# Consistency and Accuracy Evaluation Analysis of Four Cropland Cover Products: A Case Study of Indochina Peninsula

Authors: Xue Wang; Jun Wang; Wei Wang; Minning Zhao
Journal: Journal of Sensors
 (2022)

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## Abstract

Information on the area and spatial distribution of cropland resources is very important for agricultural monitoring and sustainable development. The existing evaluation and analysis of cropland resources mainly focus on the first-class cropland types, and the spatial resolution of cropland cover products studied is low (<30 m), which cannot meet the needs of food security assessment and sustainable analysis of cropland resources. Therefore, in this paper, four published cropland cover products with a spatial resolution of 30 m (GFSAD30SEACE, GLC_FCS30-2015, FROM-GLC2015, and SERVIR MEKONG) are evaluated for consistency and accuracy through area validation, comparison of spatial pattern distribution, and fieldwork data. The results showed that (1) the area and spatial pattern of the four first-class cropland cover types were consistent, especially the GFSAD30SEACE product. However, the area and spatial consistency among different products decreased significantly for the finer type of secondary cropland cover, and the area percentage of rice field was only 3.03%. (2) The spatial inconsistency areas of cultivated land for the four products have different distribution rules under different topographic factors, mainly distributed in the range of elevation (<300 m), slope (2°-15°), and topographic relief (0-10 m). (3) The GFSAD30SEACE product had the highest overall accuracy of 88.33% for the primary type of cropland cover. The overall accuracy was lower for all secondary types of cropland cover, with the SERVIR MEKONG product having the highest overall accuracy (71.64%) and the other products having an overall accuracy of less than 62%. Therefore, future land cover mapping of cropland needs to focus on technical solutions for the classification of fine cropland types.


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## Body

## 1. Introduction

The cropland cover type is the basis for the survival of human society, and its utilization state, spatial distribution, and dynamic change have an important impact on economic development and climate change [1–4]. At present, with a large population in the world, food security will face great challenges. Therefore, timely understanding of some freely available information on cropland resources is of great importance in strategic decision-making of the internationalization of food security and related research on sustainable development [5–9]. With the rapid development of remote sensing earth observation technology [10–15], a variety of land cover product data have been publicly released, such as UMD produced by University of Maryland (UMD) [16], Global Land Cover (GlobCover) produced by European Space Agency (ESA) [17], GlobeLand30 produced by National Center for Basic Geographic Information of China [18], and GCEP30 produced by USGS [19]. These multisource remote sensing land cover products are very important in food security decision-making and cultivated land quality assessment. However, the data set development principles (such as remote sensing data) used in the production of these multisource remote sensing land cover products are different, resulting in some differences among the products. Therefore, timely and accurate understanding of the quality of these land cover products, the differences between products, and the advantages and disadvantages of each product has important guiding significance for the subsequent extensive application of land cover products and production of products [20].So far, scholars have carried out comparative evaluation studies on different land cover products. For example, McCallum et al. [21] evaluated and analyzed four kinds of land cover products with spatial resolution of 1 km at global and seven regional scales. The experiment showed that the selected four kinds of low-resolution land cover products had good global spatial consistency, but low consistency at continental scale. Dong et al. [22] evaluated and analyzed MCD12Q1 land cover product with spatial resolution of 500 m produced by MODIS remote sensing images, taking Anhui Province, China, as the experimental area and GlobeLand30 product as reference data. The experimental results show that IGBP has the best consistency between land cover products and reference data. Kang et al. [23] compared and analyzed the consistency of three land cover products with spatial resolution of 30 m from the perspective of landscape ecology, and the experimental results showed that although the consistency of the three products in area and quantity was high, the consistency in spatial pattern was low. The evaluation and analysis of these multisource remote sensing land cover products have provided reference value for land resource investigation, environmental monitoring, and other fields. However, at present, there are few comparative evaluation analyses of different products for thematic factors (cropland types). Vancutsem et al. [24] first obtained the cropland data with a resolution of 250 m through the data fusion method and then compared and analyzed the fused data with two other cropland products with a resolution of 1 km. The results show that the accuracy of cropland data obtained by fusion method is higher. Chen et al. [25] took Shaanxi Province of China as the experimental area, evaluated and analyzed the cropland land types in four land cover products, and found that the accuracy of cropland types in the four land cover products was between 61.26% and 80.63%.However, the existing evaluation and analysis of multisource remote sensing land cover products for cropland types mainly focused on the first-class cropland types, but ignored the important role of the second-class cropland types (such as rice fields) in agricultural monitoring and food security assessment. In addition, the land cover products used in previous studies have a low spatial resolution (<30 m), but the application of land cover products with a coarse resolution like MODIS (1 km) to agricultural monitoring has limitations.Therefore, in order to make up for the deficiency of existing research, this paper takes Indochina Peninsula as the experimental area; GFSAD30SEACE (Global Food Security-Support Analysis Data@ 30 m for Southeast Asia) [26], GLC_FCS30-2015 (Global Land Cover Fine Surface Covering) [27], FROM-GLC2015 (Fine Resolution Observation and Monitoring of GLC2015) [28], and SERVIR MEKONG cropland types were analyzed for their accuracy and consistency. The influencing factors that lead to the difference between products are discussed comprehensively. The results not only provide a reference for selecting suitable cropland data for sustainable analysis of cropland resources and assessment of food security but also provide information support for transnational cropland investment and decision-making of global food security strategy.


## 2. Materials and Methods

### 2.1. Study Area

The study area is located between China and the South Asian subcontinent, including Vietnam, Laos, Cambodia, Myanmar, and Thailand (Figure1). Facing the Bay of Bengal, Andaman Sea, and Malacca Strait in the west and the South China Sea in the Pacific Ocean in the east, Indochina Peninsula is one of the three major peninsulas in southern Asia. The terrain of Indochina Peninsula as a whole is high in the north and low in the south. It is mountainous, plateaued, and arranged alternately with mountains and rivers, with a north-south direction. Plains are mostly distributed in the southeastern coastal areas, mainly consisting of vast alluvial plains and deltas. Indochina Peninsula has a typical tropical monsoon climate, with a hot season from March to May, with a monthly average temperature of 25-30°C. From June to October is the rainy season with abundant rainfall, November to May of the following year for the dry season, dry weather little rain. Rice cultivation in Indochina Peninsula has a long history and advanced planting technology. Under the impact of rivers, the agricultural developed areas are mostly distributed in the flood plain and delta in the middle and lower reaches of rivers, such as the Mekong Delta, where the fertile alluvial soil is conducive to agricultural development.Figure 1
Geographical location of the study area.


### 2.2. Data and Preprocessing

#### 2.2.1. Cropland Land Product Data

In this paper, four kinds of cropland cover products with spatial resolution of 30 m are collected for consistency and accuracy evaluation analysis, produced by the USGS lead GFSAD30SEACE product (https://lpdaac.usgs.gov/products/gfsad30seacev001/), Chinese Academy of Sciences Institute of Remote Sensing and Digital Earth made GLC_FCS30-2015 product (http://data.casearth.cn/sdo/detail/5d904b7a0887164a5c7fbfa0), FROM_GLC2015 produced by Tsinghua University (http://data.ess.tsinghua.edu.cn/), and SERVIR MEKONG product data produced by USAID, NASA, and other teams (https://rlcms-servir.adpc.net/en/landcover/). Table 1 shows the detailed parameters of each product.Table 1
Main parameters of each product.
ProductsResolution (m)TimeMethodOrganizationDataOverall accuracy (%)GFSAD30SEACE302015Random forestUSGS, University of New Hampshire, University of Wisconsin Madison, etc.Landsat ETM+/OLI88.6GLC_FCS30-2015302015Random forestInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesLandsat OLI81.4SERVIR MEKONG302015Support vector machine and random forestUSAID, NASA, and ADPCLandsat TM/ETM+Not publishedFROM_GLC2015302015Random forestTsinghua UniversityLandsat TM/ETM+/OLI77.3The data sources and classification systems of the four cropland products collected were different, which hindered the compatibility and interoperability of the data. Therefore, the original data should be preprocessed before the study. Firstly, four kinds of cropland products were trimmed based on ArcGIS software and Indochina Peninsula vector boundary, and all data coordinates were unified into UTM projection and WGS84 coordinate system. The classification system is very important in the process of land cover mapping [29, 30]. If there is no consistent classification system for the comparative analysis of different products, the evaluation results will be affected [31]. Therefore, based on the definition of GFSAD30SEACE product cropland, this paper normalized the classification system of the other three products. The original data and normalized classification system are shown in Tables 2 and 3, respectively.Table 2
Original classification system.
CodeGFSAD30SEACECodeGLC_FCS30-2015CodeFROM_GLC2015CodeSERVIR MEKONG0Water10Rainfed cropland10Cropland0Other1Noncropland11Herbaceous cover11Rice paddy1Surface water2Cropland12Tree or shrub cover (orchard)12Greenhouse2Snow and ice20Irrigated cropland13Other3Mangrove50Evergreen broadleaved forest14Orchard4Flooded forest60Deciduous broadleaved forest15Bare farmland5Deciduous forest61Open deciduous broadleaved forest (0.15<fc<0.4)20Forest6Orchard or plantation forest62Closed deciduous broadleaved forest (fc>0.4)21Broadleaf, leaf-on7Evergreen broadleaf alpine70Evergreen needle-leaved forest22Broadleaf, leaf-off8Evergreen broadleaf71Open evergreen needle-leaved forest (0.15<fc<0.4)23Needleleaf, leaf-on9Evergreen needleleaf72Closed evergreen needle-leaved forest (fc>0.4)24Needleleaf, leaf-off10Evergreen mixed forest80Deciduous needle-leaved forest25Mixed leaf, leaf-on11Mixed evergreen and deciduous81Open deciduous needle-leaved forest (0.15<fc<0.4)26Mixed leaf, leaf-off12Urban and built-up82Closed deciduous needle-leaved forest (fc>0.4)30Grassland13Cropland90Mixed leaf forest (broadleaved and needle-leaved)31Pasture14Rice paddy120Shrubland32Natural grassland15Mudflat and intertidal121Evergreen shrubland33Grassland, leaf-off16Mining122Deciduous shrubland40Shrubland17Barren130Grassland41Shrubland, leaf-on18Wetlands140Lichens and mosses42Shrubland, leaf-off19Grassland150Sparse vegetation (fc<0.15)50Wetland20Shrubland152Sparse shrubland (fc<0.15)51Marshland153Sparse herbaceous (fc<0.15)52Mudflat180Wetlands53Marshland, leaf-off190Impervious60Water200Bare areas70Tundra201Consolidated bare areas71Rub and brush tundra202Unconsolidated bare areas72Herbaceous tundra210Water body80Impervious surface220Permanent ice and snow90Bareland250Filled value100Snow/ice101Snow102Ice120CloudTable 3
Merging of classification system.
TypeGFSAD30SEACEGLC_FCS30-2015SERVIR MEKONGFROM_GLC2015CroplandOthers210,11,12,2013,1411,12,13,14,15Rice/201411Noncropland1,050,60,61,62,70,71,72,80,81,82,90,120,121,122,130,140,150,152,153,180,190,200,201,202,210,220,2500,1,2,3,4,5,6,7,8,9,10,11,12,15,16,17,18,19,2021,22,23,24,25,26,31,32,33,41,42,51,52,53,60,71,72,80,90,101,102


#### 2.2.2. FAO Statistical Data

In order to analyze the consistency of area, Food and Agriculture Organization (FAO) statistical data were collected (http://www.fao.org/faostat/zh/#search/cropland). The FAO defines cropland as land used to grow crops. It includes arable land and permanent cropland. Cropland refers to land used only for temporary crops, grassland used for mowing or grazing, land used for vegetable gardens, and land temporarily fallow (less than five years). Permanent cropland refers to the land for long-term crop cultivation and the land under shrubland and shrubland (excluding forests) for planting flowers and trees. According to the FAO statistics, cropland is the surface of the land where crops are grown [32].


#### 2.2.3. DEM Data

ASTER GDEM V2 data with a spatial resolution of 30 m was used for DEM data, which was downloaded from geospatial Data Cloud in this paper (http://www.gscloud.cn/). After obtaining the data, the original data were preprocessed with Mosaic, clipping and unified projection to prepare for subsequent analysis and research.


### 2.3. Methods

#### 2.3.1. Spatial Overlay

In this paper, with the help of ArcGIS platform, spatial superposition method was used to intuitively express the spatial pattern distribution of four cropland products. The spatial superposition calculation results of the 4 products can be divided into 4 cases, which are ① completely consistent (the indicator types of the 4 products are all the same), ② highly consistent (the three products have the same indication type), ③ low consistency (the two products have the same indication type), and ④ completely inconsistent (the four products are not the same indication type). See Figure2 for the schematic diagram of spatial superposition.Figure 2
Schematic diagram of spatial overlay method.In addition, topographic factors are important factors affecting land cover change, including microtopographic factors such as elevation, slope, and aspect and macrotopographic factors such as topographic relief and surface roughness [33, 34]. Surface undulation, slope, slope aspect, and the degree of surface fragmentation affect the distribution of agricultural, forest, and building land, crop cultivation, drought and flood, soil and water loss, and restrict the way and difficulty of land resource utilization [35]. Considering the correlation between topographic factors, this paper selects three representative factors, namely, elevation, slope, and topographic relief, to analyze the relationship between topographic factors and the distribution of land cover type patterns. Based on 30 m resolution DEM data, the 3 terrain factors were extracted in ArcGIS software. DEM data package contains altitude information, which does not need additional extraction. Slope can be obtained by Slope tool. Topographic relief refers to the difference between the highest elevation and the lowest elevation in a particular area [36]. The Focal Statistics tool can be used to set statistic type to range in the 3×3 window. After obtaining terrain factor data, hierarchical processing is required. ① Elevation: in order to make the gradient characteristics of land use based on elevation distribution obvious, the elevation of the study area is divided into 6 grades after analysis (Table 4). ② Slope: slope is the degree of steepness of surface unit. There are four methods of slope expression: percentage method, degree method, metric method, and fraction method. This paper adopts degree method. According to the standard [37], the slope is divided into five grades (Table 5). ③ Topographic relief: according to the 1 : 1,000,000 digital geomorphic mapping specification of China [38], topographic relief is divided into six classes (Table 6) combined with the actual topographic relief in the study area.Table 4
Elevation classification.
Grade123456Elevation (m)<100100-300300-600600-10001000-2000>2000Table 5
Slope classification.
Grade12345Slope (°)0-22-66-1515-25>25Table 6
Relief classification.
Grade123456Relief (m)0-1010-3030-7070-200200-500>500


#### 2.3.2. Absolute Accuracy Evaluation

Accuracy evaluation is a process of evaluating the quality of validation data by means of field measured data or other high-precision data. Confusion matrix is a common accuracy evaluation method [39–41]. Based on the confusion matrix, relevant indexes to evaluate the overall accuracy and the accuracy of specific categories can be calculated, including overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and Kappa coefficient. Table 7 shows the classification accuracy represented by different Kappa coefficient values [42]. The calculation formula of the four evaluation indicators is as follows [43, 44]:
(1)OA=∑i=1rxiin×100%,PA=xiix+i×100%,UA=xiixi+×100%,Kappa=n∙∑i=1rxii−∑i=1rxi+∙x+in2−∑i=1rxi+∙x+i,Table 7
The corresponding relationship between Kappa value and classification accuracy.
Kappa valueClassification accuracy<0.00Very poor0.00-0.20Poor0.20-0.40General0.40-0.60Good0.60-0.80Very good0.80-1.00Excellentwheren represents the number of total pixels in the research area; r stands for type number; xii represents the number of correctly classified pixels of class i covered category; x+i represents the total number of pixels of category i in the reference data; xi+ represents the total number of pixels of category i in the data to be evaluated.The validation samples in this paper are mainly based on high-resolution image collection of Google Earth. According to the principle of remote sensing, ground objects in remote sensing images have specific shape characteristics, radiation characteristics, spectral characteristics, etc., which constitute the interpretation symbols of ground object information and are used to identify different ground objects. In this paper, samples were collected mainly based on direct interpretation marks, such as features of ground objects such as shape, size, texture, and color. In addition, in order to ensure the accuracy of rice and other cropland samples, this paper relies on the features of land features and other platforms. Such as Geo-Wiki (https://www.geo-wiki.org/) platform and global cropland data platform (https://croplands.org/app/data/street) auxiliary interpretation. In order to reduce the negative impact of positioning and interpretation errors on sample quality, the following principles should be followed when selecting and interpreting samples:(1) the samples collected are typical and representative, that is, they can represent all the land cover types in the region. In terms of distribution, it can be evenly distributed in the study area. (2) The samples to be collected should be selected as large patches as possible. (3) Samples collected should avoid being close to the distribution boundary of types to avoid the influence of mixed types. In addition, when samples are collected based on high-resolution images of Google Earth, remote sensing images of 2015 consistent with the data to be evaluated are used, and other platforms, such as Geo-Wiki, a voluntary geographic information platform, are used to assist interpretation of the samples that are difficult to interpret [45]. See Figure 3 and Table 8 for the verification samples that cover the entire study area.Figure 3
Spatial distribution of validation samples.Table 8
The number of validation samples.
TypeCroplandNoncroplandRiceOthersForestShrubs/grasslandWetlandWaterBuilt-upBarelandOthersNumber78752661234515936122613123Total3170


## 2.1. Study Area

The study area is located between China and the South Asian subcontinent, including Vietnam, Laos, Cambodia, Myanmar, and Thailand (Figure1). Facing the Bay of Bengal, Andaman Sea, and Malacca Strait in the west and the South China Sea in the Pacific Ocean in the east, Indochina Peninsula is one of the three major peninsulas in southern Asia. The terrain of Indochina Peninsula as a whole is high in the north and low in the south. It is mountainous, plateaued, and arranged alternately with mountains and rivers, with a north-south direction. Plains are mostly distributed in the southeastern coastal areas, mainly consisting of vast alluvial plains and deltas. Indochina Peninsula has a typical tropical monsoon climate, with a hot season from March to May, with a monthly average temperature of 25-30°C. From June to October is the rainy season with abundant rainfall, November to May of the following year for the dry season, dry weather little rain. Rice cultivation in Indochina Peninsula has a long history and advanced planting technology. Under the impact of rivers, the agricultural developed areas are mostly distributed in the flood plain and delta in the middle and lower reaches of rivers, such as the Mekong Delta, where the fertile alluvial soil is conducive to agricultural development.Figure 1
Geographical location of the study area.


## 2.2. Data and Preprocessing

### 2.2.1. Cropland Land Product Data

In this paper, four kinds of cropland cover products with spatial resolution of 30 m are collected for consistency and accuracy evaluation analysis, produced by the USGS lead GFSAD30SEACE product (https://lpdaac.usgs.gov/products/gfsad30seacev001/), Chinese Academy of Sciences Institute of Remote Sensing and Digital Earth made GLC_FCS30-2015 product (http://data.casearth.cn/sdo/detail/5d904b7a0887164a5c7fbfa0), FROM_GLC2015 produced by Tsinghua University (http://data.ess.tsinghua.edu.cn/), and SERVIR MEKONG product data produced by USAID, NASA, and other teams (https://rlcms-servir.adpc.net/en/landcover/). Table 1 shows the detailed parameters of each product.Table 1
Main parameters of each product.
ProductsResolution (m)TimeMethodOrganizationDataOverall accuracy (%)GFSAD30SEACE302015Random forestUSGS, University of New Hampshire, University of Wisconsin Madison, etc.Landsat ETM+/OLI88.6GLC_FCS30-2015302015Random forestInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesLandsat OLI81.4SERVIR MEKONG302015Support vector machine and random forestUSAID, NASA, and ADPCLandsat TM/ETM+Not publishedFROM_GLC2015302015Random forestTsinghua UniversityLandsat TM/ETM+/OLI77.3The data sources and classification systems of the four cropland products collected were different, which hindered the compatibility and interoperability of the data. Therefore, the original data should be preprocessed before the study. Firstly, four kinds of cropland products were trimmed based on ArcGIS software and Indochina Peninsula vector boundary, and all data coordinates were unified into UTM projection and WGS84 coordinate system. The classification system is very important in the process of land cover mapping [29, 30]. If there is no consistent classification system for the comparative analysis of different products, the evaluation results will be affected [31]. Therefore, based on the definition of GFSAD30SEACE product cropland, this paper normalized the classification system of the other three products. The original data and normalized classification system are shown in Tables 2 and 3, respectively.Table 2
Original classification system.
CodeGFSAD30SEACECodeGLC_FCS30-2015CodeFROM_GLC2015CodeSERVIR MEKONG0Water10Rainfed cropland10Cropland0Other1Noncropland11Herbaceous cover11Rice paddy1Surface water2Cropland12Tree or shrub cover (orchard)12Greenhouse2Snow and ice20Irrigated cropland13Other3Mangrove50Evergreen broadleaved forest14Orchard4Flooded forest60Deciduous broadleaved forest15Bare farmland5Deciduous forest61Open deciduous broadleaved forest (0.15<fc<0.4)20Forest6Orchard or plantation forest62Closed deciduous broadleaved forest (fc>0.4)21Broadleaf, leaf-on7Evergreen broadleaf alpine70Evergreen needle-leaved forest22Broadleaf, leaf-off8Evergreen broadleaf71Open evergreen needle-leaved forest (0.15<fc<0.4)23Needleleaf, leaf-on9Evergreen needleleaf72Closed evergreen needle-leaved forest (fc>0.4)24Needleleaf, leaf-off10Evergreen mixed forest80Deciduous needle-leaved forest25Mixed leaf, leaf-on11Mixed evergreen and deciduous81Open deciduous needle-leaved forest (0.15<fc<0.4)26Mixed leaf, leaf-off12Urban and built-up82Closed deciduous needle-leaved forest (fc>0.4)30Grassland13Cropland90Mixed leaf forest (broadleaved and needle-leaved)31Pasture14Rice paddy120Shrubland32Natural grassland15Mudflat and intertidal121Evergreen shrubland33Grassland, leaf-off16Mining122Deciduous shrubland40Shrubland17Barren130Grassland41Shrubland, leaf-on18Wetlands140Lichens and mosses42Shrubland, leaf-off19Grassland150Sparse vegetation (fc<0.15)50Wetland20Shrubland152Sparse shrubland (fc<0.15)51Marshland153Sparse herbaceous (fc<0.15)52Mudflat180Wetlands53Marshland, leaf-off190Impervious60Water200Bare areas70Tundra201Consolidated bare areas71Rub and brush tundra202Unconsolidated bare areas72Herbaceous tundra210Water body80Impervious surface220Permanent ice and snow90Bareland250Filled value100Snow/ice101Snow102Ice120CloudTable 3
Merging of classification system.
TypeGFSAD30SEACEGLC_FCS30-2015SERVIR MEKONGFROM_GLC2015CroplandOthers210,11,12,2013,1411,12,13,14,15Rice/201411Noncropland1,050,60,61,62,70,71,72,80,81,82,90,120,121,122,130,140,150,152,153,180,190,200,201,202,210,220,2500,1,2,3,4,5,6,7,8,9,10,11,12,15,16,17,18,19,2021,22,23,24,25,26,31,32,33,41,42,51,52,53,60,71,72,80,90,101,102


### 2.2.2. FAO Statistical Data

In order to analyze the consistency of area, Food and Agriculture Organization (FAO) statistical data were collected (http://www.fao.org/faostat/zh/#search/cropland). The FAO defines cropland as land used to grow crops. It includes arable land and permanent cropland. Cropland refers to land used only for temporary crops, grassland used for mowing or grazing, land used for vegetable gardens, and land temporarily fallow (less than five years). Permanent cropland refers to the land for long-term crop cultivation and the land under shrubland and shrubland (excluding forests) for planting flowers and trees. According to the FAO statistics, cropland is the surface of the land where crops are grown [32].


### 2.2.3. DEM Data

ASTER GDEM V2 data with a spatial resolution of 30 m was used for DEM data, which was downloaded from geospatial Data Cloud in this paper (http://www.gscloud.cn/). After obtaining the data, the original data were preprocessed with Mosaic, clipping and unified projection to prepare for subsequent analysis and research.


## 2.2.1. Cropland Land Product Data

In this paper, four kinds of cropland cover products with spatial resolution of 30 m are collected for consistency and accuracy evaluation analysis, produced by the USGS lead GFSAD30SEACE product (https://lpdaac.usgs.gov/products/gfsad30seacev001/), Chinese Academy of Sciences Institute of Remote Sensing and Digital Earth made GLC_FCS30-2015 product (http://data.casearth.cn/sdo/detail/5d904b7a0887164a5c7fbfa0), FROM_GLC2015 produced by Tsinghua University (http://data.ess.tsinghua.edu.cn/), and SERVIR MEKONG product data produced by USAID, NASA, and other teams (https://rlcms-servir.adpc.net/en/landcover/). Table 1 shows the detailed parameters of each product.Table 1
Main parameters of each product.
ProductsResolution (m)TimeMethodOrganizationDataOverall accuracy (%)GFSAD30SEACE302015Random forestUSGS, University of New Hampshire, University of Wisconsin Madison, etc.Landsat ETM+/OLI88.6GLC_FCS30-2015302015Random forestInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesLandsat OLI81.4SERVIR MEKONG302015Support vector machine and random forestUSAID, NASA, and ADPCLandsat TM/ETM+Not publishedFROM_GLC2015302015Random forestTsinghua UniversityLandsat TM/ETM+/OLI77.3The data sources and classification systems of the four cropland products collected were different, which hindered the compatibility and interoperability of the data. Therefore, the original data should be preprocessed before the study. Firstly, four kinds of cropland products were trimmed based on ArcGIS software and Indochina Peninsula vector boundary, and all data coordinates were unified into UTM projection and WGS84 coordinate system. The classification system is very important in the process of land cover mapping [29, 30]. If there is no consistent classification system for the comparative analysis of different products, the evaluation results will be affected [31]. Therefore, based on the definition of GFSAD30SEACE product cropland, this paper normalized the classification system of the other three products. The original data and normalized classification system are shown in Tables 2 and 3, respectively.Table 2
Original classification system.
CodeGFSAD30SEACECodeGLC_FCS30-2015CodeFROM_GLC2015CodeSERVIR MEKONG0Water10Rainfed cropland10Cropland0Other1Noncropland11Herbaceous cover11Rice paddy1Surface water2Cropland12Tree or shrub cover (orchard)12Greenhouse2Snow and ice20Irrigated cropland13Other3Mangrove50Evergreen broadleaved forest14Orchard4Flooded forest60Deciduous broadleaved forest15Bare farmland5Deciduous forest61Open deciduous broadleaved forest (0.15<fc<0.4)20Forest6Orchard or plantation forest62Closed deciduous broadleaved forest (fc>0.4)21Broadleaf, leaf-on7Evergreen broadleaf alpine70Evergreen needle-leaved forest22Broadleaf, leaf-off8Evergreen broadleaf71Open evergreen needle-leaved forest (0.15<fc<0.4)23Needleleaf, leaf-on9Evergreen needleleaf72Closed evergreen needle-leaved forest (fc>0.4)24Needleleaf, leaf-off10Evergreen mixed forest80Deciduous needle-leaved forest25Mixed leaf, leaf-on11Mixed evergreen and deciduous81Open deciduous needle-leaved forest (0.15<fc<0.4)26Mixed leaf, leaf-off12Urban and built-up82Closed deciduous needle-leaved forest (fc>0.4)30Grassland13Cropland90Mixed leaf forest (broadleaved and needle-leaved)31Pasture14Rice paddy120Shrubland32Natural grassland15Mudflat and intertidal121Evergreen shrubland33Grassland, leaf-off16Mining122Deciduous shrubland40Shrubland17Barren130Grassland41Shrubland, leaf-on18Wetlands140Lichens and mosses42Shrubland, leaf-off19Grassland150Sparse vegetation (fc<0.15)50Wetland20Shrubland152Sparse shrubland (fc<0.15)51Marshland153Sparse herbaceous (fc<0.15)52Mudflat180Wetlands53Marshland, leaf-off190Impervious60Water200Bare areas70Tundra201Consolidated bare areas71Rub and brush tundra202Unconsolidated bare areas72Herbaceous tundra210Water body80Impervious surface220Permanent ice and snow90Bareland250Filled value100Snow/ice101Snow102Ice120CloudTable 3
Merging of classification system.
TypeGFSAD30SEACEGLC_FCS30-2015SERVIR MEKONGFROM_GLC2015CroplandOthers210,11,12,2013,1411,12,13,14,15Rice/201411Noncropland1,050,60,61,62,70,71,72,80,81,82,90,120,121,122,130,140,150,152,153,180,190,200,201,202,210,220,2500,1,2,3,4,5,6,7,8,9,10,11,12,15,16,17,18,19,2021,22,23,24,25,26,31,32,33,41,42,51,52,53,60,71,72,80,90,101,102


## 2.2.2. FAO Statistical Data

In order to analyze the consistency of area, Food and Agriculture Organization (FAO) statistical data were collected (http://www.fao.org/faostat/zh/#search/cropland). The FAO defines cropland as land used to grow crops. It includes arable land and permanent cropland. Cropland refers to land used only for temporary crops, grassland used for mowing or grazing, land used for vegetable gardens, and land temporarily fallow (less than five years). Permanent cropland refers to the land for long-term crop cultivation and the land under shrubland and shrubland (excluding forests) for planting flowers and trees. According to the FAO statistics, cropland is the surface of the land where crops are grown [32].


## 2.2.3. DEM Data

ASTER GDEM V2 data with a spatial resolution of 30 m was used for DEM data, which was downloaded from geospatial Data Cloud in this paper (http://www.gscloud.cn/). After obtaining the data, the original data were preprocessed with Mosaic, clipping and unified projection to prepare for subsequent analysis and research.


## 2.3. Methods

### 2.3.1. Spatial Overlay

In this paper, with the help of ArcGIS platform, spatial superposition method was used to intuitively express the spatial pattern distribution of four cropland products. The spatial superposition calculation results of the 4 products can be divided into 4 cases, which are ① completely consistent (the indicator types of the 4 products are all the same), ② highly consistent (the three products have the same indication type), ③ low consistency (the two products have the same indication type), and ④ completely inconsistent (the four products are not the same indication type). See Figure2 for the schematic diagram of spatial superposition.Figure 2
Schematic diagram of spatial overlay method.In addition, topographic factors are important factors affecting land cover change, including microtopographic factors such as elevation, slope, and aspect and macrotopographic factors such as topographic relief and surface roughness [33, 34]. Surface undulation, slope, slope aspect, and the degree of surface fragmentation affect the distribution of agricultural, forest, and building land, crop cultivation, drought and flood, soil and water loss, and restrict the way and difficulty of land resource utilization [35]. Considering the correlation between topographic factors, this paper selects three representative factors, namely, elevation, slope, and topographic relief, to analyze the relationship between topographic factors and the distribution of land cover type patterns. Based on 30 m resolution DEM data, the 3 terrain factors were extracted in ArcGIS software. DEM data package contains altitude information, which does not need additional extraction. Slope can be obtained by Slope tool. Topographic relief refers to the difference between the highest elevation and the lowest elevation in a particular area [36]. The Focal Statistics tool can be used to set statistic type to range in the 3×3 window. After obtaining terrain factor data, hierarchical processing is required. ① Elevation: in order to make the gradient characteristics of land use based on elevation distribution obvious, the elevation of the study area is divided into 6 grades after analysis (Table 4). ② Slope: slope is the degree of steepness of surface unit. There are four methods of slope expression: percentage method, degree method, metric method, and fraction method. This paper adopts degree method. According to the standard [37], the slope is divided into five grades (Table 5). ③ Topographic relief: according to the 1 : 1,000,000 digital geomorphic mapping specification of China [38], topographic relief is divided into six classes (Table 6) combined with the actual topographic relief in the study area.Table 4
Elevation classification.
Grade123456Elevation (m)<100100-300300-600600-10001000-2000>2000Table 5
Slope classification.
Grade12345Slope (°)0-22-66-1515-25>25Table 6
Relief classification.
Grade123456Relief (m)0-1010-3030-7070-200200-500>500


### 2.3.2. Absolute Accuracy Evaluation

Accuracy evaluation is a process of evaluating the quality of validation data by means of field measured data or other high-precision data. Confusion matrix is a common accuracy evaluation method [39–41]. Based on the confusion matrix, relevant indexes to evaluate the overall accuracy and the accuracy of specific categories can be calculated, including overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and Kappa coefficient. Table 7 shows the classification accuracy represented by different Kappa coefficient values [42]. The calculation formula of the four evaluation indicators is as follows [43, 44]:
(1)OA=∑i=1rxiin×100%,PA=xiix+i×100%,UA=xiixi+×100%,Kappa=n∙∑i=1rxii−∑i=1rxi+∙x+in2−∑i=1rxi+∙x+i,Table 7
The corresponding relationship between Kappa value and classification accuracy.
Kappa valueClassification accuracy<0.00Very poor0.00-0.20Poor0.20-0.40General0.40-0.60Good0.60-0.80Very good0.80-1.00Excellentwheren represents the number of total pixels in the research area; r stands for type number; xii represents the number of correctly classified pixels of class i covered category; x+i represents the total number of pixels of category i in the reference data; xi+ represents the total number of pixels of category i in the data to be evaluated.The validation samples in this paper are mainly based on high-resolution image collection of Google Earth. According to the principle of remote sensing, ground objects in remote sensing images have specific shape characteristics, radiation characteristics, spectral characteristics, etc., which constitute the interpretation symbols of ground object information and are used to identify different ground objects. In this paper, samples were collected mainly based on direct interpretation marks, such as features of ground objects such as shape, size, texture, and color. In addition, in order to ensure the accuracy of rice and other cropland samples, this paper relies on the features of land features and other platforms. Such as Geo-Wiki (https://www.geo-wiki.org/) platform and global cropland data platform (https://croplands.org/app/data/street) auxiliary interpretation. In order to reduce the negative impact of positioning and interpretation errors on sample quality, the following principles should be followed when selecting and interpreting samples:(1) the samples collected are typical and representative, that is, they can represent all the land cover types in the region. In terms of distribution, it can be evenly distributed in the study area. (2) The samples to be collected should be selected as large patches as possible. (3) Samples collected should avoid being close to the distribution boundary of types to avoid the influence of mixed types. In addition, when samples are collected based on high-resolution images of Google Earth, remote sensing images of 2015 consistent with the data to be evaluated are used, and other platforms, such as Geo-Wiki, a voluntary geographic information platform, are used to assist interpretation of the samples that are difficult to interpret [45]. See Figure 3 and Table 8 for the verification samples that cover the entire study area.Figure 3
Spatial distribution of validation samples.Table 8
The number of validation samples.
TypeCroplandNoncroplandRiceOthersForestShrubs/grasslandWetlandWaterBuilt-upBarelandOthersNumber78752661234515936122613123Total3170


## 2.3.1. Spatial Overlay

In this paper, with the help of ArcGIS platform, spatial superposition method was used to intuitively express the spatial pattern distribution of four cropland products. The spatial superposition calculation results of the 4 products can be divided into 4 cases, which are ① completely consistent (the indicator types of the 4 products are all the same), ② highly consistent (the three products have the same indication type), ③ low consistency (the two products have the same indication type), and ④ completely inconsistent (the four products are not the same indication type). See Figure2 for the schematic diagram of spatial superposition.Figure 2
Schematic diagram of spatial overlay method.In addition, topographic factors are important factors affecting land cover change, including microtopographic factors such as elevation, slope, and aspect and macrotopographic factors such as topographic relief and surface roughness [33, 34]. Surface undulation, slope, slope aspect, and the degree of surface fragmentation affect the distribution of agricultural, forest, and building land, crop cultivation, drought and flood, soil and water loss, and restrict the way and difficulty of land resource utilization [35]. Considering the correlation between topographic factors, this paper selects three representative factors, namely, elevation, slope, and topographic relief, to analyze the relationship between topographic factors and the distribution of land cover type patterns. Based on 30 m resolution DEM data, the 3 terrain factors were extracted in ArcGIS software. DEM data package contains altitude information, which does not need additional extraction. Slope can be obtained by Slope tool. Topographic relief refers to the difference between the highest elevation and the lowest elevation in a particular area [36]. The Focal Statistics tool can be used to set statistic type to range in the 3×3 window. After obtaining terrain factor data, hierarchical processing is required. ① Elevation: in order to make the gradient characteristics of land use based on elevation distribution obvious, the elevation of the study area is divided into 6 grades after analysis (Table 4). ② Slope: slope is the degree of steepness of surface unit. There are four methods of slope expression: percentage method, degree method, metric method, and fraction method. This paper adopts degree method. According to the standard [37], the slope is divided into five grades (Table 5). ③ Topographic relief: according to the 1 : 1,000,000 digital geomorphic mapping specification of China [38], topographic relief is divided into six classes (Table 6) combined with the actual topographic relief in the study area.Table 4
Elevation classification.
Grade123456Elevation (m)<100100-300300-600600-10001000-2000>2000Table 5
Slope classification.
Grade12345Slope (°)0-22-66-1515-25>25Table 6
Relief classification.
Grade123456Relief (m)0-1010-3030-7070-200200-500>500


## 2.3.2. Absolute Accuracy Evaluation

Accuracy evaluation is a process of evaluating the quality of validation data by means of field measured data or other high-precision data. Confusion matrix is a common accuracy evaluation method [39–41]. Based on the confusion matrix, relevant indexes to evaluate the overall accuracy and the accuracy of specific categories can be calculated, including overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and Kappa coefficient. Table 7 shows the classification accuracy represented by different Kappa coefficient values [42]. The calculation formula of the four evaluation indicators is as follows [43, 44]:
(1)OA=∑i=1rxiin×100%,PA=xiix+i×100%,UA=xiixi+×100%,Kappa=n∙∑i=1rxii−∑i=1rxi+∙x+in2−∑i=1rxi+∙x+i,Table 7
The corresponding relationship between Kappa value and classification accuracy.
Kappa valueClassification accuracy<0.00Very poor0.00-0.20Poor0.20-0.40General0.40-0.60Good0.60-0.80Very good0.80-1.00Excellentwheren represents the number of total pixels in the research area; r stands for type number; xii represents the number of correctly classified pixels of class i covered category; x+i represents the total number of pixels of category i in the reference data; xi+ represents the total number of pixels of category i in the data to be evaluated.The validation samples in this paper are mainly based on high-resolution image collection of Google Earth. According to the principle of remote sensing, ground objects in remote sensing images have specific shape characteristics, radiation characteristics, spectral characteristics, etc., which constitute the interpretation symbols of ground object information and are used to identify different ground objects. In this paper, samples were collected mainly based on direct interpretation marks, such as features of ground objects such as shape, size, texture, and color. In addition, in order to ensure the accuracy of rice and other cropland samples, this paper relies on the features of land features and other platforms. Such as Geo-Wiki (https://www.geo-wiki.org/) platform and global cropland data platform (https://croplands.org/app/data/street) auxiliary interpretation. In order to reduce the negative impact of positioning and interpretation errors on sample quality, the following principles should be followed when selecting and interpreting samples:(1) the samples collected are typical and representative, that is, they can represent all the land cover types in the region. In terms of distribution, it can be evenly distributed in the study area. (2) The samples to be collected should be selected as large patches as possible. (3) Samples collected should avoid being close to the distribution boundary of types to avoid the influence of mixed types. In addition, when samples are collected based on high-resolution images of Google Earth, remote sensing images of 2015 consistent with the data to be evaluated are used, and other platforms, such as Geo-Wiki, a voluntary geographic information platform, are used to assist interpretation of the samples that are difficult to interpret [45]. See Figure 3 and Table 8 for the verification samples that cover the entire study area.Figure 3
Spatial distribution of validation samples.Table 8
The number of validation samples.
TypeCroplandNoncroplandRiceOthersForestShrubs/grasslandWetlandWaterBuilt-upBarelandOthersNumber78752661234515936122613123Total3170


## 3. Results

### 3.1. Area Comparison

Figure4 shows the statistical area of primary cropland and secondary rice field of four products. The results show that there is a high consistency between GFSAD30SEACE and SERVIR MEKONG, and the difference in percentage of cropland to the total area of the study area is 1.22%. GlC_FCS30-2015 product had the highest extraction area of cultivated land, with an area percentage of 37.66%. The difference between GLC_FCS30-2015 product and the other three products was 4.69% to 11.51%. The cropland area extracted from FROM_GLC2015 product is the lowest among the other three products, and the area percentage is less than 30%. For the secondary rice field type, the differences among the three products were large and the consistency was low. FROM_GLC2015 product rice field extraction area was the lowest, the area percentage was only 1.08%. And the consistency of the rice fields of GLC_FCS30-2015 and SERVIR MEKONG products was slightly better than that of the first-class cropland, with a percentage difference of 1.56% between GLC_FCS30-2015 and SERVIR MEKONG products. In conclusion, the consistency of first-class cropland area among the four types of cropland cover products is good, but the consistency of fine cropland type (such as rice) is low.Figure 4
Area comparison of different cropland products.Figure5 is the comparison result between the extracted area of first-class cropland and second-class rice field types of each product and the statistical data of FAO at the national scale. In Laos, the extracted area of first-class cropland of the four products was higher than the statistical result of FAO, among which FROM_GLC2015 product had a high consistency with FAO, and the relative error was 10.93%. As for secondary rice fields, the extraction area of GLC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 was lower than that of FAO. The consistency between SERVIR MEKONG and FAO was higher, and the relative error was 37.74%.Figure 5
Comparison of cropland extraction area and FAO statistical data at national scale: (a) cropland; (b) rice.
(a)(b)In Cambodia region, the extraction area of the first-class cropland of the four products was higher than the statistical result of FAO, and the relative error between the four products and FAO remained between 81.56% and 89.06%, indicating that the cropland area of the four products was significantly different from the statistical result of FAO. For secondary rice fields, the extraction area of GLC_FCS30-2015 and FROM_GLC2015 products was lower than the statistical FAO result, and the relative errors were 54.37% and 95.56%, respectively. The area of SERVIR MEKONG was slightly higher than that of FAO, and the relative error was only 0.54%.In Vietnam region, GFSAD30SEACE, SERVIR MEKONG, and FROM_GLC2015 product class I cropland extraction areas are higher than the FAO statistics. GFSAD30SEACE and FAO have a high consistency, and the relative error between them is 6.07%. For the secondary rice field type, the extraction area of the three products was lower than the FAO statistical result, especially the FROM_GLC2015 product, and the relative error of the two products was as high as 91.96%.In Myanmar region, the extracted area of the first-level cultivated land of four products was also higher than the statistical result of FAO, among which FROM_GLC2015 product had a high consistency with FAO, and the relative error was only 1.77%. The consistency between GFSAD30SEACE product first-class cropland extraction area and FAO was slightly higher, and the relative error was 14.58%. For the secondary rice field type, FROM_GLC2015 product had the lowest consistency with the statistical results of FAO, and the relative error was as high as 94.58%, which was similar to the comparison results in Laos, Cambodia, and Vietnam.In Thailand region, the extraction area of the first-level cultivated land of four products was higher than the statistical result of FAO, among which the difference between GLC_FCS30-2015 product and FAO was the largest, with a positive error of 36.55%. The statistical difference between SERVIR MEKONG products and FAO results was relatively small with a relative error of 24.26% for secondary rice fields. However, the FROM_GLC2015 product has the largest difference with the FAO statistical results, with a relative error of 93.99%.Figure6 shows the comparison between the extracted area of first-class cropland and secondary rice field of four cropland products in the whole Indochina Peninsula and the statistical data of FAO. GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products are all higher than the FAO statistics in terms of the area of cropland extracted from the first class, with relative errors of 19.16%, 35.84%, and 18.03%, respectively. The extracted area of FROM_GLC2015 first-class cropland was lower than the statistical result of FAO, and the consistency between the two was the highest, with a relative error of 5.80%. For secondary rice fields, GLC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 differed greatly from FAO data, especially FROM_GLC2015, which showed a relative error of 93.90%. The relative errors of SERVIR MEKONG and GLC_FCS30-2015 products with FAO statistics were 36.98% and 49.86%, respectively.Figure 6
Comparison of cropland extraction area and FAO statistical data at the study area: (a) cropland; (b) rice.
(a)(b)


### 3.2. Comparison of Spatial Pattern

#### 3.2.1. Direct Comparison

Figure7 shows the spatial pattern distribution of four first-class cropland types of cropland products. The four product level categories of cropland have good spatial consistency across the board. Cropland resources in the study area are widely distributed, mainly in plains and deltas formed by sediment deposition carried by rivers, such as the Red River Delta in Vietnam, the Mekong River Plain in Thailand, the Mekong River Plain through the four countries of Laos, Cambodia, Vietnam, and Thailand, and the Mekong Delta, Salween, and Irrawaddy River regions. By comparing the spatial pattern distribution of the four first-class cropland products, the spatial pattern distribution of the four first-class cropland products was consistent in the areas with high land cover homogeneity (such as plain and delta), such as the central, southern, and northwestern Indochina Peninsula. However, in areas with high heterogeneity of land surface type (mountain area), the spatial pattern of cropland for each product varies greatly, such as the mountainous areas in the north of Red River in Vietnam, northern Laos, eastern Cambodia, northern Thailand, and some areas in eastern Myanmar.Figure 7
Spatial distribution of first-class cropland for each product.The spatial distribution of paddy field types of GlC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 was compared (Figure8). The spatial distribution of paddy field types in the whole study area was significantly different. Among them, the spatial distribution of FROM_GLC2015 product rice fields is the least, mainly distributed in the southern part of the Chao Phraya River Plain in Thailand, the Red River Delta in Vietnam, the Mekong River Delta in Vietnam, and the southern part of the Irrawaddy River in Myanmar. For GLC_FCS30-2015 products, rice fields were mainly distributed in a few areas around the Irrawaddy River, most of the Mekong River delta and Red River Delta, and the Chao Phraya River plain. For SERVIR MEKONG products, rice fields are mainly distributed around the Irrawaddy River, the Mekong Delta, the Red River Delta, the Chao Phraya Plain, and the Mekong Plain. In conclusion, the spatial pattern consistency of the four types of cropland cover products was higher in the first-class cropland type, but lower in the fine rice field type.Figure 8
Spatial distribution of rice type of each product.


#### 3.2.2. Compared with Google Earth Image

Figure9 shows the spatial comparison results of cropland products in four typical regions and Google Earth images. For the Irrawaddy Delta region, the results of GFSAD30SEACE and SERVIR MEKONG are consistent with Google Earth images, followed by GLC_FCS30-2015 product extraction results. However, the cropland extraction results of FROM_GLC2015 product had the lowest consistency with Google Earth image, mainly because part of cropland was wrongly classified into built-in type. For the Mae Nam River Plain region, the GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products are in good overall agreement with Google Earth imagery, with the GLC_FCS30-2015 product classification results being more fragmented, while the FROM_GLC2015 product classification results are more different from Google Earth imagery, with serious confusion between forest, grassland, and cropland and a more fragmented spatial distribution.Figure 9
Comparison of typical regions with Google Earth image.
(a)(b)(c)(d)For the Mekong Plain region located in the country of Cambodia, the results of the four product cropland extracts were in low agreement with Google Earth imagery, mainly due to the high level of confusion between forest, grassland, and cropland types. For the Red River Delta region located in the country of Vietnam, the GFSAD30SEACE product has the best overall agreement with Google Earth imagery, and this product does a better job of removing road types. The next best agreement was with the GLC_FCS30-2015 and SERVIR MEKONG products, which misclassified a few built-ups into cultivated land types. FROM_GLC2015 has the worst agreement with Google Earth imagery, with the product misclassifying mainly cropland as forest and other types as cropland.


#### 3.2.3. Spatial Consistency

Four kinds of cropland products were spatially superimposed to obtain the spatial consistency distribution atlas of different products (Figure10). Figures 10 and 11 show that the spatial pattern of cropland is less consistent across the four product classes, with 36.08% of the area of perfectly consistent cropland, mainly in the Irrawaddy River Basin, the Mekong River Plain, and the Mekong River Plain, where the homogeneity of cropland types is high. The percentage of area of completely inconsistent cropland for the four products was 21.69%, mainly in areas with a more fragmented and heterogeneous distribution of cropland. The distribution is more concentrated in the eastern part of the study area in areas of inconsistent Vietnamese national cropland, while it is scattered in other parts of the study area.Figure 10
Spatial consistency distribution of first-class cropland.Figure 11
Percentage of spatial consistency area of first-class cropland.Figures12 and 13 show the spatial consistency distribution and consistency area statistics of GlC_FCS30-2015, FROM_GLC2015, and SERVIR MEKONG paddy fields, respectively. The results show that the spatial consistency of the three product rice types is extremely low, with the percentage of fully consistent areas being only 3.03%, mainly in the Mekong Delta, the Red River Delta, and the southern Mekong River Plain and a smaller number of areas in the southern Ayeyarwady River, and with a more fragmented distribution. The area percentage of low-degree consistent area of rice field type was 23.64%, and its spatial distribution pattern was similar to that of completely consistent area. The high percentage of areas with completely disparate rice types is 73.33%, mainly in the Mekong Plain in the central part of the study area and to a lesser extent in the Irrawaddy River Basin, the Mekong Plain, the Mekong Delta, and the Red River Delta.Figure 12
Spatial consistency distribution of rice type.Figure 13
Percentage of spatial consistency area of rice type.


### 3.3. Spatial Inconsistency under Topographic Factors

In order to analyze the spatial inconsistency distribution of different cropland cover products under topographic and geomorphic characteristics, four product spatial inconsistency areas were superimposed with topographic factors to obtain the spatial distribution rules of inconsistent areas within different topographic factors. Figure14 shows the statistical results of spatial distribution and area percentage of inconsistent areas at different elevation levels. The experiments showed that the inconsistent areas of the four cropland products were mainly distributed in the range of elevation classes<100m and 100-300 m, with a percentage area of 45.34% and 32.02%, respectively. When the elevation>300m, the area of inconsistency decreases significantly, especially when the elevation>2000m, the percentage of area of inconsistency is only 0.15%. Figure 15 shows the statistical results of spatial distribution and area percentage of inconsistent areas under different slope grades. The results showed that the four kinds of inconsistent areas were mainly distributed in the slope of 2°-6° and 6°-15°, and their area percentages were 40.39% and 34.62%, respectively. However, in other slope grades, the percentage of inconsistent area decreased significantly, and the percentage was less than 15%. Figure 16 shows the statistical results of spatial distribution and area percentage of inconsistent areas at different topographic relief levels. The experimental results show that the spatial inconsistency of the four products is the highest in the range of 0-10 m, and the area percentage is 57.85%. The area percentage of spatial inconsistency in the range of 10-30 m was 37.36%. In the range of other topographic relief grades, the spatial inconsistency of the four products is low, and the area percentage is less than 5%.Figure 14
Distribution and percentage of inconsistent areas at different elevations.Figure 15
Distribution and percentage of inconsistent areas at different slopes.Figure 16
Distribution and percentage of inconsistent areas at different relief.In conclusion, the spatial inconsistency of the four cropland products is significantly affected by altitude, slope, and topographic relief. The spatial distribution of different topographic factors at different levels is different, and the area percentage is also different. Therefore, topographic factors have a certain influence on the classification accuracy of cropland cover types.


### 3.4. Accuracy Verification Based on Field Data

The absolute accuracy of the four cropland products was evaluated based on the verification samples collected by field investigation and high-resolution Google Earth image (Tables9–12). The experimental results showed that GFSAD30SEAC product had the highest overall accuracy and Kappa coefficient, which were 88.33% and 0.76, respectively, achieving a good classification accuracy. The overall accuracy of GlC_FCS30-2015 and SERVIR MEKONG was slightly lower than that of GFSAD30SEA, but the difference between GlC_FCS30-2015 and SERVIR MEKONG was small. The overall accuracy of GLC_FCS30-2015 and SERVIR MEKONG was 81.10% and 82.15%, respectively, with Kappa coefficients of 0.62 and 0.63. According to the corresponding relationship between Kappa value and classification accuracy, GlC_FCS30-2015 and SERVIR MEKONG products also achieved good classification accuracy. The extraction accuracy of FROM_GLC2015 product was lower than that of the other three products, and its overall accuracy and Kappa coefficient were 77.89% and 0.53, respectively, achieving good classification results.Table 9
GFSAD30SEACE product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1626138176492.17Cropland2321174140683.50Column total185813123170PA (%)87.5189.48OA (%)88.33Kappa0.76Table 10
GLC_FCS30-2015 product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1437178161588.98Cropland4211134155572.93Column total185813123170PA (%)77.3486.43OA (%)81.10Kappa0.62Table 11
SERVIR MEKONG product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1576284186084.73Cropland2821028131078.47Column total185813123170PA (%)84.8278.35OA (%)82.15Kappa0.63Table 12
FROM_GLC2015 product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1645488213377.12Cropland213824103779.46Column total185813123170PA (%)88.5462.81OA (%)77.89Kappa0.53Table 13
GLC_FCS30-2015 product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland143713147161588.98Other cropland348376606133028.27Rice731913322559.11Column total18585267863170PA (%)77.3471.4816.92OA (%)61.39Kappa0.37Table 14
SERVIR MEKONG product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland157623153186084.73Other cropland24427631483433.09Rice381941947688.03Column total18585267863170PA (%)84.8252.4753.31OA (%)71.64Kappa0.51Table 15
FROM_GLC2015 product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland1645293195213377.12Other cropland205231571100722.94Rice82203066.67Column total18585267863170PA (%)88.5443.922.55OA (%)59.81Kappa0.27Table 16
Related research on consistency analysis of 30 m land cover products.
SourceRegionProduct categoryConclusionGao et al. [57]European UnionGlobeland30-2010, GLC_FCS30-2015, FROM_GLC2015The cropland mapping accuracy of the GlobeLand30-2010 product was the highest at99.01±0.01%; this was followed by GLC_FCS30-2015 (83.64±0.01%) and FROM_GLC2015 (63.46±0.01%).Kang et al. [15]IndonesiaGlobeLand30-2010, GLC_FCS30-2015, FROM_GLC2015The cropland mapping accuracy evaluation of the validation sample shows that GLC_FCS30-2015 products have the highest overall accuracy (85.28%); the second is the overall accuracy of GlobeLand30-2010 products (62.04%); and FROM_ GLC2015 products have the lowest overall accuracy (32.73%).Kang et al. [23]Northern LaosGlobeLand30-2010, FROM-GLC2010, SERVIR MEKONG2010The relative accuracy of cropland in SERVIR MEKONG2010 and FROM-GLC2010 is 15.55%, and that in GlobeLand30-2010 and SERVIR MEKONG2010 is 60.42%.Similarly, the confusion matrix is calculated for the secondary class cropland land types of the GLC_FCS30-2015, FROM_GLC2015, and SERVIR MEKONG products for the validation samples collected in this paper (Tables13–15). The experimental results show that the SERVIR MEKONG product secondary class cropland has the highest overall accuracy and Kappa coefficient with 71.64% and 0.51, respectively, achieving good classification results. This is followed by the GLC_FCS30-2015 product with an overall accuracy and Kappa coefficient of 61.39% and 0.37, respectively. According to the correspondence between Kappa value and classification accuracy, the classification result of GLC_FCS30-2015 product is average. In contrast, the overall precision and Kappa coefficient of the FROM_GLC2015 product second-class cropland are the lowest, at 59.81% and 0.27, respectively, with an overall average classification result.According to the evaluation results of the absolute accuracy of the first- and second-class cropland, the overall accuracy of the four first class cropland products was higher, but the overall accuracy of the more precise second-class cropland decreased significantly, especially GlC_FCS30-2015 and FROM_GLC2015 products covering the global region.


## 3.1. Area Comparison

Figure4 shows the statistical area of primary cropland and secondary rice field of four products. The results show that there is a high consistency between GFSAD30SEACE and SERVIR MEKONG, and the difference in percentage of cropland to the total area of the study area is 1.22%. GlC_FCS30-2015 product had the highest extraction area of cultivated land, with an area percentage of 37.66%. The difference between GLC_FCS30-2015 product and the other three products was 4.69% to 11.51%. The cropland area extracted from FROM_GLC2015 product is the lowest among the other three products, and the area percentage is less than 30%. For the secondary rice field type, the differences among the three products were large and the consistency was low. FROM_GLC2015 product rice field extraction area was the lowest, the area percentage was only 1.08%. And the consistency of the rice fields of GLC_FCS30-2015 and SERVIR MEKONG products was slightly better than that of the first-class cropland, with a percentage difference of 1.56% between GLC_FCS30-2015 and SERVIR MEKONG products. In conclusion, the consistency of first-class cropland area among the four types of cropland cover products is good, but the consistency of fine cropland type (such as rice) is low.Figure 4
Area comparison of different cropland products.Figure5 is the comparison result between the extracted area of first-class cropland and second-class rice field types of each product and the statistical data of FAO at the national scale. In Laos, the extracted area of first-class cropland of the four products was higher than the statistical result of FAO, among which FROM_GLC2015 product had a high consistency with FAO, and the relative error was 10.93%. As for secondary rice fields, the extraction area of GLC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 was lower than that of FAO. The consistency between SERVIR MEKONG and FAO was higher, and the relative error was 37.74%.Figure 5
Comparison of cropland extraction area and FAO statistical data at national scale: (a) cropland; (b) rice.
(a)(b)In Cambodia region, the extraction area of the first-class cropland of the four products was higher than the statistical result of FAO, and the relative error between the four products and FAO remained between 81.56% and 89.06%, indicating that the cropland area of the four products was significantly different from the statistical result of FAO. For secondary rice fields, the extraction area of GLC_FCS30-2015 and FROM_GLC2015 products was lower than the statistical FAO result, and the relative errors were 54.37% and 95.56%, respectively. The area of SERVIR MEKONG was slightly higher than that of FAO, and the relative error was only 0.54%.In Vietnam region, GFSAD30SEACE, SERVIR MEKONG, and FROM_GLC2015 product class I cropland extraction areas are higher than the FAO statistics. GFSAD30SEACE and FAO have a high consistency, and the relative error between them is 6.07%. For the secondary rice field type, the extraction area of the three products was lower than the FAO statistical result, especially the FROM_GLC2015 product, and the relative error of the two products was as high as 91.96%.In Myanmar region, the extracted area of the first-level cultivated land of four products was also higher than the statistical result of FAO, among which FROM_GLC2015 product had a high consistency with FAO, and the relative error was only 1.77%. The consistency between GFSAD30SEACE product first-class cropland extraction area and FAO was slightly higher, and the relative error was 14.58%. For the secondary rice field type, FROM_GLC2015 product had the lowest consistency with the statistical results of FAO, and the relative error was as high as 94.58%, which was similar to the comparison results in Laos, Cambodia, and Vietnam.In Thailand region, the extraction area of the first-level cultivated land of four products was higher than the statistical result of FAO, among which the difference between GLC_FCS30-2015 product and FAO was the largest, with a positive error of 36.55%. The statistical difference between SERVIR MEKONG products and FAO results was relatively small with a relative error of 24.26% for secondary rice fields. However, the FROM_GLC2015 product has the largest difference with the FAO statistical results, with a relative error of 93.99%.Figure6 shows the comparison between the extracted area of first-class cropland and secondary rice field of four cropland products in the whole Indochina Peninsula and the statistical data of FAO. GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products are all higher than the FAO statistics in terms of the area of cropland extracted from the first class, with relative errors of 19.16%, 35.84%, and 18.03%, respectively. The extracted area of FROM_GLC2015 first-class cropland was lower than the statistical result of FAO, and the consistency between the two was the highest, with a relative error of 5.80%. For secondary rice fields, GLC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 differed greatly from FAO data, especially FROM_GLC2015, which showed a relative error of 93.90%. The relative errors of SERVIR MEKONG and GLC_FCS30-2015 products with FAO statistics were 36.98% and 49.86%, respectively.Figure 6
Comparison of cropland extraction area and FAO statistical data at the study area: (a) cropland; (b) rice.
(a)(b)


## 3.2. Comparison of Spatial Pattern

### 3.2.1. Direct Comparison

Figure7 shows the spatial pattern distribution of four first-class cropland types of cropland products. The four product level categories of cropland have good spatial consistency across the board. Cropland resources in the study area are widely distributed, mainly in plains and deltas formed by sediment deposition carried by rivers, such as the Red River Delta in Vietnam, the Mekong River Plain in Thailand, the Mekong River Plain through the four countries of Laos, Cambodia, Vietnam, and Thailand, and the Mekong Delta, Salween, and Irrawaddy River regions. By comparing the spatial pattern distribution of the four first-class cropland products, the spatial pattern distribution of the four first-class cropland products was consistent in the areas with high land cover homogeneity (such as plain and delta), such as the central, southern, and northwestern Indochina Peninsula. However, in areas with high heterogeneity of land surface type (mountain area), the spatial pattern of cropland for each product varies greatly, such as the mountainous areas in the north of Red River in Vietnam, northern Laos, eastern Cambodia, northern Thailand, and some areas in eastern Myanmar.Figure 7
Spatial distribution of first-class cropland for each product.The spatial distribution of paddy field types of GlC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 was compared (Figure8). The spatial distribution of paddy field types in the whole study area was significantly different. Among them, the spatial distribution of FROM_GLC2015 product rice fields is the least, mainly distributed in the southern part of the Chao Phraya River Plain in Thailand, the Red River Delta in Vietnam, the Mekong River Delta in Vietnam, and the southern part of the Irrawaddy River in Myanmar. For GLC_FCS30-2015 products, rice fields were mainly distributed in a few areas around the Irrawaddy River, most of the Mekong River delta and Red River Delta, and the Chao Phraya River plain. For SERVIR MEKONG products, rice fields are mainly distributed around the Irrawaddy River, the Mekong Delta, the Red River Delta, the Chao Phraya Plain, and the Mekong Plain. In conclusion, the spatial pattern consistency of the four types of cropland cover products was higher in the first-class cropland type, but lower in the fine rice field type.Figure 8
Spatial distribution of rice type of each product.


### 3.2.2. Compared with Google Earth Image

Figure9 shows the spatial comparison results of cropland products in four typical regions and Google Earth images. For the Irrawaddy Delta region, the results of GFSAD30SEACE and SERVIR MEKONG are consistent with Google Earth images, followed by GLC_FCS30-2015 product extraction results. However, the cropland extraction results of FROM_GLC2015 product had the lowest consistency with Google Earth image, mainly because part of cropland was wrongly classified into built-in type. For the Mae Nam River Plain region, the GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products are in good overall agreement with Google Earth imagery, with the GLC_FCS30-2015 product classification results being more fragmented, while the FROM_GLC2015 product classification results are more different from Google Earth imagery, with serious confusion between forest, grassland, and cropland and a more fragmented spatial distribution.Figure 9
Comparison of typical regions with Google Earth image.
(a)(b)(c)(d)For the Mekong Plain region located in the country of Cambodia, the results of the four product cropland extracts were in low agreement with Google Earth imagery, mainly due to the high level of confusion between forest, grassland, and cropland types. For the Red River Delta region located in the country of Vietnam, the GFSAD30SEACE product has the best overall agreement with Google Earth imagery, and this product does a better job of removing road types. The next best agreement was with the GLC_FCS30-2015 and SERVIR MEKONG products, which misclassified a few built-ups into cultivated land types. FROM_GLC2015 has the worst agreement with Google Earth imagery, with the product misclassifying mainly cropland as forest and other types as cropland.


### 3.2.3. Spatial Consistency

Four kinds of cropland products were spatially superimposed to obtain the spatial consistency distribution atlas of different products (Figure10). Figures 10 and 11 show that the spatial pattern of cropland is less consistent across the four product classes, with 36.08% of the area of perfectly consistent cropland, mainly in the Irrawaddy River Basin, the Mekong River Plain, and the Mekong River Plain, where the homogeneity of cropland types is high. The percentage of area of completely inconsistent cropland for the four products was 21.69%, mainly in areas with a more fragmented and heterogeneous distribution of cropland. The distribution is more concentrated in the eastern part of the study area in areas of inconsistent Vietnamese national cropland, while it is scattered in other parts of the study area.Figure 10
Spatial consistency distribution of first-class cropland.Figure 11
Percentage of spatial consistency area of first-class cropland.Figures12 and 13 show the spatial consistency distribution and consistency area statistics of GlC_FCS30-2015, FROM_GLC2015, and SERVIR MEKONG paddy fields, respectively. The results show that the spatial consistency of the three product rice types is extremely low, with the percentage of fully consistent areas being only 3.03%, mainly in the Mekong Delta, the Red River Delta, and the southern Mekong River Plain and a smaller number of areas in the southern Ayeyarwady River, and with a more fragmented distribution. The area percentage of low-degree consistent area of rice field type was 23.64%, and its spatial distribution pattern was similar to that of completely consistent area. The high percentage of areas with completely disparate rice types is 73.33%, mainly in the Mekong Plain in the central part of the study area and to a lesser extent in the Irrawaddy River Basin, the Mekong Plain, the Mekong Delta, and the Red River Delta.Figure 12
Spatial consistency distribution of rice type.Figure 13
Percentage of spatial consistency area of rice type.


## 3.2.1. Direct Comparison

Figure7 shows the spatial pattern distribution of four first-class cropland types of cropland products. The four product level categories of cropland have good spatial consistency across the board. Cropland resources in the study area are widely distributed, mainly in plains and deltas formed by sediment deposition carried by rivers, such as the Red River Delta in Vietnam, the Mekong River Plain in Thailand, the Mekong River Plain through the four countries of Laos, Cambodia, Vietnam, and Thailand, and the Mekong Delta, Salween, and Irrawaddy River regions. By comparing the spatial pattern distribution of the four first-class cropland products, the spatial pattern distribution of the four first-class cropland products was consistent in the areas with high land cover homogeneity (such as plain and delta), such as the central, southern, and northwestern Indochina Peninsula. However, in areas with high heterogeneity of land surface type (mountain area), the spatial pattern of cropland for each product varies greatly, such as the mountainous areas in the north of Red River in Vietnam, northern Laos, eastern Cambodia, northern Thailand, and some areas in eastern Myanmar.Figure 7
Spatial distribution of first-class cropland for each product.The spatial distribution of paddy field types of GlC_FCS30-2015, SERVIR MEKONG, and FROM_GLC2015 was compared (Figure8). The spatial distribution of paddy field types in the whole study area was significantly different. Among them, the spatial distribution of FROM_GLC2015 product rice fields is the least, mainly distributed in the southern part of the Chao Phraya River Plain in Thailand, the Red River Delta in Vietnam, the Mekong River Delta in Vietnam, and the southern part of the Irrawaddy River in Myanmar. For GLC_FCS30-2015 products, rice fields were mainly distributed in a few areas around the Irrawaddy River, most of the Mekong River delta and Red River Delta, and the Chao Phraya River plain. For SERVIR MEKONG products, rice fields are mainly distributed around the Irrawaddy River, the Mekong Delta, the Red River Delta, the Chao Phraya Plain, and the Mekong Plain. In conclusion, the spatial pattern consistency of the four types of cropland cover products was higher in the first-class cropland type, but lower in the fine rice field type.Figure 8
Spatial distribution of rice type of each product.


## 3.2.2. Compared with Google Earth Image

Figure9 shows the spatial comparison results of cropland products in four typical regions and Google Earth images. For the Irrawaddy Delta region, the results of GFSAD30SEACE and SERVIR MEKONG are consistent with Google Earth images, followed by GLC_FCS30-2015 product extraction results. However, the cropland extraction results of FROM_GLC2015 product had the lowest consistency with Google Earth image, mainly because part of cropland was wrongly classified into built-in type. For the Mae Nam River Plain region, the GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products are in good overall agreement with Google Earth imagery, with the GLC_FCS30-2015 product classification results being more fragmented, while the FROM_GLC2015 product classification results are more different from Google Earth imagery, with serious confusion between forest, grassland, and cropland and a more fragmented spatial distribution.Figure 9
Comparison of typical regions with Google Earth image.
(a)(b)(c)(d)For the Mekong Plain region located in the country of Cambodia, the results of the four product cropland extracts were in low agreement with Google Earth imagery, mainly due to the high level of confusion between forest, grassland, and cropland types. For the Red River Delta region located in the country of Vietnam, the GFSAD30SEACE product has the best overall agreement with Google Earth imagery, and this product does a better job of removing road types. The next best agreement was with the GLC_FCS30-2015 and SERVIR MEKONG products, which misclassified a few built-ups into cultivated land types. FROM_GLC2015 has the worst agreement with Google Earth imagery, with the product misclassifying mainly cropland as forest and other types as cropland.


## 3.2.3. Spatial Consistency

Four kinds of cropland products were spatially superimposed to obtain the spatial consistency distribution atlas of different products (Figure10). Figures 10 and 11 show that the spatial pattern of cropland is less consistent across the four product classes, with 36.08% of the area of perfectly consistent cropland, mainly in the Irrawaddy River Basin, the Mekong River Plain, and the Mekong River Plain, where the homogeneity of cropland types is high. The percentage of area of completely inconsistent cropland for the four products was 21.69%, mainly in areas with a more fragmented and heterogeneous distribution of cropland. The distribution is more concentrated in the eastern part of the study area in areas of inconsistent Vietnamese national cropland, while it is scattered in other parts of the study area.Figure 10
Spatial consistency distribution of first-class cropland.Figure 11
Percentage of spatial consistency area of first-class cropland.Figures12 and 13 show the spatial consistency distribution and consistency area statistics of GlC_FCS30-2015, FROM_GLC2015, and SERVIR MEKONG paddy fields, respectively. The results show that the spatial consistency of the three product rice types is extremely low, with the percentage of fully consistent areas being only 3.03%, mainly in the Mekong Delta, the Red River Delta, and the southern Mekong River Plain and a smaller number of areas in the southern Ayeyarwady River, and with a more fragmented distribution. The area percentage of low-degree consistent area of rice field type was 23.64%, and its spatial distribution pattern was similar to that of completely consistent area. The high percentage of areas with completely disparate rice types is 73.33%, mainly in the Mekong Plain in the central part of the study area and to a lesser extent in the Irrawaddy River Basin, the Mekong Plain, the Mekong Delta, and the Red River Delta.Figure 12
Spatial consistency distribution of rice type.Figure 13
Percentage of spatial consistency area of rice type.


## 3.3. Spatial Inconsistency under Topographic Factors

In order to analyze the spatial inconsistency distribution of different cropland cover products under topographic and geomorphic characteristics, four product spatial inconsistency areas were superimposed with topographic factors to obtain the spatial distribution rules of inconsistent areas within different topographic factors. Figure14 shows the statistical results of spatial distribution and area percentage of inconsistent areas at different elevation levels. The experiments showed that the inconsistent areas of the four cropland products were mainly distributed in the range of elevation classes<100m and 100-300 m, with a percentage area of 45.34% and 32.02%, respectively. When the elevation>300m, the area of inconsistency decreases significantly, especially when the elevation>2000m, the percentage of area of inconsistency is only 0.15%. Figure 15 shows the statistical results of spatial distribution and area percentage of inconsistent areas under different slope grades. The results showed that the four kinds of inconsistent areas were mainly distributed in the slope of 2°-6° and 6°-15°, and their area percentages were 40.39% and 34.62%, respectively. However, in other slope grades, the percentage of inconsistent area decreased significantly, and the percentage was less than 15%. Figure 16 shows the statistical results of spatial distribution and area percentage of inconsistent areas at different topographic relief levels. The experimental results show that the spatial inconsistency of the four products is the highest in the range of 0-10 m, and the area percentage is 57.85%. The area percentage of spatial inconsistency in the range of 10-30 m was 37.36%. In the range of other topographic relief grades, the spatial inconsistency of the four products is low, and the area percentage is less than 5%.Figure 14
Distribution and percentage of inconsistent areas at different elevations.Figure 15
Distribution and percentage of inconsistent areas at different slopes.Figure 16
Distribution and percentage of inconsistent areas at different relief.In conclusion, the spatial inconsistency of the four cropland products is significantly affected by altitude, slope, and topographic relief. The spatial distribution of different topographic factors at different levels is different, and the area percentage is also different. Therefore, topographic factors have a certain influence on the classification accuracy of cropland cover types.


## 3.4. Accuracy Verification Based on Field Data

The absolute accuracy of the four cropland products was evaluated based on the verification samples collected by field investigation and high-resolution Google Earth image (Tables9–12). The experimental results showed that GFSAD30SEAC product had the highest overall accuracy and Kappa coefficient, which were 88.33% and 0.76, respectively, achieving a good classification accuracy. The overall accuracy of GlC_FCS30-2015 and SERVIR MEKONG was slightly lower than that of GFSAD30SEA, but the difference between GlC_FCS30-2015 and SERVIR MEKONG was small. The overall accuracy of GLC_FCS30-2015 and SERVIR MEKONG was 81.10% and 82.15%, respectively, with Kappa coefficients of 0.62 and 0.63. According to the corresponding relationship between Kappa value and classification accuracy, GlC_FCS30-2015 and SERVIR MEKONG products also achieved good classification accuracy. The extraction accuracy of FROM_GLC2015 product was lower than that of the other three products, and its overall accuracy and Kappa coefficient were 77.89% and 0.53, respectively, achieving good classification results.Table 9
GFSAD30SEACE product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1626138176492.17Cropland2321174140683.50Column total185813123170PA (%)87.5189.48OA (%)88.33Kappa0.76Table 10
GLC_FCS30-2015 product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1437178161588.98Cropland4211134155572.93Column total185813123170PA (%)77.3486.43OA (%)81.10Kappa0.62Table 11
SERVIR MEKONG product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1576284186084.73Cropland2821028131078.47Column total185813123170PA (%)84.8278.35OA (%)82.15Kappa0.63Table 12
FROM_GLC2015 product first-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandCroplandRow totalUA (%)Noncropland1645488213377.12Cropland213824103779.46Column total185813123170PA (%)88.5462.81OA (%)77.89Kappa0.53Table 13
GLC_FCS30-2015 product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland143713147161588.98Other cropland348376606133028.27Rice731913322559.11Column total18585267863170PA (%)77.3471.4816.92OA (%)61.39Kappa0.37Table 14
SERVIR MEKONG product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland157623153186084.73Other cropland24427631483433.09Rice381941947688.03Column total18585267863170PA (%)84.8252.4753.31OA (%)71.64Kappa0.51Table 15
FROM_GLC2015 product second-class cropland confusion matrix.
Reference dataData to be evaluatedNoncroplandOther croplandRiceRow totalUA (%)Noncropland1645293195213377.12Other cropland205231571100722.94Rice82203066.67Column total18585267863170PA (%)88.5443.922.55OA (%)59.81Kappa0.27Table 16
Related research on consistency analysis of 30 m land cover products.
SourceRegionProduct categoryConclusionGao et al. [57]European UnionGlobeland30-2010, GLC_FCS30-2015, FROM_GLC2015The cropland mapping accuracy of the GlobeLand30-2010 product was the highest at99.01±0.01%; this was followed by GLC_FCS30-2015 (83.64±0.01%) and FROM_GLC2015 (63.46±0.01%).Kang et al. [15]IndonesiaGlobeLand30-2010, GLC_FCS30-2015, FROM_GLC2015The cropland mapping accuracy evaluation of the validation sample shows that GLC_FCS30-2015 products have the highest overall accuracy (85.28%); the second is the overall accuracy of GlobeLand30-2010 products (62.04%); and FROM_ GLC2015 products have the lowest overall accuracy (32.73%).Kang et al. [23]Northern LaosGlobeLand30-2010, FROM-GLC2010, SERVIR MEKONG2010The relative accuracy of cropland in SERVIR MEKONG2010 and FROM-GLC2010 is 15.55%, and that in GlobeLand30-2010 and SERVIR MEKONG2010 is 60.42%.Similarly, the confusion matrix is calculated for the secondary class cropland land types of the GLC_FCS30-2015, FROM_GLC2015, and SERVIR MEKONG products for the validation samples collected in this paper (Tables13–15). The experimental results show that the SERVIR MEKONG product secondary class cropland has the highest overall accuracy and Kappa coefficient with 71.64% and 0.51, respectively, achieving good classification results. This is followed by the GLC_FCS30-2015 product with an overall accuracy and Kappa coefficient of 61.39% and 0.37, respectively. According to the correspondence between Kappa value and classification accuracy, the classification result of GLC_FCS30-2015 product is average. In contrast, the overall precision and Kappa coefficient of the FROM_GLC2015 product second-class cropland are the lowest, at 59.81% and 0.27, respectively, with an overall average classification result.According to the evaluation results of the absolute accuracy of the first- and second-class cropland, the overall accuracy of the four first class cropland products was higher, but the overall accuracy of the more precise second-class cropland decreased significantly, especially GlC_FCS30-2015 and FROM_GLC2015 products covering the global region.


## 4. Discussion

As one of the most important types of land cover, cropland has great influence on the change of global environment, climate, and ecosystem. In this paper, four kinds of land cover products with spatial resolution of 30 m are selected to conduct a comparative analysis on the accuracy of the cultivated land type and the consistency among the products. The experimental results showed that the extraction accuracy of the four products was slightly higher for the first-class cropland type, and the consistency among the products was slightly better. However, the accuracy and consistency of the four products were significantly lower for the second-class rice field type. The main reasons for the above phenomenon were as follows.First of all, there are some differences in the subordinate definitions of cropland types in the classification system of the four products. The SERVIR MEKONG products selected in this paper only cover the Indochina Peninsula region, while the other three products cover the global region. However, global characteristics need to be considered when making land cover products on a global scale, and there may be limitations when applying them to local areas [46]. Cropland types in GFSAD30SEACE products are defined as all cultivated crops (such as rice, corn, and orchards) and fallow farmland. In addition to the type defined in GFSAD30SEACE, the cropland covered by herbs is also merged into cropland in GLC_FCS30-2015 product. In addition to the type defined in GFSAD30SEACE product, the FROM_GLC2015 product also merges greenhouse type into cropland. The specific cropland cover types contained in SERVIR MEKONG are not specified. In contrast to the FAO statistics, a comparison of the cropland area of the different products shows that there are some differences between the products and the FAO statistics, mainly because the types of arable land and permanent crops are included in the FAO statistics, which are different from the classification system of the four products. So the definition of the type of cropland in the classification system therefore needs to be clearly given; otherwise, it will affect the product itself and the research associated with it.Secondly, differences in mapping strategies and methods used in the production of data by different production organizations will also have an impact on the evaluation results. The four cover products selected in this paper all use a robust random forest classification algorithm [47], which is in line with existing land cover classification studies [48–51]. The GFSAD30SEACE product was used to obtain the classification sample through a variety of means, such as fieldwork data provided by tertiary collaborators (Indonesia, Japan, Korea, Thailand, and Vietnam) [52] and data collected by the authors on their own fieldwork visits (Thailand, Myanmar, Vietnam, and Indonesia) [53], and the product was used to classify the sample with a high degree of confidence. In addition, the GFSAD30SEACE product postprocesses the raw classification results using Open Street Map Vector data (https://extract.bbbike.org) to further improve the accuracy of the classification results. The GLC_FCS30-2015 product was produced with the aid of the MCD43A4 product [54], the CCI_LC2015 product [55], and topographic data to extract the surface cover type for each area individually. The product’s classification results avoid the banding that tends to occur when using monotemporal remote sensing images for classification. The geographical coverage of SERVIR MEKONG products is small and Landsat, the surface reflectance composites, Shuttle Radar Topography Mission (SRTM), and JRC Global Surface Water Mapping Layers data were introduced in the classification process. The rice field types derived from SERVIR MEKONG products were the most accurate of the four products (Table 14). Therefore, the SERVIR MEKONG product information will be used as a reference for future extraction of fine cropland types. In the production process of FROM_GLC2015 product, the study area was divided into small regions, and then, the first global multiseason sample data was used for scene-by-scene classification [56], and then, the classification results of each scene were inlaid to obtain the land cover products of the whole region. However, it was found through this paper that some areas of the FROM_GLC2015 product were heavily striped. In addition, when cropland types are monitored by remote sensing means, the differences between their spectral and textural characteristics and other vegetation types (e.g., grassland) are small, and the phenomenon of foreign objects sharing the same spectrum tends to occur during the monitoring process of optical remote sensing images [15]. Therefore, future extraction methods for fine cropland types need to be studied in depth.Table16 shows the accuracy evaluation and analysis studies of several 30 m land cover products. The results of Gao et al. [57] and Kang et al. [15] showed that the cropland mapping accuracy of GLC_FCS30-2015 was higher than that of FROM_GLC2015 product. Kang et al. [23] evaluated the relative accuracy of SERVIR MEKONG2010, and the results showed that the cropland mapping accuracy of SERVIR MEKONG2010 was low. The results of previous studies are basically consistent with those of this paper.Finally, data preprocessing (uniform projection and classification system) may be an external factor contributing to the results of the analysis in this paper. During the preprocessing of the raw data, the original information of the data may be lost or create an imaginary confusion of information, which has an impact on the spatial overlay analysis in this paper. In addition, the accuracy of the validation samples collected by manual decoding in the experiments carried out to evaluate absolute accuracy based on validation samples can also affect the evaluation results.


## 5. Conclusions

Cropland is an essential resource and condition for people’s livelihoods, and its quantity and quality are important for sustainable agricultural development. This paper presents an analytical study to assess the cropland types of four land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM-GLC2015, and SERVIR MEKONG) at a spatial resolution of 30 m, which led to the following conclusions. (1) There is a small variation in the area of cropland types in the first-class for different products, but there is a large variation in the area of rice land types in the secondary class, and there are some differences between the FAO statistics. (2) The spatial pattern distribution of the first-class of cropland types varied little among the products, with the GFSAD30SEACE product showing the best spatial agreement with Google Earth imagery. However, the spatial consistency of the secondary class of rice field types is extremely low, with the percentage of area of perfectly consistent rice fields being only 3.03%, mainly in the Mekong Delta, Red River Delta, southern Mekong River Plain, and southern Irrawaddy River regions of the study area. (3) Areas of spatial inconsistency between the four product cropland types are mainly located in the range of elevation (<300 m), slope (2°-15°), and topographic relief (0-10 m). (4) The overall accuracy of the GFSAD30SEACE, GLC_FCS30-2015, and SERVIR MEKONG products for the first-class of cropland types is above 81%, while the overall accuracy of the secondary class of cropland types is lower except for the SERVIR MEKONG product (71.64%). Therefore, the mapping accuracy of the finer cropland types needs to be improved.



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*Source: 2876976-2022-07-16.xml*