in humid climates often experience shorter exposure duration.
The limited exposure duration in high-latitude regions, such as
Canada, the border area between Russia and Kazakhstan, and north-
eastern China, is largely due to prolonged snow cover, which can last
over three months (Supplementary Fig. 5a). We found a discernible
negative correlation between cropland soil exposure duration and the
length of the growing period reported by Food and Agriculture
Organization (Methods and Supplementary Fig. 6). This correlation is
less evident in conservation agriculture, where crop residues often
remain on the soil surface during the off-growing season, and in high-
latitude regions, where prolonged snow cover disrupts the relation-
ship (Methods, Supplementary Fig. 5a).

30 60 90 120 150 180 210 240 270

Mean soil exposure duration (days)

a

b

c

d

Arid

Semi-arid

Dry sub-humid

Humid

1 2 3 4 5
6 7 8 9 10

India

China

Russia

Brazil

Australia

Argentina

Canada

Ukraine

Nigeria

2

1

3

4

5

6

7

8

9

10

US

Other

Countries

30 60 90 120 150 180 210 240 270 300

2

10

20

24

19

13

7

4

1

Proportion(%)

Days

Fig. 1 | Average annual duration of cropland soil exposure during 2001

–

2022.

a

Global map of cropland soil exposure duration at 500 m resolution. Pixels

identi

ﬁ

ed as cropland for any given year are retained. The insert shows the dis-

tribution of cropland area proportions by exposure duration.

b

Schematic

representation of cropland soil exposure.

c

The cropland soil exposure duration

aggregated to the country level. We refer to Supplementary Data 1 for the complete
list.

d

The cropland soil exposure duration as a function of global aridity

61

.

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2

Global trends in cropland soil exposure during 2001

–

2022

About 57% of croplands experienced a reduction in annual soil expo-
sure duration from 2001 to 2022, with 23% of these reductions being
signi

ﬁ

cant (

p

< 0.05) (Fig.

a). The areas with the largest reductions in

exposure duration are mainly located in India, North America, and
China, which together account for more than half of the global
decrease in cropland exposure (Supplementary Data 1). India is parti-
cularly noteworthy, with 92% of its stable cropland areas undergoing
reduced soil exposure duration (65% signi

ﬁ

cant at

p

< 0.05), accounting

for over 20% of the world

’

s croplands with a shortened soil exposure

duration. Relative changes were calculated to assess the rate of change
in soil exposure duration in 2022 relative to 2001 (Methods, Eq.

11

). Asia

has experienced a relatively stable decline in cropland soil exposure
duration, declining by approximately 13 days (relative change =

−

8.31%)

from 2001 to 2022 (Fig.

b). Again, India recorded the most substantial

decrease, with a reduction of 25 days (relative change = 14.44%) over
the last two decades (Fig.

c), which may be driven by agriculture

intensi

ﬁ

cation during recent decades

,

22

.

On the other hand, about 43% of croplands saw an increase in

annual soil exposure duration, with 11% being statistically signi

ﬁ

cant

(

p

< 0.05). Notably, Eastern Europe exhibited a signi

ﬁ

cant increasing

trend in cropland exposure, accounting for approximately 20% of the
world

’

s cropland with prolonged soil exposure durations. In Africa,

cropland soil exposure is also apparent with a cumulative increase of
8 days, representing a relative change of 4.62%. This trend is

particularly pronounced in Uganda, Senegal, Chad, and Nigeria,
where more than 80% of cropland areas show increased soil exposure
duration. Speci

ﬁ

cally, Uganda experienced an average increase in soil

exposure duration of 28 days (relative change =26.20%) from 2001
to 2022.

We combined crop cover and crop residue cover, and determined

the annual duration of crop/crop residue cover by subtracting the
snow cover duration and soil exposure duration from the total number
of days in each year. Then, we compared the in

ﬂ

uence of this com-

bined cover with that of snow cover in determining soil exposure
across the Northern Hemisphere (Methods, Figs.

a, and Supplemen-

tary Fig. 5b, c). The increases in both snow and crop/crop residue
coverage have contributed to a reduction in cropland soil exposure in
northeastern China. In Canada, an increase in snow cover alongside a
decrease in crop/crop residue cover is observed. The impact of the
snow cover changes is particularly evident, leading to a marked
decrease in exposed cropland throughout the region (Supplementary
Figs. 5b, d). Conversely, in Nebraska and Kansas in the central U.S.,
there has been a decrease in snow cover coupled with an increase in
crop/crop residue cover. In these regions, the in

ﬂ

uence of crop/crop

residue is more pronounced, resulting in reduced exposure time in
most areas. Moreover, in humid regions, the area experiencing a
reduction in cropland soil exposure far exceeds that with an increase,
whereas in other aridity conditions, no notable differences are
observed (Fig.

d).

D·y-1

Proportion (%)

Trends in soil exposure duration

Decrease (p < 0.05)

Decrease (p ≥ 0.05)

Increase (p < 0.05)

Increase (p ≥ 0.05)

-5

0

5

23%

11%

57% 43%

a

b

c

d

Africa

Oceania

Asia

Europe

North America

South America

Fig. 2 | Trends in duration of cropland soil exposure during 2001

–

2022.

a

Global map of trends in cropland soil exposure duration at 500 m resolution.

Only pixels identi

ﬁ

ed as cropland for all years are retained for the trend analysis.

The insert shows the distribution of cropland area proportions with different
exposure trends.

b

The cropland soil exposure duration across six continents

during 2001

–

2022.

c

Share of global cropland for the trends in cropland soil

exposure duration among the top 10 countries by cropland area in 2022.

d

Share of

global cropland for the trends in cropland soil exposure duration among four
aridity conditions

61

.

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3

Increasing global cropland soil exposure to climate extremes

We calculated the number of extreme climate events (high tempera-
ture, high solar radiation, heavy rainfall, and strong wind) during
periods of exposed soil conditions to quantify the risk of soil degra-
dation (Methods). From 2001 to 2022, the extent of cropland soils
exposed to climate extremes have changed substantially (Fig.

).

Around 86% of global cropland areas have seen an increase in soil
exposure due to the various climate extremes examined during the
exposure periods (Fig.

a). Notably, 17% have experienced an increased

risk of exposure to all four types of climate extremes, with the most
affected regions located in the U.S. and Europe. Only 14% of global
cropland areas have seen a decrease in exposure to all four climate
extremes, primary located in northern India, northeastern China, and
Canada.

In particular, 73% of exposed soils of global croplands experi-

enced an increased frequency of high-temperature events from 2001
to 2022 (Fig.

b). The remaining 27% areas experiencing a decreased

frequency of high-temperature events are mainly located in India,

northeastern China, and Canada. On average, there has been an
increase of about four days experiencing extreme atmospheric heat to
exposed soils across global croplands (Supplementary Table 1). Areas
with decreased solar radiation exposure outweigh those with increases
by 6%, with notable increases in Europe, South America, and along the
border between the U.S. and Canada (Fig.

c). A similar trend is

observed with extreme rainfall, where areas with decreased exposure
outweigh those with increases by 10% (Fig.

d). The exposure to strong

wind is relatively balanced between increases and decreases, with
signi

ﬁ

cant increases along the Russia-Ukraine border and in Sudan,

Africa (Fig.

e).

Higher soil exposure to climate extremes in SOC-rich croplands

Cropland soil quality varies substantially worldwide, with higher-
quality soils offering enhanced productivity, greater soil microbiome
diversity, and superior carbon sequestration capabilities

5

. We used soil

organic carbon (SOC) as a proxy for soil quality due to its close asso-
ciation with soil fertility, water-holding capacity, and structure

23

–

25

. We

b

Trends in high temperature during soil exposure

c

Trends in high solar radiation during soil exposure

d

Trends in heavy rainfall during soil exposure

e

Trends in strong wind during soil exposure

Decrease (p < 0.5)

Decrease (p ≥ 0.5)

Increase (p < 0.5)

Increase (p ≥ 0.5)

49%

7%

42%

11%

40%

51%

45%

55%

7% 38%

10%

45%

73%

27%

3% 24%

20%

53%

47%

53%

9%

38%

12%

41%

a

Number of climate factors with increasing trends in extreme events during soil exposure

0

1

2

17%

25%

14%

3

4

27%

17%

Number of climate factors

Fig. 3 | Trends in cropland soil exposure to climate extremes during
2001

–

2022. a

Number of climate factors with increasing trends in extreme events

during soil exposure

. b

–

e

Trends in cropland soil exposed to high temperature,

high solar radiation, heavy rainfall, and strong wind, respectively. The inserts show
the areal proportion of cropland areas with increased and decreased exposure to
climate extremes.

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| (2025) 16:4354

4

then analyzed soil exposure-induced degradation risks in global
croplands. Global cropland SOC content typically ranges from 2 to 9 kg
m

−

2

, with an average value of 4.8 kg m

−

2

(Supplementary Fig. 7)

26

.

As expected, our results show higher SOC content in croplands

with shorter exposure duration (Fig.

a). Notably, SOC content was

signi

ﬁ

cantly higher in croplands where soil exposure increased from

2001 to 2022, compared to those with a decreased soil exposure
(Fig.

b). The average difference in SOC between areas with signi

ﬁ

cant

increases and decreases in soil exposure is relative low (0.46 kg m

−

2

,

Glass

’

s Delta = 0.28). However, even small changes in SOC (0.1 kg m

−

2

y

−

1

) were found to be correlated with increased food grain production

(32 billion kg y

−

1

)

27

. Moreover, cropland soils exposed to increased

climate extreme conditions all have signi

ﬁ

cantly higher SOC levels

(Welch

’

s t-test,

p

< 0.05) than those exposed to decreased climate

extreme conditions, except for heavy rainfall (Fig.

c). This indicates

that more croplands experienced a decrease than an increase in soil
exposure duration, but it did not necessarily correspond to a reduced
risk of SOC loss. When considering regions with signi

ﬁ

cant trends in

exposure-induced risks, the differences in SOC between areas with
increased and decreased soil exposure to high solar radiation and
heavy wind are even larger. Therefore, global cropland soils possibly
experienced an increased SOC loss due to heightened exposure to
climate extremes over the past two decades.

Discussion

Soil is the foundation for life on Earth. In 1994, the United Nations
Convention to Combat Deserti

ﬁ

cation (UNCCD) was founded to pro-

tect and restore Earth

’

s land, following two high-level UN conferences

in 1977 and 1992 on land degradation and deserti

ﬁ

cation. The

ﬁ

rst task

is to assess where and how severely Earth

’

s land has degraded. Satellite

remote sensing provides suitable, global, and spatiotemporally con-
sistent data for the assessment. Current remote sensing-based studies
use changes in vegetation greenness as a proxy for land degradation
assessment, and in the past decades, satellite observations show
that the Earth is greening due to climate change and land
management

,

. However, land degradation is about soil quality

rather than vegetation greenness, and ground surveys show that soil
quality has declined due to extensive agriculture practices

30

. Instead

of assessing vegetation greenness, our study mapped bare soil fraction
in global croplands as a proxy for land degradation risk. Also, daily
estimation of soil exposure is essential for accurately determining bare
land duration and exposure risks to climate extremes, as both land
management and weather conditions can change rapidly over time.

We produced a global, daily, and wall-to-wall map of cropland soil

exposure over the past two decades and reported an increased crop-
land soil degradation risk due to direct exposure to more frequent
climate extremes during 2001

–

2022, which has not been documented

**

Trends in climate extremes

during soil exposure

Decrease (p < 0.05)

Increase (p < 0.05)

Increase

Decrease

a

b

c

**

Δ=0.18

Δ=0.28

Δ=0.24

Δ=0.38

Δ=0.64

Δ=1.00

Δ=0.34

Δ=0.99

Δ=0.14

Δ=0.20

Fig. 4 | Statistics on soil organic carbon (SOC) for areas grouped by different
cropland soil exposure conditions. a

SOC grouped by the average annual dura-

tion of soil exposure.

b

SOC grouped by positive and negative trends in soil

exposure during 2001

–

2022.

c

SOC grouped by positive and negative trends in soil

exposure to climate extremes from 2001

–

2022. In boxplots, the horizontal lines,

from bottom to top, represent the minimum,

ﬁ

rst quartile, median, third quartile,

and maximum values, respectively. The width of the boxes in each box plot
represents the number of pixels. ** indicates that the differences in SOC between
groups were tested using Welch

’

s t-test, with

p

< 0.05.

Δ

represents that the mag-

nitude of difference between groups were tested using Glass

’

s Delta.

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| (2025) 16:4354

5

before. India, in particular, showed a marked decrease in soil exposure
regarding spatial extent and temporal duration, consistent with

ﬁ

nd-

ings of a strong greening trend

,

22

. The reduction in soil exposure is

largely attributed to agricultural intensi

ﬁ

cation, characterized by

heightened cropping frequency and density

31

32

. This trend is likely to

be substantiated by the rise in global population and food demand/
production

33

34

. U.S. has experienced a substantial decrease in cropland

soil exposure, potentially attributable to the widespread adoption of
cover crops since 2005

35

. For example, Southern Pennsylvania has

experienced an increase of the proportion of winter cover crops from
2010 to 2013, driven by ongoing efforts to promote their adoption

36

.

Concurrently, the duration of cropland soil exposure has decreased
(Supplementary Fig. 8), re

ﬂ

ecting the positive impact of these initia-

tives. In addition to the U.S., many high-income countries have already
begun to integrate cover crops into their farming practices to address
soil erosion, nutrient leakage, control pests and diseases, and boost
carbon storage

37

38

. Beyond cover cropping, perennial grain agriculture

also holds potential as an effective means to reduce soil disturbance
and degradation

. Additionally, changes in snow cover also affect the

exposure of soils in croplands at higher latitudes (Figs.

a, and Sup-

plementary Fig. 5b). While a general decrease in snow cover in the
Northern Hemisphere has resulted in an increase in bare land

39

40

, there

are notable exceptions, such as the crop-growing areas in Canada and
Northeast China, where an increase in snow cover has correspondingly
decreased soil exposure.

Soil exposure in croplands appears to be in

ﬂ

uenced by a country

’

s

level of economic development and degree of political stability. We
observed a substantial difference in the duration of soil exposure
across an area within approximately 10 km of the Turkey

–

Syria border

(Supplementary Figs. 9a, b). The difference observed is likely asso-
ciated with the different levels of economic development between the
regions. Speci

ﬁ

cally, the prolonged con

ﬂ

ict in Syria has severely

damaged agricultural infrastructure, including irrigation systems

41

.

The variance of annual cropland soil exposure in Syria (std=17.93) is
higher than that of Turkey (std=15.42) (Supplementary Fig. 9c). This
difference may be attributed to the less developed agricultural prac-
tices in Syria to mitigate climate conditions.

In the context of ongoing climate change, more cropland soils

have experienced escalated risks of degradation (Fig.

a). Extreme heat

has emerged as the primary threat to bare soils in croplands over the
past two decades (Fig.

b). Only 14% of all global cropland areas remain

unaffected by increases in any of four climate extremes examined
during soil exposure. These regions are primarily located in northern
India, northeastern China, which have witnessed substantial reduc-
tions in soil exposure (Fig.

a). Yet, climate extremes, including heat-

waves and extreme precipitation, are projected to increase with
ongoing global warning

42

,

43

. With extreme rainfall projected to inten-

sify further in India

44

, the risk of soil exposure to heavy rainfall is likely

to increase accordingly.

Cropland soil quality exhibits widespread global variations

Consequently, the potential damage caused by soil exposure has
varying impacts on the global food supply and environmental health.
Speci

ﬁ

cally, damage to higher-quality soils is likely to cause greater

crop yield and soil carbon losses compared to lower-quality soils. Our
analysis indicates that regions with increasing trends in cropland soil
exposure tend to feature higher SOC levels. Additionally, areas with
pronounced risks from climate extremes also show higher SOC values
(Fig.

b, c). Therefore, despite more croplands worldwide have

experienced reduced soil exposure compared to increased soil expo-
sure (Fig.

a), the overall risk of cropland degradation has not neces-

sarily decreased, and crucial functions of croplands such as carbon
sequestration and nutrient-rich food production may be adversely
affected.

Although maintaining cover over cropland soils can mitigate the

detrimental effects of direct exposure to air and sunlight, the methods

of coverage are crucial, as they potentially introduce additional risks.
For example, the observed reduction in cropland soil exposure in the
U.S. is likely attributable to the implementation of cover crop strate-
gies, which are expected to retain soil structure, reduce soil erosion,
and enhance soil health. Conversely, in India, the current reduction has
primarily been achieved through intensive farming, which, para-
doxically, may exacerbate soil health deterioration

. However, the

current method only captures the changes in cropland soil exposure,
without identifying the underlying causes. Future research should
investigate the speci

ﬁ

c drivers of cropland soil exposure reduction,

allowing for a comprehensive assessment of the risks faced by
cropland soils.

Methods

Overview

We introduced a methodology for daily soil exposure mapping across
global croplands using satellite imagery (Supplementary Fig. 1). Initi-
ally, we selected 34 regions globally, each measuring 0.2° × 0.2°,
characterized by extensive cropland coverage. We then applied Linear
Spectral Mixture Analysis (LSMA)

45

to generate multi-temporal bare

soil fraction data utilizing Sentinel-2-based Normalized Difference
Vegetation Index (NDVI) and Normalized Difference Tillage Index
(NDTI) time series. The bare soil fraction data, serving as ground truth,
was then resampled to align with the spatial resolution of MODIS
imagery. Subsequently, a linear regression model correlating resam-
pled bare soil fraction data with MODIS-derived NDVI and NDTI was
developed. Employing this model, we mapped daily bare soil fraction
from 2001 to 2022. We excluded dates with snow or water cover by
thresholding Normalized Difference Snow Index (NDSI) and Auto-
mated Water Extraction Index (AWEI)

46

, thereby obtaining precise soil

exposure conditions in croplands. We further calculated the annual
duration of soil exposure for the same period to assess the temporal
trends of potential risks associated with exposure to climate extremes.
Additionally, we used SOC as a proxy for soil quality and explored the
variations in exposure risks across croplands with differing soil
qualities.

The extent of cropland area

The cropland extent was generated from a 30 m time-series global
cropland mapping product, comprising

ﬁ

ve layers at four-year

intervals from 2000 to 2019

47

. We then aggregated the 30 m reso-

lution product to 0.0045° by classifying pixels as cropland if more
than 50% of the original pixels represent cropland. Our study spans
from 2001 to 2022, and for each speci

ﬁ

c year, we utilized the layer

with the nearest year as a mask to derive the cropland extent. For
trend analysis, only pixels identi

ﬁ

ed as cropland during all years were

retained. For other analyses, pixels identi

ﬁ

ed as cropland for any

given year were retained.

Preparation of satellite imagery

We used Sentinel-2 and MODIS satellite imagery to map soil exposure
across global croplands. Speci

ﬁ

cally, MOD09GA data

48

spanning from

2001 to 2022 were included in the study, and the state_1km quality
band was employed to minimize the in

ﬂ

uence from cloud contamina-

tion. The data were originally in a sinusoidal projection at a 500 m
spatial resolution, and were converted into a WGS84 geographical
projection at a 0.0045° spatial resolution (~500 m × 500 m at Equator).
Additionally, Sentinel-2 Level-2A data

49

, which is provided in the Uni-

versal Transverse Mercator projection, was used in this study for the
period from 2019 to 2021. To ensure high quality data, the Scene
Classi

ﬁ

cation Layer was utilized to eliminate cloud, snow, and water,

with further cloud removal achieved by setting the probability
threshold at 50% using the Sentinel-2 Cloud Probability product.

Using the imagery, we calculated four indices, namely NDVI, NDTI,

NDSI, and AWEI, known for effectively characterizing the conditions of

Article

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| (2025) 16:4354

6

crops, crop residue, snow, and water, respectively, using Eqs. (

–

).

NDVI =

NIR

RED

NIR + RED

ð

1

Þ

NDTI =

SWIR1

SWIR2

SWIR1 + SWIR2

ð

2

Þ

NDSI =

GREEN

SWIR1

GREEN + SWIR1

ð

3

Þ

AWEI = 4 × GREEN

SWIR1

ð

Þ

0

:

25 × NIR + 2

:

75 × SWIR2

ð

Þ

ð

4

Þ

Selection of representative regions for model building

To enhance the robustness of the bare soil fraction estimation model
across space and crop types, we selected 34 regions globally (Sup-
plementary Data 2), each spanning 0.2° × 0.2°. These regions were
chosen for their high cropland density and considerable variation in
dominant crop types, with the assistance of multiple crop type data.
These data include the Cropland Data Layer (CDL) for 2020 released by
the United States Department of Agriculture (USDA)

50

, the Agriculture

and Agri-Food Canada (AAFC) crop type digital maps for 2020

51

, the

ESA WorldCereal product for 2021

52

, the ESA WorldCover data for

2020

53

, and crop type maps in Northeast China for 2019

54

.

Estimation of bare soil fraction with MODIS-based NDVI
and NDTI

Using data from the 34 representative regions, we developed a bare
soil fraction estimation model using MODIS-based NDVI and NDTI.
Two main steps were involved: calculation of alternatives to ground
truth bare soil fraction using Sentinel-2 data, and construction of
MODIS-based bare soil fraction estimation model.

Due to the dif

ﬁ

culty of obtaining ground measurements for bare

soil fraction values, we generated 10-meter resolution bare soil frac-
tion data from Sentinel-2 imagery using LSMA as alternatives to
ground truth. LSMA assumes that the observed spectrum of a pixel is a
linear combination of the spectra of different pure components,
referred to as endmembers, present within that pixel. In cropland
ecosystems, endmembers mainly refer to crops, crop residue, and bare
soils. In each region, we randomly selected 1500 Sentinel-2 pixels and
excluded non-dominant crop areas based on the aforementioned crop
type data (Supplementary Data 2). Then, we extracted the NDVI and
NDTI values for the remaining points using the 5-day median compo-
sites of Sentinel-2 imagery corresponding to the years of the crop type
data. The valid data from all the remaining points across all timestamps
were aggregated to derive the NDVI-NDTI matches for each region.
Based on the triangular space method (Supplementary Fig. 2), we
constructed a two-dimensional scatter plot, commonly referred to as
the triangular space, using NDVI-NDTI matches for each region. Within
each plot, we visually determined the vertices of the triangular space
(Supplementary Fig. 3), which symbolize pure pixels of crops, crop
residue, and bare soils. The values of NDVI and NDTI corresponding to
the vertices were taken as the spectral features of endmembers in each
region (Supplementary Data 2). Finally, the Sentinel-2-based bare soil
fraction could be determined by LSMA using Eq. (

5

):

NDVI

m

= CF × NDVI

c

+ CRF × NDVI

cr

+ BSF × NDVI

bs

NDTI

m

= CF × NDTI

c

+ CRF × NDTI

cr

+ BSF × NDTI

bs

CF + CRF + BSF = 100%, 0

≤

CF

≤

100%, 0

≤

CRF

≤

100%, 0

≤

BSF

≤

100%

ð

5

Þ

where NDVI

m

and NDTI

m

represent the NDVI and NDTI values of a

mixed pixel. NDVI

c

, NDVI

cr

, and NDVI

bs

are the NDVI values of crop,

crop residue, and bare soil endmembers, respectively. NDTI

c

, NDTI

cr

,

and NDTI

bs

are the NDTI values of crop, crop residue, and bare soil

endmembers, respectively. CF, CRF, and BSF denote the proportions
of crops, crop residue, and bare soils in a mixed pixel.

In the second step, we aim to formulate the bare soil fraction

estimation model using MODIS-based NDVI and NDTI. To achieve this,
we

ﬁ

rst computed the cloud proportion in 5-day median composites of

MODIS and Sentinel-2 imagery for each selected region spanning from
2019 to 2021, identifying timestamps when the cloud cover was below
50%. Then, we generated the MODIS-based NDVI and NDTI for the
chosen timestamps of these regions. And the Sentinel-2-based bare soil
fraction data was resampled to 0.0045° by averaging aggregation.
Within each region, 1000 points were randomly generated, and
MODIS-based NDVI and NDTI values were extracted at each timestamp
as independent variables, with corresponding resampled bare soil
fraction values extracted as dependent variables. Multiple linear
regression was used to model the relationship between bare soil
fraction and MODIS-based NDVI and NDTI. To enhance the reliability of
our results, we employed the leave-one-out cross-validation strategy.
In this approach, the data from each of the 34 selected regions is
individually used as the test set in a single iteration, with the data from
the other regions serving as the training set. The resulting regression
coef

ﬁ

cients and accuracy for each iteration are detailed in Supple-

mentary Data 3 and Supplementary Fig. 10. By averaging the 34 sets of
regression coef

ﬁ

cients, we derived the regression equation for mod-

eling the global bare soil fraction (Eq.

6

). Using the equation, the

ﬁ

nal

accuracy was calculated by incorporating all data from the 34 selected
regions, resulting in coef

ﬁ

cient of determination (R

2

), root mean

squared error (RMSE), and mean absolute error (MAE) of 0.73,13.61%,
and 10.69%, respectively.

BSF = 36 × NDVI

268 × NDTI + 90

ð

6

Þ

Spatiotemporal mapping of cropland bare soil

To acquire the spatiotemporal distribution of bare soils in croplands,
non-cropland pixels were excluded using the previously mentioned
resampled cropland extent. Then, we extracted the daily values of
NDVI, NDTI, NDSI, and AWEI

46

during 2001

–

2022 from MODIS ima-

gery. Due to the impact of cloud cover and atmospheric perturbations,
satellite observations frequently exhibit discontinuities. We therefore
employed Harmonic Analysis of Time Series (HANTS)

55

–

57

, a time series

reconstruction algorithm, to

ﬁ

t the four indices of each pixel based on

the annually available observations, ensuring the generation of tem-
porally continuous data. Employing principles reminiscent of Fourier
analysis, HANTS is able to decompose complex time series datasets
into distinct harmonic components, each representing a unique fre-
quency or periodicity. HANTS can be expressed by the Eq. (

).

y

t

j

=

a

0

+

X

N

i

= 1

a

i

sin 2

π

f

i

t

j

+

b

i

cos 2

π

f

i

t

j

h

i

ð

7

Þ

where

y

t

j

represents the reconstructed value of pixel

j

at time

t

.

a

0

is

the coef

ﬁ

cient at zero frequency.

a

i

and

b

i

are coef

ﬁ

cients of the tri-

gonometric components associated with the frequencies

f

i

.

N

is the

number of harmonic components and is set to 4 in this study. After
employing HANTS to generate annually reconstructed time series of
NDVI, NDTI, NDSI, and AWEI, the daily bare soil fraction for each pixel
from 2001 to 2022 was computed based on Eq. (

6

). It is worth noting

that, due to winter snow cover in high latitudes, we used NDSI to
identify snow for areas north of 35°N from November to March. Pixels
with NDSI values above 0.4 were considered as snow, leading to a
corresponding bare soil fraction value of 0. For areas north of 35°N
from April to October and in other regions where the soil in rice cul-
tivation regions is periodically covered by water, we used AWEI for

Article

https://doi.org/10.1038/s41467-025-59544-1

| (2025) 16:4354

7

water identi

ﬁ

cation. Pixels with AWEI values exceeding 0 were desig-

nated as water-covered, resulting in the corresponding bare soil frac-
tion being set to 0. Utilizing the method described above, we
calculated the daily bare soil fraction for global cropland pixels span-
ning from 2001 to 2022 (Supplementary Fig. 4). We determined the
annual duration of cropland soil exposure by aggregating the bare soil
fraction values for all days of the year. We further calculated the
average annual duration of cropland soil exposure and analyzed trends
in exposure duration using the slopes of linear regression equations.

Quantifying the risk of exposure to climate extremes

An extreme climate event is de

ﬁ

ned as a day with a meteorological

variable value surpassing the 90th percentile

58

between 2001 and

2022. In this study, we considered four meteorological variables,
namely mean daily surface temperature, mean daily surface solar
radiation downwards, daily total rainfall, and mean daily wind speed.
These meteorological variables were extracted from ERA5-Land, a
global land reanalysis dataset provided by the European Centre for
Medium-Range Weather Forecasts

59

. The dataset provides hourly land

surface variables on a regular grid with a spatial resolution of
0.1° × 0.1°, and it was further resampled to a spatial resolution of
0.0045° using the nearest neighbor method.

We quanti

ﬁ

ed the risk of exposure to climate extremes by evalu-

ating the length of time each year that cropland soil was exposed to
climate extremes, as shown in Eq. (

).

R

i

,

y

,

t

=

X

D

y

d

= 1

F

i

,

y

,

t

d

E

i

,

y

,

t

d

ð

8

Þ

where

R

i

,

y

,

t

represents the risk of exposure characterized by extreme

weather

t

of pixel

i

in year

y

.

D

y

is the number of days in year

y

, and

d

is

the day of year (DOY).

F

d

denotes the bare soil fraction on DOY

d

.

E

d

denotes whether extreme climate events occurred on DOY

d

, with

values of either 0 or 1. The trends in risk of exposure were considered
to be linear and were represented by the slopes.

Quantifying the relationship between SOC and exposure

We used the SOC content obtained from SoilGrids version 2.0

26

to

represent the quality of cropland soils. The original data, with a spatial
resolution of 250 m, was converted to a geographic resolution of
0.0045° by averaging aggregation to match the spatial resolution of
MODIS-based bare soil fraction data. We investigated the relationship
between SOC content and the average annual duration of cropland soil
exposure. Additionally, we compared SOC content across regions with
increased versus decreased soil exposure durations and between
regions with heightened versus reduced exposure-induced risks.

Statistics by country

We conducted a statistical analysis of six continents and the top 50
countries of the largest cropland areas in 2022 (Supplementary Data 1).
This analysis encompasses a range of critical indicators, including the
average annual exposure duration (Eq.

9

), the average change in

exposure duration (Eq.

10

), the average relative change in exposure

duration (Eq.

11

), the area with decreased exposure (Eq.

12

), the area

with increased exposure duration (Eq.

13

), the proportion of areas with

decreased exposure (Eq.

https://lwfeng.users.earthengine.app/

), the proportion of areas with increased

exposure (Eq.

15

).

Average annual exposure duration =

P

n

i

= 1

A

i

D

i

P

n

i

= 1

A

i

ð

9

Þ

Average change in exposure duration =

X

n

s

i

= 1

T

i

A

i

N

ð

10

Þ

Average relative change in exposure duration =

P

n

i

= 1

A

i

^

D

2022

P

n

i

= 1

A

i

^

D

2001

P

n

i

= 1

A

i

^

D

2001

ð

11

Þ

Area with decreased exposure =

X

n

i

= 1

A

i

ð

12

Þ

Area with increased exposure =

X

n

+

i

= 1

A

i

ð

13

Þ

Proportion of areas with decreased exposure =

P

n

i

= 1

A

i

P

n

i

= 1

A

i

ð

14

Þ

Proportion of areas with increased exposure =

P

n

+

i

= 1

A

i

P

n

i

= 1

A

i

ð

15

Þ

where

A

i

is the area of pixel

i

,

D

i

is the average annual exposure

duration of pixel

i

.

^

D

2001

and

^

D

2022

represent the predicted exposure

durations for 2001 and 2022 using the linear regression

. T

is the trend,

expressed as the slope of the linear regression.

N

represents the years

from 2001 to 2022, which is set to 21.

n

,

n

s

,

n

−

, and

n

+

represent the

number of cropland pixels, the number of stable cropland pixels, the
number of pixels with decreased exposure, and the number of pixels
with increased exposure, respectively.

Analysis of the impact of snow cover on cropland soil exposure

We explored the impact of snow cover on cropland exposure duration
at higher latitudes (north of 35°N) in the Northern Hemisphere using
the aforementioned MODIS-based NDSI time series data processed
with the HANTS algorithm. Pixels with NDSI values above 0.4 were
considered to be snow-covered. We calculated the annual duration of
snow cover from 2001 to 2022. The annual duration of crop/crop
residue cover was then determined by subtracting the snow cover
duration and soil exposure duration from the total number of days in
each year. We calculated the correlation between the durations of
these two types of cover and the soil exposure duration, identifying
the dominant factor as the one with the larger absolute correlation
value (Supplementary Fig. 5d).

Correlating bare soil fraction data with growing period data

We used the length of the growing period data, published by Food and
Agriculture Organization

60

, to correlate with our assessment of the

duration of soil exposure. Length of the growing period is character-
ized as the number of days when the average daily temperature
remains above 5 °C and the soil moisture is suf

ﬁ

cient to support crop

germination and growth. Data from 2001 to 2010 was used because the
values prior to 2010 were derived from historical meteorological data,
whereas values after 2010 were based on predicted meteorological
data. The estimated average annual duration of cropland soil exposure
during 2001

–

2010 was resampled to match the spatial resolution of

the length of the growing period data at 5 arc-minute using averaging
aggregation. A correlation analysis of the two datasets was performed
along the gradient of latitude (Supplementary Fig. 6).

Data availability

The data on annual cropland soil duration and trends are available in
Google

’

s Earth Engine App at

view/cropland-soil-exposure-explorer

. The daily bare soil fraction in

croplands for 2022 is available at

https://doi.org/10.5281/zenodo.

14553703

.

Article

https://doi.org/10.1038/s41467-025-59544-1

https://doi.org/10.5281/zenodo.14553644

| (2025) 16:4354

8

Code availability

The code for time series data generation and bare soil fraction calcu-
lation is available at

.

References

1.

Amundson, R. et al. Soil and human security in the 21st century.

Science

348

, 1261071 (2015).

2.

Montanarella, L. et al. World

’

s soils are under threat.

Soil

2

, 79

–

82

(2016).

3.

Rumpel, C. et al. The role of soil carbon sequestration in enhancing
human resilience in tackling global crises including pandemics.

Soil

Security

8

, 100069 (2022).

4.

Borrelli, P. et al. An assessment of the global impact of 21st century
land use change on soil erosion.

Nat. Commun.

8

, 2013 (2017).

5.

Montanarella, L. et al.

Status of the World

’

s Soil Resources (SWSR)

–

Main Report

. Food and Agriculture Organization of the United

Nationsand Intergovernmental Technical Panel on Soils, Rome, Italy
(FAO and ITPS, 2015).

https://openknowledge.fao.org/server/api/

core/bitstreams/6ec24d75-19bd-4f1f-b1c5-5becf50d0871/content

.

6.

FAO.

The State of the World

’

s Land and Water Resources for Food

and Agriculture

–

Systems at breaking point

. (FAO, 2021).

7.

Montgomery, D. R. Soil erosion and agricultural sustainability.

Proc.

Natl Acad. Sci.

104

, 13268

–

13272 (2007).

8.

Crews, T. E., Carton, W. & Olsson, L. Is the future of agriculture
perennial? imperatives and opportunities to reinvent agriculture by
shifting from annual monocultures to perennial polycultures.

Glob.

Sustainability

1

, e11 (2018).

9.

Smith, P. Soils and climate change.

Curr. Opin. Environ. sustain-

ability

4

, 539

–

544 (2012).

10. Crews, T. E. & Rumsey, B. E. What agriculture can learn from native

ecosystems in building soil organic matter: a review.

Sustainability

9

, 578 (2017).

11.

He, Y. T. et al. Long-term combined chemical and manure fertili-
zations increase soil organic carbon and total nitrogen in aggregate
fractions at three typical cropland soils in China.

Sci. Total Environ.

532

, 635

–

644 (2015).

12. Sleutel, S. et al. Carbon stock changes and carbon sequestration

potential of Flemish cropland soils.

Glob. Change Biol.

9

, 1193

–

1203

(2003).

13. Garnier, J. et al. Storage or loss of soil active carbon in cropland

soils: the effect of agricultural practices and hydrology.

Geoderma

407

, 115538 (2022).

14. Demattê, J. A. M. et al. Bare Earth

’

s surface spectra as a proxy for soil

resource monitoring.

Sci. Rep.

10

, 4461 (2020).

15. Mzid, N., Pignatti, S., Huang, W. & Casa, R. An analysis of bare soil

occurrence in arable croplands for remote sensing topsoil appli-
cations.

Remote Sens.

13

, 474 (2021).

16. Diek, S., Fornallaz, F., Schaepman, M. E. & De Jong, R. Barest pixel

composite for agricultural areas using Landsat time series.

Remote

Sens.

9

, 1245 (2017).

17. Wang, M. et al. Responses of soil organic carbon to climate

extremes under warming across global biomes.

Nature Climate

Change

14

, 98

–

105 (2023).

18. Hartley, I. P., Hill, T. C., Chadburn, S. E. & Hugelius, G. Temperature

effects on carbon storage are controlled by soil stabilisation capa-
cities.

Nat. Commun.

12

, 6713 (2021).

19. Joergensen, R. G., Brookes, P. C. & Jenkinson, D. S. Survival of the

soil microbial biomass at elevated temperatures.

Soil Biol. Biochem.

22

, 1129

–

1136 (1990).

20. Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate

change.

Nat. Rev. Microbiol.

18

, 35

–

46 (2020).

21. Wang, M. et al. Global soil pro

ﬁ

les indicate depth-dependent soil

carbon losses under a warmer climate.

Nat. Commun.

13

, 5514

(2022).

22. Chen, C. et al. China and India lead in greening of the world through

land-use management.

Nat. sustainability

2

, 122

–

129 (2019).

23. Haefele, S. M., Nelson, A. & Hijmans, R. J. Soil quality and constraints

in global rice production.

Geoderma

235

, 250

–

259 (2014).

24. Shukla, M. K., Lal, R. & Ebinger, M. Determining soil quality indica-

tors by factor analysis.

Soil Tillage Res.

87

, 194

–

204 (2006).

25. Olsson, L. et al. The State of the World

’

s Arable Land.

Annu. Rev.

Environ. Resour.

48

, 451

–

475 (2023).

26. Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe

with quanti

ﬁ

ed spatial uncertainty.

Soil

7

, 217

–

240 (2021).

27. Lal, R. Enhancing crop yields in the developing countries through

restoration of the soil organic carbon pool in agricultural lands.

Land Degrad. Dev.

17

, 197

–

209 (2006).

28. Fensholt, R. et al. Greenness in semi-arid areas across the globe

1981

–

2007

—

an Earth observing satellite based analysis of trends

and drivers.

Remote Sens. Environ.

121

, 144

–

158 (2012).

29. Zhu, Z. et al. Greening of the Earth and its drivers.

Nat. Clim. change

6

, 791

–

795 (2016).

30. Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity

across Europe.

Glob. change Biol.

21

, 973

–

985 (2015).

31. Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency:

recent trends and future directions.

Environ. Res. Lett.

8

, 044041

(2013).

32. Hu, Q. et al. Global cropland intensi

ﬁ

cation surpassed expansion

between 2000 and 2010: a spatio-temporal analysis based on
GlobeLand30.

Sci. Total Environ.

746

, 141035 (2020).

33. Kopittke, P. M., Menzies, N. W., Wang, P., McKenna, B. A. & Lombi, E.

Soil and the intensi

ﬁ

cation of agriculture for global food security.

Environ. Int.

132

, 105078 (2019).

34. Ramankutty, N. et al. Trends in global agricultural land use: impli-

cations for environmental health and food security.

Annu. Rev. plant

Biol.

69

, 789

–

815 (2018).

35. Wallander S., Smith D., Bowman M., Claassen R. Cover crop trends,

programs, and practices in the United States. (2021).

36. Hively, W. D., Duiker, S., McCarty, G. & Prabhakara, K. Remote

sensing to monitor cover crop adoption in southeastern Pennsyl-
vania.

J. Soil Water Conserv.

70

, 340

–

352 (2015).

37. Rivière, C., Béthinger, A. & Bergez, J.-E. The effects of cover crops

on multiple environmental sustainability indicators

—

a review.

Agr-

onomy

12

, 2011 (2022).

38. Jian, J., Du, X., Reiter, M. S. & Stewart, R. D. A meta-analysis of global

cropland soil carbon changes due to cover cropping.

Soil Biol.

Biochem.

143

, 107735 (2020).

39. Chen, X., Liang, S., Cao, Y., He, T. & Wang, D. Observed contrast

changes in snow cover phenology in northern middle and high
latitudes from 2001

–

2014.

Sci. Rep.

5

, 16820 (2015).

40. Zhang, Y. & Ma, N. Spatiotemporal variability of snow cover and

snow water equivalent in the last three decades over Eurasia.

J.

Hydrol.

559

, 238

–

251 (2018).

41. Li, X.-Y. et al. Civil war hinders crop production and threatens food

security in Syria.

Nat. Food

3

, 38

–

46 (2022).

42. Morit, A. Extreme heatwaves: surprising lessons from the record

warmth.

Nature

608

, 464

–

465 (2022).

43. de Vries, I., Sippel, S., Zeder, J., Fischer, E. & Knutti, R. Increasing

extreme precipitation variability plays a key role in future record-
shattering event probability.

Commun. Earth Environ.

5

, 482 (2024).

44. Iles, C. E. et al. Strong regional trends in extreme weather over the

next two decades under high-and low-emissions pathways.

Nat.

Geosci.

17

, 845

–

850 (2024).

45. Keshava, N. & Mustard, J. F. Spectral unmixing.

IEEE signal Process.

Mag.

19

, 44

–

57 (2002).

46. Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated water

extraction index: a new technique for surface water mapping using
Landsat imagery.

Remote Sens. Environ.

140

, 23

–

35 (2014).

Article

https://doi.org/10.1038/s41467-025-59544-1

gaez.fao.org/pages/data-viewer

| (2025) 16:4354

9

47. Potapov, P. et al. Global maps of cropland extent and change show

accelerated cropland expansion in the twenty-

ﬁ

rst century.

Nat.

Food

3

, 19

–

28 (2022).

48. Vermote E., Wolfe R. MODIS/Terra surface re

ﬂ

ectance daily L2G

Global 1km and 500m SIN Grid V061.

NASA EOSDIS Land Processes

Distributed Active Archive Center

(2021).

49. Main-Knorn, M. et al. Sen2Cor for sentinel

−

2. Image and Signal

Processing for Remote Sensing XXIII; 2017; Warsaw, Poland: SPIE; p.
37

–

48 (2017).

50. USDA NASS. Cropland Data Layer. (2020).
51. AAFC. Agriculture and Agri-Food Canada Annual Crop Inven-

tory. (2020).

52. Van Tricht, K. et al. WorldCereal: a dynamic open-source system for

global-scale, seasonal, and reproducible crop and irrigation map-
ping.

Earth Syst. Sci. Data Discuss.

2023

, 1

–

36 (2023).

53. Zanaga, D. et al. ESA WorldCover 10 m 2020 v100. Zenodo (2021).
54. You, N. et al. The 10-m crop type maps in Northeast China during

2017

–

2019.

Sci. data

8

, 41 (2021).

55. Roerink, G. J., Menenti, M. & Verhoef, W. Reconstructing cloudfree

NDVI composites using Fourier analysis of time series.

Int. J. Remote

Sens.

21

, 1911

–

1917 (2000).

56. Zhou, J., Jia, L. & Menenti, M. Reconstruction of global MODIS NDVI

time series: performance of harmonic ANalysis of time series
(HANTS).

Remote Sens. Environ.

163

, 217

–

228 (2015).

57. Zhou, J. et al. A scalable software package for time series recon-

struction of remote sensing datasets on the Google Earth Engine
platform.

Int. J. Digital Earth

16

, 988

–

1007 (2023).

58. Masson-Delmotte, V. et al. Climate change 2021: the physical sci-

ence basis.

Contribution working group I sixth Assess. Rep. inter-

governmental panel Clim. change

2

, 2391 (2021).

59. Muñoz Sabater J. ERA5-Land monthly averaged data from 1981 to

present.

Copernicus Climate Change Service (C3S) Climate Data

Store (CDS)

(2019).

60. FAO. GAEZ Data Portal. 2021. Data accessed: 23 Dec 2023

https://

.

61. Zomer, R. J., Xu, J. & Trabucco, A. Version 3 of the global aridity

index and potential evapotranspiration database.

Sci. Data

9

,

409 (2022).

Acknowledgements

This research was funded by the National Key Research and Develop-
ment Program of China (2023YFF1303702, F.T.), the China Postdoctoral
Science Foundation (2023M742679, L.F.), the Open Research Fund
Program of Key Laboratory of Digital Mapping and Land Information
Application, Ministry of Natural Resources (ZRZYBWD202306, L.F.).

Author contributions

F.T. and L.F. designed the study. Y.W., L.F. and W.S. prepared the
satellite imagery. J. Z., Y.W. and L.F. developed the code for time series
data generation. L.F. performed the data analyses. L.F. drafted the
manuscript with editing by F.T., R.F., X.T., T.T., X.Z., J.A., J.Z., Y.D. and Y.S.

Competing interests

The authors declare no competing interests.

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Correspondence

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Feng Tian.

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