IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 62, 2024 4403214

Automated In-Season Crop-Type Data Layer

Mapping Without Ground Truth for the

Conterminous United States Based

on Multisource Satellite Imagery

Hui Li , Liping Di , Senior Member, IEEE, Chen Zhang , Member, IEEE, Li Lin , Member, IEEE,

Liying Guo , Member, IEEE, Eugene G. Yu, Member, IEEE, and Zhengwei Yang , Member, IEEE

Abstract— Mapping nationwide in-season crop-type data is a

significant and challenging task in agriculture remote sensing.

The existing data product for U.S. crop-type planting, such as

the Cropland Data Layer (CDL), falls short in facilitating nearreal-time applications. This article designed a workflow aimed

at automating the generation of in-season CDL-like products

for USA. We methodically extracted trusted pixels as land

cover labels from historical CDL datasets, employing Sentinel-2,

Landsat 8, and Landsat-9 as sources for spectrum data, using the

random forest classifier to conduct nationwide crop-type classifications. These classifications were integrated into the In-Season

Crop Data Layer (ICDL) covering the entire Conterminous

United States (CONUS). This approach facilitated the efficient

generation of ICDLs for May, June, and July 2022, achieving

satisfactory accuracy in July. Compared to Nebraska and Iowa

ground truth data, ICDL achieved F1 scores of (0.911, 0.845)

for corn and (0.959, 0.969) for soybean. Furthermore, ICDL’s

regional acreage estimates for major crops (corn, soybean, spring

wheat, cotton, winter wheat, and rice) closely align with the

U.S. Department of Agriculture (USDA) National Agricultural

Statistics Service (NASS) figures, showing minimal variances as

low as (0.01%, −0.68%, 0.19%, −4.39%, −5.78%, −1.28%).

Notably, ICDL outperforms CDL in most assessments. This

research consistently produces annual ICDLs from May to

July that are readily accessible to the public in the iCrop

system. Simultaneously, it presents an alternative technique for

nationwide, in-season mapping of crop types.

Index Terms— Cropland Data Layer (CDL), In-Season Cropland Data Layer (ICDL), Landsat, Sentinel-2, trusted pixel.

I. INTRODUCTION

T

HE U.S. croplands play a crucial role in global sustenance

by not only producing food but also serving as a vital

Manuscript received 8 March 2023; revised 3 June 2023 and 31 December

2023; accepted 29 January 2024. Date of publication 5 February 2024; date

of current version 15 February 2024. The work of Liping Di was supported

in part by the United States Department of Agriculture National Institute of

Food and Agriculture (USDA) under Grant 2021-67021-34151; and in part by

the National Science Foundation under Grant CNS-1739705, Grant 2228000,

and Grant 2236137. (Corresponding author: Liping Di.)

Hui Li, Liping Di, Chen Zhang, Li Lin, Liying Guo, and Eugene G. Yu are

with the Center for Spatial Information Science and Systems, George Mason

University, Fairfax, VA 22030 USA (e-mail: hli47@gmu.edu; ldi@gmu.edu;

czhang11@gmu.edu; llin2@gmu.edu; lguo2@gmu.edu; gyu@gmu.edu).

Zhengwei Yang is with the National Agricultural Statistics Service,

U.S. Department of Agriculture, Washington, DC 20250 USA (e-mail:

zhengwei.yang@usda.gov).

Digital Object Identifier 10.1109/TGRS.2024.3361895

source of raw materials for various industries [1]. Consequently, the cultivation and yield of crops in USA significantly

influence global food and energy security. Recognizing this,

an in-season map detailing crop types across the entire country

becomes essential, providing crucial insights into the location

and extent of crops planted during the growing season. Simultaneously, an up-to-date national-scale crop-type map proves

invaluable for agricultural research and decision-making processes. This includes applications such as comprehensive yield

predictions, investigations into biofuel storage, analysis of

grain commodity prices, and accurate estimation of crop losses

during disasters. The U.S. Department of Agriculture (USDA)

National Agricultural Statistics Service (NASS) has consistently generated the annual Cropland Data Layer (CDL) for

many years. This comprehensive dataset includes information

on over 100 crop types and other significant nonagricultural

land use types, covering the entire Conterminous United States

(CONUS) with a spatial resolution of 30 m. As reported

by Boryan et al. [2], the See5 decision tree classifier model

underwent training for each Landsat scene, utilizing ground

truths provided by farmers regarding crop types at USDA’s

Farm Service Agency Common Land Units (CLU). Notably,

the overall classification accuracy for major crops, such as

corn and soybean, exhibited a range between 85% and 95%

in the year 2009. The CDL product is accessible through

CropScape [3] and has found extensive utility in diverse

agricultural applications and decision-making processes. Its

applications include flood monitoring and crop-loss estimation [4], the extraction of crop masks for early season winter

wheat identification [5], and the estimation of land cover

changes [6]. Nevertheless, the CDL is a post-season product

typically accessible in January or February of the subsequent

year. This temporal delay renders numerous in-season agricultural applications and decision-making processes impractical.

Previous researchers have delved into in-season crop mapping using remote sensing and machine learning models [7],

[8], [9], [10], [11], [12], [13], [14], [15], [16]. The success

of some endeavors relies on the availability of high-quality

crop-type ground truth data, which is essential for accurately labeling satellite image pixels as training data that

play a pivotal role in determining classification performance.

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Researchers commonly gather ground truth samples directly

from fields to construct the training dataset [17]. In recent

years, researchers also extracted crop category data from

historical official cropland maps. Wang et al. [18] collected a

certain number of random corn and soybean pixels from historical CDL that acted as the crop category data to participate in

the supervised classification. Li et al. [19] also used 2011 and

2014 CDL crop pixels without field edge training the random

forest classifier to identify the crop in California. In the realm

of large-scale mapping, Pazúr et al. [39] employed farmers

reported data and Sentinel-2 imagery as the primary data

source for classification, relying on random forest classifier

to generate distribution maps for cropland and permanent

grassland across the entirety of Switzerland. In addition,

Blickensdörfer et al. [47] integrated data from Sentinel-2,

Landsat 8, Sentinel-1, and other environment and agricultural

practices data to map crop types in Germany. Notably, they

undertook the resampling of Landsat 8 data to align with the

resolution of Sentinel-2. Johnson and Mueller [22] conducted

a study testing four data utility strategies (CDL, Landsat

7/8, Sentinel-2, and a combination of all) using a random

forest classifier to generate in-season land cover maps for the

Corn Belt. Their findings confirmed that the integration of

CDL, full-season Landsat 7/8, and Sentinel-2 imagery yielded

the best classification performance for corn and soybean.

Cai et al. [23] employed USDA CLUs in conjunction with

multiple Landsat imagery datasets. They trained a deep neural

network model specifically for classifying corn and soybeans

in Illinois county, achieving consistently high overall accuracy

across the years 2000–2015.

In the context of crop-type mapping for USA, nevertheless,

ground survey data, such as CLUs, are usually inaccessible.

Moreover, the CDL still possesses misclassifications, introducing uncertainty into the process of directly extracting crop

labels. To overcome the challenges, a concept of trusted

pixels and an extraction method were introduced, grounded in

U.S. agriculture rotation practices. This innovative approach

forecasts current-year crop types in specific locations with

consistent rotation patterns identified in historical CDL. Several studies have substantiated the viability of this method,

showcasing advanced classification performance for crop mapping during the early stages of the growing season [10],

[12], [13], [14], [22], [24]. In detail, most of them employed

trusted pixels of corn, cotton, rice, and soybean extracted from

2008–2019 CDL in the middle-west of USA based on three

kinds of intricate rotation patterns.

Rather than diversifying data source strategies, some peers

focus their efforts on innovating machine learning algorithms.

Hang et al. [25] developed a cascaded recurrent neural network

(RNN) model for hyperspectral image classification. This

model has two RNN layers to refine overflow information

and enhance correlative features. Subsequently, the features

weighted fusion and loss functions weighted combination

strategies were built to efficiently connect these two layers.

In addition, the convolutional layers were also incorporated

to recognize spectral and spatial information. This model

achieved higher overall cropland classification accuracy than

RNN for two different datasets. Hong et al. [26] introduced

mini graph convolutional networks (miniGCNs) as a solution to address the disadvantages associated with traditional

graph convolutional networks (GCNs). These networks were

designed to be trained in a minibatch fashion, aiming to

discover improved and more robust local optima. The concatenation fusion strategies of miniGCN and convolutional

neural network (CNN) reached the highest overall accuracy in crop classification when compared to ten models.

Rußwurm et al. [27] developed an end-to-end learned early

classification of time-series (ELECTS) RNN. This model

utilized multiple remote sensing imagery and official crop

datasets to achieve in-season crop-type mapping across four

geographically distinct locations, demonstrating a notable level

of accuracy.

Nevertheless, there are persistent challenges that require

attention. The integration of multiple spectrum datasets with

different widths probably introduces uncertainties in the classification process. Extended history periods and complex

rotation patterns usually cause a scarcity of trusted pixels and lower accuracy. Machine learning models based on

network structures often demand a substantial amount of

computation resources when dealing with super large-scale

mapping, such as for USA, where careful consideration must

be given to computational costs. To efficiently produce U.S.

in-season crop-type maps, this article implemented an automated mapping-without-ground-truth workflow on multisource

satellite imagery. We utilized the 2017–2021 Crop Data Layer

(CDL) to extract trusted pixels serving as land cover labels

for the year 2022. Subsequently, we conducted random forest

classifications independently for Landsat 8/9 and Sentinel2A datasets. The results of these classifications were then

methodically integrated into the In-Season Crop Data Layer

(ICDL) covering the entire CONUS. This approach facilitated

the automated generation of ICDLs as early as the end of

May. Notably, by the conclusion of July 2022, the accuracy

reached exceptional levels, surpassing that of CDL. These

ICDL products come out around six months earlier than CDL

updates. This research mainly has three contributions.

1) Employing the last five-year CDL to extract a substantial quantity of trusted pixels as land cover labels,

expanding the trusted pixel methodology from Corn

Belt to CONUS for all land cover types, concentrating on two kinds of rotation patterns, as opposed to

the previous approach that considered three patterns in

approximately ten-year and only for specific crop types

in the middle-west of USA.

2) Conducting random forest classifications for Landsat

8/9 and Sentinel-2A datasets independently, as opposed

to the previous approach of merging datasets before

classification, ignoring discrepancies in spectrum widths

between corresponding bands of two datasets.

3) Pioneering the annual production of CONUS ICDLs

during the months of May, June, and July, achieving

a satisfactory level of accuracy by July.

The production environment is the public cloud computing

platform—Google Earth Engine (GEE) [28] and Python of

ArcGIS Pro. The remainder of this article will accomplish the

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Fig. 1. Major crop’s typical planting, growing, and harvest period in USA.

following tasks: elucidating the workflow involved in CONUS

ICDL production, evaluating the accuracy of trusted pixel

and classification methodologies, assessing the performance

of ICDL in regional crop acreage estimation, and analyzing

both the advantages and limitations inherent in this workflow.

II. METHODOLOGY

A. Study Area

The study area is the entire CONUS, including 48 states and

the District of Columbia with a total size of 7 653 006 KM2

[20]. According to the USDA NASS acreage estimates for

2022, the primary crops in USA, ranked from first to sixth

in terms of acreage, are corn, soybean, winter wheat, cotton,

spring wheat, and rice [21]. Corn and soybean are mainly

located in the Corn Belt. Winter wheat and spring wheat are

planted in the middle and northern areas such as Kansas,

Oklahoma, Texas, South Dakota, North Dakota, Montana, and

Washington. A great quantity of cotton is planted in Texas and

Georgia. Rice mainly grows in Missouri, Arkansas, Louisiana,

and Alabama delta regions. In USA, the annual planting season

for major crops, excluding winter wheat, typically commences

in April and concludes in June. The harvest, on the other

hand, generally takes place between September and November.

This established timeframe reflects the customary planting and

harvest practices for these crops. This study concentrates on

the May–July period, which is the early growing season of

corn, soybean, cotton, spring wheat, and rice, and the late

growing season of winter wheat. Fig. 1 illustrates the typical

planting, growing, and harvest periods for the above major

crops in USA [29].

B. Data Collection

To produce early season and in-season crop maps, we select

Landsat-8, Landsat-9, and Sentinel-2A satellite images in May,

May–June, and May–July as the primary sources for the timeseries crop-type classification. The primary reason for this

phenomenon is that a specific type of satellite imagery often

lacks a sufficient number of cloud-free images in certain locations. The presence of cloud coverage can substantially impact

the quality of time-series remote sensing classification [30].

Landsat 8 and Landsat 9 Operational Land Imager (OLI)

surface reflectance products are generated by the Land Surface

Reflectance Code algorithm, which both are atmospherically

corrected surface reflectance images. Landsat 8 OLI products

are available from 2013 that provide nine spectral bands with

30-m spatial resolution for all bands except the panchromatic

band with 15 m, which has a 16-day revisit cycle [31]. The

Landsat 9, launched on September 27, 2021, whose sensor

OLI-2 has the same capacity as Landsat 8’s OLI, and therefore,

they have the same spectral band structure, spatial resolution,

and revisit frequency, but different observation times [32].

Landsat 8 and Landsat 9 swath sizes are both 185 × 180 km

in a scene, and they have the same scene grids with the same

path and row numbers [33].

Thus, we utilized them collectively as Landsat 8/9. There

are 459 WRS2 descending scenes covering the CONUS.

Sentinel-2A with a five-day revisit frequency on the CONUS

published globally in December 2018 and provides bottom-ofatmosphere (BOA) reflectance images, which own 13 spectral

bands [34]. Sentinel-2A imagery is presented in the Universal Transverse Mercator (UTM) projection, with a total

of 990 scenes covering the CONUS [35], [36]. Each scene

encompasses a ground area of 100 × 100 km. Table I shows

the Landsat 8, Landsat 9, and Sentinel-2A properties, including spectral resolution, spatial resolution, revisit period, and

coverage size. Meanwhile, we collected 2017–2021 CDL data

to extract 2022 CONUS trusted pixels as crop-type and land

cover labels. To operate the time-series classification scene-byscene, the scene-tiling grids of Landsat 8/9 and Sentinel-2A

were employed as scene-bound features. The WRS-2 Descending (daytime) grid shapefile has Landsat 8/9 scene-tiling grid

geometry and path/row number [37]. Meyers [36] developed a

Sentinel 2 tiling grid shapefile with every scene’s polygon and

identification. In addition, the 2021 1:500 000 states boundary

shapefile served to restrict the mapping extent [38].

C. Workflow of ICDL Production

The process for generating ICDL is depicted in Fig. 2.

Sentinel 2A and Landsat 8/9 images have different spectrum

wavelengths and were used to generate the crop-type classification, separately. The shorter revisit period of Sentinel-2A

results in a higher quantity of data, which positively impacts

the performance of time-series classification. Consequently,

Sentinel-2A classification takes precedence, with Landsat 8/9

classification utilized as a supplementary source. The entire

production consists of three main steps: 1) scene-by-scene

classification of multisource images for a state; 2) every

state ICDL; and 3) CONUS ICDL. The top two steps were

implemented in GEE, and the third step was conducted using

Python of ArcGIS Pro.

Scene-by-scene classification in a specific state is the first

step. For the time-series classification of a certain Sentinel2A scene, a selection of multitemporary high-quality images

is made by applying filters based on cloud-cover and nodate percentages. As well as Sentinel-2A is processed via

resampling and spectrum calculation. Subsequently, trusted

pixels within the extent of this scene are extracted from the

2017–2021 CDL. Following this, the selected trusted pixels,

along with their corresponding time-series image pixels, constitute the training data used to train the random forest model,

facilitating the execution of the time-series classification. This

operation is iteratively applied to each Sentinel-2A scene,

with the resulting classifications being mosaicked to construct

a comprehensive Sentinel-2A crop-type map for this state.

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TABLE I

CHARACTERS OF LANDSAT 8, LANDSAT 9, AND SENTINEL-2A IMAGERY DATA

Fig. 2. Workflow of producing ICDL for CONUS.

In cases where the generated map does not cover the entire

state area, additional processing is required for the Landsat 8/9

crop-type data to ensure comprehensive coverage. Similarly,

the Landsat 8/9 classification for this state can be generated

using the same method. Mosaicking these two classification

results to generate a new comprehensive in-season crop type

map for this state. This step is reiterated for each state,

ensuring the repetition of the process to achieve a complete

set of ICDL for every individual state. Finally, the ICDLs

from all states are mosaicked to create a comprehensive dataset

representing the ICDL for the entire CONUS. According to

this workflow, we produced the 2022 CONUS ICDL in May,

June, and July.

D. Spectrum Data Processing

This article aims to automate the production of the ICDL

using Sentinel-2A and Landsat 8/9 time-series images. Thus,

high-quality multisource satellite imageries need to be collected and encapsulated into a time-series image stack in every

scene before the classification. Sentinel-2A has cloud-cover

and no-data properties, while Landsat 8/9 only has cloud-cover

property, facilitating the extraction of high-quality images.

Given the necessity for a 30-m resolution in the ICDL and

considering that the Sentinel-2A images at a resolution of

10–20 m, a bilinear resampling process was implemented to

adjust their resolution to 30-m before conducting the Sentinel2A classification.

To provide a clear description of the high-quality image

selection process, we will use Nebraska in May–July as an

illustrative example. During the period from May to July in

Nebraska, Sentinel-2A images with cloud cover of less than

5% and no-data properties of less than 8% were chosen to

ensure the inclusion of images with minimal cloud interference

and missing data. The occurrence of no-data in Sentinel2A images is a common phenomenon observed across all

states. Fig. 3(b) displays Sentinel-2 no-data areas’ distribution

and empty-image scene in the southeast corner of Nebraska,

which need to be filled by Landsat 8/9 classification. The

high-quality image selection process for Landsat 8/9 imagery

involved setting a threshold of less than 6% for the cloud-cover

property. Fig. 3(a) visualizes how Landsat 8/9 images were

gathered in areas where Sentinel-2 data had missing values

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Fig. 3. Example of selected multiple satellite images in May–July in

Nebraska. (a) Landsat 8/9, (b) Sentinel 2A (image count—the number of

time-series images in the scene).

(no-data). After processing, time-series image stacks for each

scene are constructed for both Sentinel-2A and Landsat 8/9.

These stacks represent a sequential collection of images over

time, capturing the spectral change pattern in two kinds of

data. Fig. 3 also illustrates the quantity of time-series images in

every scene. The count of time-series images for Sentinel-2A

in each scene ranges from 3 to 9, while the additional Landsat

8/9 images supplementing each scene range from 3 to 5.

In certain states, such as Louisiana, where low cloud-cover

images are scarce compared to Nebraska, the cloud filter

conditions are adjusted accordingly. In that case, the selection

criteria for high-quality images involve setting a threshold of

less than 12% separately for both Sentinel-2 and Landsat 8/9.

To augment the classification performance, we employed a

combination of multiple spectral bands along with two vegetation indexes (VIs) in the classification process. This approach

resulted in higher accuracy in crop classification [14], [23].

A similar method was used for mapping pan-European land

cover and Switzerland cropland [39], [40]. The well-known

vegetable index is the normalized difference vegetation index

(NDVI) [41]. Time-series NDVI curve can indicate the vegetable phenology change in the growing period [42]. The

normalized difference water index (NDWI) is the indicator

to enhance open water body character in multiple spectrum

images [43], which was also used in crop identification for

MODIS and Sentinel-2A images [14], [44]. In this article,

specific bands (blue, green, red, NIR, SWIR_1, and SWIR_2)

were extracted from each Landsat 8/9 and Sentinel-2A image.

At the same time, the NDVI and NDWI bands also were

calculated in these images. As a result, the time-series images

of training and classification spectral data have blue, green,

red, NIR, SWIR_1, SWIR_2, NDVI, and NDWI bands. Two

VIs formulas are shown as follows:

NDVI =

NIR − RED

NIR + RED

(1)

NDWI =

GREEN − NIR

GREEN + NIR

. (2)

E. Training Label Extraction

This study enhanced its methodology by incorporating

trusted pixel data and time-series satellite images as training samples for the construction of the machine learning

model. The pivotal step in this process involves the extraction

of trusted pixels, which entails predicting crop types for

the upcoming year using historical planting data based on

crop rotation patterns. Our prior research has unequivocally

validated the efficacy and precision of this method in the

middle-west of USA [10], [12], [13], [14], [22], [24]. This

study expands the trusted pixel method from Corn Belt to

CONUS and concentrates on two kinds of crop rotation

patterns in the last five years, as opposed to the previous

approach, which considered three in approximately ten years.

The 2022 training pixel data were extracted from 2017–2021

CDL, which serves as an effective predictor for the types of

crops anticipated to be planted in 2022. Crop rotation is a

farming practice wherein farmers alternate the cultivation of

either the same or different crops on a specific field over

a defined period. Common rotation patterns in agriculture

include both multiple-year and one-year rotations. In the

multiple-year pattern, a single type of crop is cultivated

continuously on a field for several years. Fields adhering to

the multiple-year pattern are expected to plant the same crop

in 2022 as they did in the years 2017–2021. Conversely, in the

one-year pattern, two different types of crops are alternately

grown on the same field during this cycle. Fields following

the one-year pattern will cultivate the same crop in 2022 as

they did in the years 2020 and 2018.

As an illustration, a field that has been consistently planted

with corn from 2017 to 2021 will continue to plant corn in

2022. In contrast, another field with a crop sequence of “corn–

soybean–corn–soybean–corn” during this cycle will shift to

planting soybeans in 2022. The planting choices for 2022 are

determined by the established crop rotation patterns observed

in the preceding years. Based on this theory, we successfully

extracted a substantial number of trusted pixels for the year

2022 across the CONUS. Each trusted pixel is characterized by

its crop category attribution and geospatial location, providing

a convenient means to label time-series multisource images

for the year 2022.

F. Time-Series Classification

The random forest method [45] was employed for

time-series classification in this study. Several studies have

observed that this approach demonstrates superior interpretability and stability when dealing with complex input data

with a few errors. Moreover, it exhibits higher identification

precision compared to both support vector machines (SVM)

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Fig. 4. Distribution of ground truth in Nebraska and Iowa.

and AdaBoost in operational contexts [46]. This model has

been used in large-scale optical satellite imagery crop classification [10], [14], [39], [47]. The previous research witnessed

random forest classification with a 0.88–0.9 Kappa index when

the number of decision trees is 100–1000 and the number of

random split variables is 1–8 (number of classification indices)

[48]. Nevertheless, the increased number of decision trees

necessitates a larger allocation of computational resources and

entails more time expenditure, especially in extensive area

classification tasks. In this study, we are tasked with processing

classifications for hundreds of scene images. For each scene,

we have set the number of random split variables to eight,

and the number of decision trees is fixed at 500, striking a

balance between computational efficiency and classification

accuracy. Every processed Landsat 8/9 and Sentinel-2A timeseries image stack, along with its corresponding trusted pixel

data, is compiled into the training data set. For the training of

the random forest model, 5000 random samples are selected

to carry out the classification calculation. In this article, the

satellite data sources for both training and classification remain

consistent across every scene. Specifically, the same Sentinel2A time-series images are used for both training the model and

conducting the classification, and a similar process is followed

with Landsat 8/9 data.

G. Validation

This process involves computing pixel-level accuracy and

region-level acreage estimates to validate the performance of

the ICDL. Pixel-level accuracy evaluates the quality of trusted

pixels and classifications by comparing them to independent

ground truth data, represented by ground-field raster maps

in Nebraska and Iowa. Fig. 4 illustrates the distribution of

ground truth data located in the states of Nebraska and

Iowa, encompassing areas with corn, soybean, and grass. The

assessment process can be segmented into three aspects.

1) Trusted Pixel Accuracy Assessment: The precision,

recall, and F1 score will be calculated between ground

truth data and corresponding trusted pixels.

2) Classification Accuracy Assessment: The precision,

recall, and F1 score need to be computed between

ground truth pixels and corresponding 2022 ICDL

pixels.

3) Crop Acreage Comparison: The comparison involves

assessing the difference between the crop acreage of

ground truth pixels and the corresponding crop acreage

predicted by the 2022 ICDL pixels.

Simultaneously, the performance of the CDL 2022 classification is assessed and compared to evaluate the quality

of the ICDL. Equations (3)–(5) show precision, recall, and

F1 calculation equations, where TP is the number of true

positives, FP is the number of false positives, and FN is the

number of false negatives. Precision is a value to indicate

how many correct pixels are predicted in the trusted pixels

or classification pixels. Recall expresses how many correct

pixels are predicted in the ground truth pixels. F1 is more like

a comprehension value to assess prediction quality

Precision =

TP

TP + FP

(3)

Recall =

TP

TP + FN

(4)

F1 =

2

1

Precision +

1

Recall

. (5)

In the region-level assessment, we compared the regional

crop acreage estimates from the 2022 ICDL with the annual

crop acreage estimates from the NASS to evaluate the overall regional performance of the ICDL. The region crop

acreage estimate is a crucial application of crop maps in

agriculture. The NASS Crop Acreage Report (CAR) annually

publishes major crop acreage estimates in USA, providing essential support for national agricultural research and

decision-making [21]. In addition, the CDL 2022 specific

crop-type acreage estimates were also assessed using information from the NASS CAR.

III. EXPERIMENTS AND RESULTS

A. Production of 2022 ICDL

The ICDLs were produced by processing these steps,

as shown in Fig. 2. Fig. 5 shows a few detail screenshots

of 2022 ICDL products in May, June, and July, illustrating

crop field units, grass area, forest area, and river boundary.

The 2022 ICDLs are raster maps with 30-m spatial resolution,

which includes 86 types of land cover types such as crops,

fruits, vegetables, grass, forests, open water, developed areas,

and more. The pixel value and color structure are consistent

with CDL. The major crop-type land cover distribution areas

can be observed easily from ICDL such as corn, soybean,

winter wheat, cotton, spring wheat, and rice.

B. Assessment of Trusted Pixel and Classification Quality

Using Ground Truth

In this section, we employed ground truth data to assess

trusted pixels and classification accuracy at the pixel level.

The ground truth data are represented as a ground-field raster

map with corn, soybean, and grass. These data were collected

in Nebraska and Iowa during July 2022. The 2022 trusted pixel

data were extracted from the 2017–2021 CDL. Fig. 6 as an

example shows Nebraska trusted pixel data.

In the assessment process, we extracted trusted pixels, ICDL

pixels, and CDL 2022 pixels corresponding to ground truth

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Fig. 5. 2022 CONUS ICDL produced by May, June, and July.

pixels. Subsequently, precision, recall, and F1 scores were calculated for each crop category separately. These measurement

values of corn and soybean’s trusted pixels are presented in

Table II. In Nebraska, the precision of corn trusted pixels is

higher than that of soybean, although the recall is lower. The

F1 score for corn trusted pixels is 0.930, while for soybean,

it is 0.888. Similarly, in Iowa, corn trusted pixel precision

remains higher than soybean, with both having recalls greater

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Fig. 6. Nebraska trusted pixel data.

TABLE II

TRUSTED PIXEL ACCURACY ASSESSMENT

than 0.9. Consequently, the F1 score for corn trusted pixels is

slightly smaller than soybean, with values of 0.949 and 0.954.

In addition, the precision, recall, and F1 scores for the

time-series classification results of corn and soybean from

May to July, considering both ICDL and CDL 2022 pixels corresponding to ground truth pixels, were computed

and are presented in Table III. The results indicate a

time-series increasing accuracy tendency in all items of ICDL.

In Nebraska, the classification performances of corn and

soybean in ICDL exhibit an increasing trend from May to

July, reaching peak accuracy in July. The F1 scores for corn

and soybean in July are 0.911 and 0.845, respectively. These

scores are slightly higher than CDL for corn but slightly

lower than CDL for soybean. During the period from May

to July, the classification quality of corn in ICDL is superior

to that of soybean, aligning with the accuracy distribution

observed in trusted pixels in Nebraska. In Iowa, within the

growing season, ICDL from May to July demonstrates a

similar increasing tendency in corn and soybean classification

performance, climbing to the top accuracy in July with corn

F1 = 0.959 and soybean F1 = 0.969, both higher than CDL

2022. In the July ICDL F1 scores for Iowa, corn shows a

slightly lower score than soybean, which corresponds to their

trusted pixel accuracy distribution, as indicated in Table II.

Both corn and soybean of Iowa exhibit better classification

performance compared to Nebraska. These assessments still

indicate that July 2022 ICDL obtains stratified classification

accuracy.

The corn and soybean acreage estimates from ground truth

data, as well as the corresponding estimates from ICDL

and CDL 2022, were calculated separately. In addition, the

percentage differences in acreage between them were computed. Table IV shows the acreage estimates and percentage

differences. In ICDL, Iowa corn and soybean acreage estimates

are much closer to ground truth crop acreage than Nebraska.

In Nebraska, the soybean ICDL acreage has a bigger gap

compared to the ground truth acreage than corn. However,

in Iowa, the soybean ICDL acreage is closer to the ground truth

acreage than corn. The observed crop acreage comparisons

align with the accuracy distribution of trusted pixels and the

classification results discussed earlier. For CDL, in Nebraska,

the biases in corn and soybean acreage for CDL compared

to the ground truth (11.26%, 11.77%) are significantly larger

than those for ICDL. In Iowa, the percentage difference in

corn acreage compared to ground truth data (−1.43%) is

higher than ICDL (−1.15%), but for soybean acreage, the gap

(0.04%) is smaller than ICDL (−0.39%).

C. Assessment of Region-Level Crop Acreage Using

Official Crop Acreage Data

Crop acreage estimates in extensive areas are widely applied

in agricultural research, such as total yield prediction. In this

section, major crops, such as corn, soybean, winter wheat,

cotton, spring wheat, and rice, are presented as acreage estimates in the 2022 July CONUS ICDL. These estimates are

then compared with the 2022 official figures from the NASS

CAR [21] and the CDL. In this context, the NASS CAR is

considered as reference data, while ICDL and CDL serve as

test data. We calculated specific crop planted acreage in each

main plantation region, typically encompassing specific states.

Fig. 7 shows the comparisons.

1) Corn and Soybean Acreage Assessment: According to

the NASS acreage data, corn and soybean are recognized as the

most important crops in USA [21]. Their primary cultivation

region is the Corn Belt, a traditional agricultural plantation

area consisting of 13 states in the Midwest of the CONUS.

Acreages for corn and soybean in the Corn Belt were estimated

using ICDL and CDL, and these estimates were compared with

NASS CAR data. Fig. 7(a) and (b) illustrates their comparisons

in this region. In corn acreage estimate, most states’ estimates

in 2022 ICDL are quite near to the NASS acreage data. For

corn acreage estimates, the 2022 ICDL estimates in most

states closely align with NASS acreage data. While there

are instances of underestimation and overestimation in certain

states, such as North Dakota and Missouri, the overall corn

acreage estimation in the Corn Belt region for 2022 ICDL

is very close to the 2022 NASS corn acreage data, with

only a 0.01% gap. Regarding the soybean acreage estimate

in the Corn Belt region, the 2022 ICDL provides estimates

that are generally close to the NASS acreage data, with some

variations observed in states such as Kansas and Minnesota.

The overall soybean acreage estimate exhibits only a −0.68%

gap compared to NASS soybean acreage. In the case of

CDL, underestimates and overestimates are observed in certain

states, leading to an overall corn acreage difference of −1.25%

compared to NASS CAR. Similarly, for soybean, the overall

acreage difference is −2.59%. Both differences are larger than

those observed with ICDL.

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TABLE III

CORN AND SOYBEAN CLASSIFICATION ACCURACY ASSESSMENT FOR ICDL AND CDL 2022 IN NEBRASKA AND IOWA

TABLE IV

CORN AND SOYBEAN ACREAGE ESTIMATES IN GROUND TRUTH DATA AND CLASSIFICATION DATA

2) Spring Wheat Acreage Assessment: According to

the 2022 NASS crop acreage estimate, a total of 11 110 000

acres of spring wheat were planted in USA. This cultivation

predominantly occurred in states such as North Dakota, Montana, Minnesota, Washington, Idaho, and South Dakota. Spring

wheat acreage was reported to be less than that of winter wheat

but considerably more than rice [21]. We estimated spring

wheat planted acreage in 2022 ICDL and CDL, as well as

compared them with NASS acreage data in these six states.

The calculation and comparison results are shown in Fig. 7(c).

North Dakota, Montana, and Minnesota emerged as the leading

states in terms of spring wheat cultivation, with a substantial

portion of their acreage being identified in the 2022 ICDL.

The states of Washington, Idaho, and South Dakota planted

relatively few acres of spring wheat, and extremely low

quantities of them were estimated in the 2022 ICDL. Possible

reasons for this could include the presence of numerous

incorrectly trusted pixels and biased distribution of spring

wheat in these three states. These factors may contribute to

the challenges in accurately estimating spring wheat acreage

in the ICDL for these states. The overall spring wheat acreage

in this region from ICDL is 11 131 000 acres, which has a

0.19% gap with NASS acreage data. The total acreage of

spring wheat from CDL in this region is 12 200 000, drifting

around 9.81% from NASS CAR. In general, the overall

spring wheat acreage estimates from CDL exhibit a larger

difference compared to NASS when compared to the estimates

from ICDL.

3) Cotton Acreage Assessment: USA exports one-third of

the cotton in the world and stands as the third cotton producer

in the global [49]. In the 2022 ICDL, the predominant cotton

cultivation in USA was observed in Texas, Georgia, Oklahoma,

and Arkansas, collectively contributing to over 70% of the

total cotton production. In addition, limited cotton cultivation

was identified in the Delta region and California. This section

estimated cotton acreage in four specific states from ICDL

and CDL, comparing them with NASS acreage data. Fig. 7(d)

illustrates the comparison outcomes.

Notably, approximately 6 915 000 acres of cotton were

planted in Texas, closely aligning with the NASS acreage data

of 7 100 000 acres. Similarly, the cotton acreage estimate in

Georgia closely matches the NASS data, with both indicating

the same acreage of 1 200 000. However, in Arkansas and

Oklahoma, their cotton acreages exhibit big differences from

the NASS acreage data. In this region, 2022 ICDL overall

cotton acreage shows a −4.39% difference from the NASS

data. The 2022 CDL cotton acreage appears to significantly

overestimate in the four states mentioned, leading to an overall

acreage that exhibits a substantial gap with NASS CAR

(26.04%).

4) Winter Wheat Acreage Assessment: Winter wheat is

predominantly cultivated in the central and western regions of

the CONUS. In this analysis, the 2022 ICDL was employed to

estimate winter wheat acreage in eight specific states, namely,

Kansas, Oklahoma, Texas, Colorado, Washington, Montana,

Idaho, and Oregon. These estimates were then compared

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Fig. 7. Comparison of six major crop acreage between 2022 July ICDL, NASS Crop Acreage Report (NASS CAR), and CDL. (a) Corn acreage comparison

in Corn Belt. (b) Soybean acreage comparison in Corn Belt. (c) Spring Wheat acreage comparison. (d) Cotton acreage comparison. (e) Winter Wheat acreage

comparison. (f) Rice acreage comparison.

with corresponding NASS acreage data. Fig. 7(e) shows the

calculation results. Kansas stands out as the leading state

in the nation for winter wheat cultivation, with approximately 6 909 000 acres estimated in the 2022 ICDL. This

estimate closely aligns with the corresponding NASS winter

wheat acreage data. Oklahoma, Texas, Colorado, and Washington also demonstrate substantial winter wheat cultivation,

with sizable acreages identifiable in the 2022 ICDL. The

total winter wheat acreage in this region is approximately

23 188 000 acres, indicating a −5.84% difference compared

to the corresponding NASS acreage data. Notably, the overall

acreage estimation for winter wheat in CDL (23 223 000

acres) exhibits a slightly smaller difference (−5.64%) with

NASS CAR compared to ICDL. Both estimates are remarkably close to each other. Winter wheat is typically harvested

in early June. The ICDL in 2022 July, employing only

three months (May–July) of spectrum information for classification, likely contributes to the significant underestimation

of winter wheat in certain states, such as Montana and

Texas.

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5) Rice Acreage Assessment: Rice holds the rank of

the sixth most-grown grain in USA, with cultivation primarily concentrated in southern states such as Arkansas,

Louisiana, Texas, Missouri, and the state of California. This

section processed similar estimates and comparisons with

the above. Fig. 7(d) demonstrates the outcomes. According

to the 2022 ICDL, Arkansas boasted the largest acreage of

rice fields in the entire nation, having cultivated 1 093 000

acres of rice in 2022. Notably, this estimate aligns closely

with the 2022 NASS acreage data. In the other four states

(Louisiana, Texas, Missouri, and California), there are notable

differences between the two datasets. In this region, the overall

rice acreage in the 2022 ICDL is quite close to NASS acreage

data, and their gap is −1.28%. The total acreage estimation

in this region of rice from 2022 CDL exhibits a smaller gap

(0.8%) with NASS CAR than ICDL.

IV. DISCUSSION

A. Significance of ICDL

The 2022 ICDL data product, as derived from this study,

stands as the first in-season crop-type land cover map for the

CONUS. Notably, the ICDL successfully identified various

land cover categories encompassing all major crops in USA,

such as corn, soybean, winter wheat, cotton, spring wheat,

and rice. An outstanding feature of the ICDL is its early

production and publication, occurring in early August. This

timing is approximately six months ahead of the CDL release

schedule. This endeavor represents an exploration into utilizing

the capabilities of a public cloud platform for the automated

production of an extensive area crop map. Through multiple

assessments, the classification quality of ICDL demonstrates

an increasing trend throughout the growing season, achieving

satisfactory accuracy in the July data product when compared

to ground truth data. In addition, the acreage estimates for

major crops, such as corn, soybean, spring wheat, and cotton,

exhibit higher agreement with NASS CAR in certain regions

compared to CDL 2022. This suggests that ICDL performs

well in capturing the dynamics of crop cultivation and provides

more accurate estimates in specific areas.

B. Application Scenario of ICDL

The ICDL product serves as a nationwide raster cropland

map, offering valuable information for agriculture applications and decision-making processes. This comprehensive

map provides a detailed and accessible overview of cropland

distribution across the entire nation, supporting a range of

applications in the agricultural sector. The potential applications might involve the following.

1) Yield Prediction: By monitoring crop conditions

throughout the growing season, ICDL supports the estimation of potential yields. This information is valuable

for farmers, agribusinesses, and policymakers for planning and decision-making.

2) Harvest Planning: ICDL aids in harvest planning by

providing basic field data to monitor the spatial and

temporal variability of crop maturity. This information

helps optimize harvest timing and logistics.

3) Land Use Planning: ICDL contributes to land use planning by providing information on the distribution and

types of crops grown in specific regions. These data are

useful for policymakers, land managers, and researchers.

4) Insurance and Risk Assessment: Crop insurance agencies

can use ICDL to assess and mitigate risks associated with yield fluctuations. Timely information on

crop distribution enhances the accuracy of insurance

assessments.

5) Environmental Monitoring: Monitoring changes in crop

cover contributes to environmental monitoring efforts.

This includes assessing the impact of agriculture on

ecosystems, tracking land use changes, and studying the

relationship between crop patterns and environmental

conditions.

6) Precision Agriculture: Farmers can utilize ICDL to

facilitate precision agriculture practices. This involves

tailoring inputs (such as water, fertilizers, and pesticides)

based on the specific needs of different areas within

a field, optimizing resource use and enhancing overall

crop productivity.

7) Loss Estimates: ICDL can be fundamental data to be

used in crop loss estimates during the growing season.

For some extremely natural disasters such as flooding,

rescue departments or insurance companies can use

ICDL to estimate the affected crop types and areas,

assess the economic loss, and assist disaster relief

decision-making.

C. Advantages of the Mapping-Without-Ground-Truth

Approach

The ICDL offers the capability to generate early in-season

crop maps for major crop types across large-scale counties,

utilizing an automated and efficient approach that does not

necessarily rely on ground truth data. The central focus of

this study lies in mapping without the reliance on ground

truth samples. We employed trusted pixels, derived as land

cover labels from historical CDL data. This approach not

only circumvented the need for extensive time and financial

resources but also facilitated a freely automated extraction process. Notably, our study expanded the scope of trusted pixels

to encompass not only major crop types but also various other

land cover categories such as developed areas, grass, forest,

and open water. This inclusive approach significantly broadened the identification capabilities of remote sensing cropland

supervised classification. Consequently, the 2022 CONUS

ICDL encompasses more than 80 distinct land cover types.

D. Limitations and Potential Solutions

According to the acreage assessment, their accuracies are

not equal everywhere in the CONUS, and there are significantly low accuracies in some states such as corn acreage

estimates in North Dakota and spring wheat acreage estimates

in South Dakota. Meanwhile, ICDL and CDL both have

big percentage differences in overall winter wheat acreage

compared to NASS CAR. The reasons may include unequal

crop rotation patterns and misclassification of CDL. In some

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areas of CONUS, on one hand, the crop rotation pattern

could be variable dramatically in different places, which will

significantly impact the trusted pixel quality, but our trusted

pixel is based on the constant rotation patterns. On the other

hand, the historical CDL is the basic data of trusted pixels,

which still own a certain number of misclassifications pixels.

To improve crop-type classification accuracy, such as winter

wheat, merging the confidence data layer with CDL probable

is a method to improve the CDL accuracy [50]. Another

approach is CDL purification and refinement to improve the

quality of the trusted pixels [51]. Meanwhile, some areas are

covered by clouds for a long period: thus, there are insufficient

high-quality time-series satellite imageries that miss crop phenology information and reduce the classification performance.

Synthetic aperture radar (SAR) data such as Sentinel-1 can

be a potential supplementary data in crop classification that is

insensitive to the cloud.

E. Computational Complexity

In the production of ICDLs, various tasks, such as collecting

multiple satellite imagery, image processing, CDL collection,

trusted pixel extraction, training the random forest model,

scene-by-scene classification, and state ICDL, primarily rely

on the GEE platform to the automated process. During the

process, computational instability was only encountered when

training with more than 5000 samples. Consequently, in this

study, we opted to randomly select 5000 samples from each

scene’s trusted pixels to address this computational challenge.

The computation time for each state varies, ranging from

0.5 to 2 h, depending on the size of the state’s geographical

area. Moreover, it is worth noting that multiple states can

be processed simultaneously. For the CONUS ICDL, the

“Set Null” and “Mosaic To New Raster” Python APIs in

ArcGIS Pro were employed to achieve efficient outcomes

in the processing of the third step of the workflow. This

program completed its execution in no more than 2 h, even

when running on a standard laptop without additional GPU.

In summary, the entire process took approximately 40 h to

produce an ICDL map.

V. CONCLUSION

This study explored an automated workflow for the inseason crop-type data layer mapping without ground truth

in the CONUS using Landsat 8, Landsat 9, and Sentinel-2A

datasets. The trusted pixels from historical CDL acted as land

cover labels based on the crop rotation patterns. Thousands

of high-quality satellite images in May–July, integrating land

cover labels to train random forest models, conducting the

time-series classification. The approach successfully produced

the CONUS ICDLs product from May to July 2022, including

major crop types and other land use types. The pixel- and

region-level data assessments were executed by utilization of

the ground truth data and NASS acreage estimates data. The

ICDL product at the end of July reaches higher accuracy than

CDL in most regions. This study offered a set of in-season

CDL-like data products, as well as provided an option for

automated large-scale in-season crop mapping.

In the future, some relevant work should be procedure to

further increase the accuracy and applications of ICDL. First,

the refinement of trusted pixels should be a pivotal focus for

enhancing mapping quality. On the one hand, future research

endeavors could delve into the intricate study of diverse crop

rotation patterns across different regions and different crop

types. The trusted pixel pre-refinement using early season

spectrum information, on the other hand, could be explored

to reduce noise pixels in each crop type. Concurrently, there

are plans to explore the integration of microwave satellite

data into the crop-type identification process, aiming to mitigate the impact of atmospheric conditions. This innovative

approach seeks to enhance the robustness and accuracy of crop

mapping. In addition, the introduction of transfer learning in

crop-type mapping is on the horizon. This involves extracting high-quality trusted pixels to train classifiers in specific

locations and utilizing the trained model to process crop

mapping in other areas, extremely reducing the uncertainty

of large-scale trusted pixels. The versatility of this algorithm

is envisioned to transcend national and temporal boundaries.

Simultaneously, those applications of ICDL will be explored,

aiming to unlock and maximize its potential uses in various

contexts.

DATA AVAILABILITY

The ICDL data product is GeoTIFF 8-bit integer file format

with 30-m resolution and EPSG:5070 coordinate system. The

full dataset and associated web services are accessible on the

iCrop service system (https://cloud.csiss.gmu.edu/icrop/) [52],

served and managed by the Centre for Spatial Information

Science and System, George Mason University.

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Hui Li received the M.S. degree in cartography

and geography information system from the Xi’an

University of Science and Technology, Xi’an, China,

in 2012. He is currently pursuing the Ph.D. degree

in Earth systems and geoinformation sciences with

George Mason University, Fairfax, VA, USA.

He is a Research Assistant with the Center for

Spatial Information Science and Systems, George

Mason University. His research interests include

machine learning technology, agro-geoinformatics,

remote sensing, and geographic information

systems.

Liping Di (Senior Member, IEEE) received the

Ph.D. degree in remote sensing/GIS (geography)

from the University of Nebraska–Lincoln, Lincoln,

NE, USA, in 1991.

He is a Professor and the Founding Director of the

Center for Spatial Information Science and Systems

(CSISS) and a Professor at the Department of Geography and Geoinformation Science, George Mason

University, Fairfax, VA, USA. He has engaged in

geoinformatics and remote sensing research for more

than 30 years, published over 550 publications, and

received research grants of more than $70 million.

Dr. Di has actively participated in the activities of a number of professional

societies and international organizations, such as IEEE GRSS, ISPRS, CEOS,

ISO TC 211, OGC, INCITS, and GEO. He chaired INCITS/L1, a U.S. national

committee responsible for setting U.S. national standards on geographic

information and representing USA at ISO Technical Committee 211 (ISO TC

211) from 2010 to 2016, and served as the Convenor of ISO TC 211 Working

Group 9 from 2019 to 2022. He was elected as the Inaugural President

of the International Society of Agromatics (ISAM) in 2022. He served as

the Co-Chair for the Data Archiving and Distribution Technical Committee

(DAD TC) of IEEE GRSS from 2002 to 2005 and the Chair for DAD TC

from 2005 to 2009. He was one of the founders of the Earth Science Informatics Technical Committee (ESI TC) of IEEE GRSS. In 2012, he founded

the annual International Conference on Agro-geoinformatics, an IEEE GRSSsponsored specialty conference, and has served as the General Conference

Chair since 2012. He is also the General Chair of IGARSS 2026. He has

received many awards for his work, including the R&D100 Award from R&D

Magazine in 2008, the Secretary’s Honor Award from USDA in 2011, the

Merit Award from the InterNational Committee for Information Technology

Standards (INCITS) in 2016, and the Lifetime Achievement Award from

INCITS in 2022.

Chen Zhang (Member, IEEE) received the Ph.D.

degree in Earth systems and geoinformation sciences

from George Mason University, Fairfax, VA, USA,

in 2021.

He is a Research Assistant Professor with the

Center for Spatial Information Science and Systems,

George Mason University, Fairfax, VA. His research

interests include geographic information science

and systems, remote sensing, agro-geoinformatics,

geospatial information interoperability and standards, and land use and land cover change. He has

published over 80 peer-reviewed journal articles, book chapters, conference

papers, and engineering reports. He leads the development of many geospatial

data service systems to serve the geosciences and remote sensing community.

Li Lin (Member, IEEE) received the Ph.D. degree

in Earth systems and geoinformation sciences

from George Mason University, Fairfax, VA, USA,

in 2022.

He is a Research Assistant Professor with the

Center for Spatial Information Science and Systems,

George Mason University. His research interests

include agro-geoinformatics, remote sensing, urban

development, geospatial metadata, and catalog federation. He has authored or coauthored more than

50 peer-reviewed journal articles, book chapters,

conference papers, and engineering reports. He leads the development of many

geospatial data service systems to serve the geosciences and remote sensing

community.

Liying Guo (Member, IEEE) is a Research Professor at the Center for Spatial Information Science

and Systems, George Mason University, Fairfax, VA,

USA. She has engaged in geospatial information

research for more than 25 years. Her research interests include geospatial science applications, food

security, ecosystem sustainability, public health, climate change, and land use and land cover change.

She has actively participated in the activities of

a number of IEEE GRSS, ISO TC211, INCITS,

and OGC, and served as the PI and the Co-PI on

multiple federal and national research grants. She is the Secretary of the

International Society of Agromatics (ISAM), the Co-Chair of the Organizing

Committee of the annual International Conference on Agro-Geoinfomatics,

and the Manager of the NSF-funded multi-institutional and multidisciplinary

CropSmart project.

Eugene G. Yu (Member, IEEE) received the

B.Sc. degree in physical geography from Peking

University, Beijing, China, the M.Sc. degree in environmental remote sensing from the University of

Aberdeen, Aberdeen, U.K., the M.S. degree in information systems and the M.S. degree in computer

science from George Mason University, Fairfax,

VA, USA, and the Ph.D. degree in geography with

focus on remote sensing and geographic information

systems from Indiana State University, Terre Haute,

IN, USA.

His research interests include geographic information systems, remote sensing, intelligent image understanding, sensor web, semantic web, computational

vision, and robotics. He is a Research Professor in geographic information

sciences and remote sensing and an Associate Director of the Center for

Spatial Information Science and Systems, George Mason University.

Dr. Yu is a member of the American Geophysical Union, IEEE Geoscience

and Remote Sensing Society, and Computer Society.

Zhengwei Yang (Member, IEEE) received the B.S.

degree in electrical engineering from the Shanghai

University of Science and Technology, Shanghai,

China, in 1982, the M.S. degree in systems engineering from Shanghai Jiaotong University, Shanghai,

in 1985, and the Ph.D. degree in electrical engineering from Drexel University, Philadelphia, PA, USA,

in 1997.

He is a Geographer with the Research and

Development Division, National Agricultural Statistics Service, since 2005. He has published over

90 papers. His research interests include AI/machine learning, remote sensing

methods, and GIS technology and their application in agriculture. His work

spans conceptual, theoretical, and application research as well as system

development.

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