MODIS Land Surface Reflectance Research Articles

Understanding the states and dynamics of mangrove forests in land cover transitions of The Gambia using a Fourier transformation of Landsat and MODIS time series in Google Earth Engine

IntroductionMangroves are resilient forests of transitional zones between land, ocean and freshwater for they are tolerant to salinity. In The Gambia, mangrove forests are found along the coast of Atlantic Ocean and River Gambia where they support the livelihoods of millions through multiple ecosystems services. Despite their importance in The Gambia, consistent country-wide information on their coverage, dynamics and change hotspots are lacking. Thus far, it remains unclear whether the coverage of mangroves has decreased or increased over the last few decades. Often, the existing estimates vary strongly across sources and the methodologies in the available literature are not always reproducible. This study attempts to fill these gaps by providing up-to-date spatial information on mangrove forests in The Gambia.MethodsTo provide a reproducible workflow and a comprehensive assessment, we used continuous time series of freely available data to study the extent and dynamics of mangrove forests in The Gambia. To construct gap-free image time series, we used statistical models to describe land surface phenology based on monthly composites derived from Landsat and MODIS land surface reflectance acquired between 2000 and 2020. We used 1212 Landsat and 407 MODIS tiles spread across multiple spectral indices along with a calibrated set of training and validation data to train and validate a random forest classifier for accurate land cover classifications.Results and discussionThe overall accuracy and Kappa statistics of the classifications range between 0.96 and 0.98. Our findings suggest a net increase of 4,000 ha in mangrove forests over the last 2 decades, corresponding to an annual rate of 200 ha. The net increase is largely due to strong policy making which results in participative forest resource management through the national forest action plan. The net increase in mangrove forests should not mask the substantial degradation in some places across the country. We estimated a total loss of nearly 5,670 ha, most of which appears to have taken place during the last decade in favor of wetlands. The Gambian mangroves are amongst the most promising green business in Gulf of Guinea, deserving an integrated governance - policy framework that address the key requirements for ecological sustainability.

Compositing the Minimum NDVI for MODIS Data

The maximum and minimum normalized difference vegetation indexes (NDVIs) describe two extremes of vegetation greenness during a predefined period. A maximum NDVI image can be composited easily via the direct selection of the maximum NDVI from multiple observations without the need to mask out cloud or snow. But, a minimum NDVI image cannot be built in a similar manner. In this paper, an approach was proposed to composite the minimum NDVI (the least vegetation greenness) image. The minimum spectral index that consists of the green (555 nm) and SWIR bands (2130 nm) from MODIS data, which was named here as the Brown Vegetation Index (BVI), was taken as a proxy to composite the minimum vegetation NDVI. This composite method performs well on a global scale for the NDVIs that were derived from MODIS land surface reflectance (MOD09A1) products. The BVI-based minimum NDVI was compared with the direct selection of the minimum NDVI after excluding contaminated observations using a refined cloud/snow mask. The comparison shows that the difference for 97% of the minimum NDVI between the two approaches is within the range of ±0.1 NDVI unit. Various potential spectral indices for compositing the minimum NDVI were compared, which demonstrated the BVI-based approach was top rated. Several examples demonstrated that the composited minimum NDVI is valuable and effective for identifying evergreen forests, monsoon forests, and double cropping. The minimum NDVI combined with the maximum NDVI would simplify the way to describe intraannual vegetation changes.

A Method for Retrieving Daily Land Surface Albedo from Space at 30-m Resolution

Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA) from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028).

Atmospheric correction of HJ-1 A/B CCD over land: Land surface reflectance calculation for geographical information product

This paper proposed a method to retrieve the land surface reflectance from the HJ-1A/B CCD data. The aerosol optical depth (AOD), the most important factor affecting the atmospheric correction of CCD images at all bands, is proposed to retrieve from the CCD imagery by the approach of dense dark vegetation (DDV) method. A look-up table in terms of the transmittances, the path radiances and the atmospheric spherical albedo as functions of the AOD was established for a variety of sun-sensor geometry and aerosol loadings. The atmospheric correction is then achieved with the look-up table and the MODIS surface reflectance output (MOD09) as the priori datasets. Based on the retrieved AOD and the look-up table of atmospheric correction coefficients, the land surface reflectance was retrieved for the HJ-1A/B data according to the atmospheric radiative transfer equation. Some in-situ measurement Data for Yanzhou of Shandong province in East China and MODIS land surface reflectance products MOD09 are used to preliminarily validate the proposed method. The results show that the proposed method can remove effectively the atmospheric contributions, and the overall accuracy of the retrieval land surface reflectance can be improved substantially.

Inter-Comparison and Validation of the FY-3A/MERSI LAI Product Over Mainland China

Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval.