An index of non-sampling error in area frame sampling based on remote sensing data

Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units

The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data

Terrestrial ecosystems greatly contribute to carbon (C) emission reduction targets through photosynthetic C uptake.Net primary production (NPP) represents the amount of atmospheric C fixed by plants and accumulated as biomass. The Three-North Shelterbelt Program (TNSP) zone accounts for more than 40% of China’s landmass. This zone has been the scene of several large-scale ecological restoration efforts since the late 1990s, and has witnessed significant changes in climate and human activities.Assessing the relative roles of different causal factors on NPP variability in TNSP zone is very important for establishing reasonable local policies to realize the emission reduction targets for central government. In this study, we examined the relative roles of drought and land cover conversion(LCC) on inter-annual changes of TNSP zone for 2001–2010. We applied integrated correlation and decomposition analyses to a Standardized Evapotranspiration Index (SPEI) and MODIS land cover dataset. Our results show that the 10-year average NPP within this region was about 420 Tg C. We found that about 60% of total annual NPP over the study area was significantly correlated with SPEI (p<0.05). The LCC-NPP relationship, which is especially evident for forests in the south-central area, indicates that ecological programs have a positive impact on C sequestration in the TNSP zone. Decomposition analysis generally indicated that the contributions of LCC, drought, and other Natural or Anthropogenic activities (ONA) to changes in NPP generally had a consistent distribution pattern for consecutive years. Drought and ONA contributed about 74% and 23% to the total changes in NPP, respectively, and the remaining 3% was attributed to LCC. Our results highlight the importance of rainfall supply on NPP variability in the TNSP zone

Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index

Eutrophication of inland waters is considered a serious global environmental problem. Satellite remote sensing (RS) has been established as an important source of information to determine the trophic state of inland waters through the retrieval of optically active water quality parameters such as chlorophyll-a (Chl-a). However, the use of RS techniques for assessment of the trophic state of inland waters on a global scale is hindered by the performance of retrieval algorithms over highly dynamic and complex optical properties that characterize many of these systems. In this study, we developed a new RS approach to assess the trophic state of global inland water bodies based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and the Forel-Ule index (FUI). First, the FUI was calculated from MODIS data by dividing natural water colour into 21 indices from dark blue to yellowish-brown. Then the relationship between FUI and the trophic state index (TSI) was established based on in-situ measurements and MODIS products. The water-leaving reflectance at 645 nm band was employed to distinguish coloured dissolved organic matter (CDOM)-dominated systems in the FUI-based trophic state assessment. Based on the analysis, the FUI-based trophic state assessment method was developed and applied to assess the trophic states of 2058 large inland water bodies (surface area >25 km2) distributed around the world using MODIS data from the austral and boreal summers of 2012. Our results showed that FUI can be retrieved from MODIS with a considerable accuracy (92.5%, R2 = 0.92) by comparing with concurrent in situ measurements over a wide range of lakes, and the overall accuracy of the FUI-based trophic state assessment method is 80.0% (R2 = 0.75) validated by an independent dataset. Of the global large water bodies considered, oligotrophic large lakes were found to be concentrated in plateau regions in central Asia and southern South America, while eutrophic large lakes were concentrated in central Africa, eastern Asia, and mid-northern and southeast North America

Response of Spectral Reflectances and Vegetation Indices on Varying Juniper Cone Densities

Juniper trees are widely distributed throughout the world and are common sources of allergies when microscopic pollen grains are transported by wind and inhaled. In this study, we investigated the spectral influences of pollen-discharging male juniper cones within a juniper canopy. This was done through a controlled outdoor experiment involving ASD FieldSpec Pro Spectroradiometer measurements over juniper canopies of varying cone densities. Broadband and narrowband spectral reflectance and vegetation index (VI) patterns were evaluated as to their sensitivity and their ability to discriminate the presence of cones. The overall aim of this research was to assess remotely sensed phenological capabilities to detect pollen-bearing juniper trees for public health applications. A general decrease in reflectance values with increasing juniper cone density was found, particularly in the Green (545–565 nm) and NIR (750–1,350 nm) regions. In contrast, reflectances in the shortwave-infrared (SWIR, 2,000 nm to 2,350 nm) region decreased from no cone presence to intermediate amounts (90 g/m2) and then increased from intermediate levels to the highest cone densities (200 g/m2). Reflectance patterns in the Red (620–700 nm) were more complex due to shifting contrast patterns in absorptance between cones and juniper foliage, where juniper foliage is more absorbing than cones only within the intense narrowband region of maximum chlorophyll absorption near 680 nm. Overall, narrowband reflectances were more sensitive to cone density changes than the equivalent MODIS broadbands. In all VIs analyzed, there were significant relationships with cone density levels, particularly with the narrowband versions and the two-band vegetation index (TBVI) based on Green and Red bands, a promising outcome for the use of phenocams in juniper phenology trait studies. These results indicate that spectral indices are sensitive to certain juniper phenologic traits that can potentially be used for juniper cone detection in support of public health applications

A Novel in Situ FPAR Measurement Method for Low Canopy Vegetation Based on a Digital Camera and Reference Panel

Abstract: The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in describing the exchange of fluxes of energy, mass and momentum between the surface and atmosphere. In this study, we present a method to measure FPAR using a digital camera and a reference panel. A digital camera was used to capture color images of low canopy vegetation, which contained a reference panel in one corner of the field of view (FOV). The digital image was classified into photosynthetically active vegetation, ground litter, sunlit soil, shadow soil, and the reference panel. The relative intensity of the incident photosynthetically active radiation (PAR), scene-reflected PAR, exposed background absorbed PAR and the green vegetation-covered ground absorbed PAR were derived from the digital camera image, and then FPAR was calculated. This method was validated on eight plots with four vegetation species using FPAR measured by a SunScan instrument. A linear correlation with a coefficient of determination (R 2) of 0.942 and mean absolute error (MAE) of 0.031 was observed between FPAR values derived from the digital camera and measurement using the SunScan instrument. The result suggests that the present method can be used to accurately measure the FPAR of low canopy vegetation

Progress and Trends in the Application of Google Earth and Google Earth Engine

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective

Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data

Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data