A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.

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Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

Remote Sensing ◽

10.3390/rs11151823 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1823 ◽

Cited By ~ 9

Author(s):

Xiaojuan Huang ◽

Jingfeng Xiao ◽

Mingguo Ma

Keyword(s):

Primary Productivity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Gross Primary Productivity ◽

Surface Reflectance ◽

Monthly Scale ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

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Livestock intensification potential in Brazil based on agricultural census and satellite data analysis

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2018000900009 ◽

2018 ◽

Vol 53 (9) ◽

pp. 1053-1060 ◽

Cited By ~ 2

Author(s):

Arielle Elias Arantes ◽

Victor Rezende de Moreira Couto ◽

Edson Eyji Sano ◽

Laerte Guimarães Ferreira

Keyword(s):

Primary Productivity ◽

Carrying Capacity ◽

Dry Matter ◽

Census Data ◽

Stocking Rate ◽

Agricultural Census ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Stocking Rates

Abstract: The objective of this work was to evaluate the potential of livestock intensification in Brazil. Beef cattle stocking rates were estimated according to agricultural census data on livestock production in Brazilian municipalities. Pasture carrying capacity was obtained by combining moderate resolution imaging spectroradiometer (Modis) images of gross primary productivity and data on dry matter demand per animal unit (AU). Cattle stocking rate for Brazil, in 2014/2015, was 0.97 AU ha-1, and the carrying capacity was 3.60 AU ha-1; therefore, there is an average livestock intensification potential of 2.63 AU ha-1. The highest average intensification potential was observed for the Southern region (3.62 AU ha-1), and the lowest for the Northern (2.13 AU ha-1) and Northeastern regions (2.22 AU ha-1). It is possible to estimate cattle stocking rate, pasture carrying capacity, and potential of livestock intensification by integrating data on agricultural census and remote sensing.

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Response of net primary productivity to grassland phenological changes in Xinjiang, China

PeerJ ◽

10.7717/peerj.10650 ◽

2021 ◽

Vol 9 ◽

pp. e10650

Author(s):

Renping Zhang ◽

Jing Guo ◽

Gang Yin

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Growing Season ◽

Casa Model ◽

Phenological Changes ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Phenological Metrics ◽

The Impact

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

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KUALITAS LINGKUNGAN PERAIRAN DAN POTENSI PRODUKSI IKAN KAWASAN KONSERVASI TERUBUK BENGKALIS (Tenualosa macrura Bleeker, 1852)

Jurnal Penelitian Perikanan Indonesia ◽

10.15578/jppi.1.1.2018.37-49 ◽

2018 ◽

Vol 1 (1) ◽

pp. 37

Author(s):

Khairul Amri ◽

Gathot Winarso ◽

Muchlizar Muchlizar

Keyword(s):

Water Quality ◽

Satellite Imagery ◽

Primary Productivity ◽

Landsat 8 ◽

Productivity Level ◽

Mangrove Vegetation ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Terubuk Bengkalis (Tenualosa macrura) yang hidup di perairan Bengkalis dinyatakan terancam punah akibat eksploitasi berlebih dan penurunan kualitas perairan. Penelitian ini bertujuan untuk menganalisa kualitas perairan habitat terubuk Bengkalis, menggunakan data parameter oseanografi hasil pengukuran in-situ. Selain itu, data penginderaan jauh berupa citra Landsat 8 digunakan untuk analisa tutupan mangrove (hutan bakau) serta citra MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer) untuk analisis produktivitas primer (NPP). Penelitian dilaksanakan selama April-November 2015. Hasil penelitian menunjukkan total luas tutupan mangrove yang teridentifikasi citra Satelit Landsat 8 (2015) mencapai 11.736,2 Ha, berkurang sekitar 4.470,8 Ha dalam waktu 12 tahun (2003-2015) dengan laju kehilangan 372,5 Ha/tahun. Dari aspek oseanografi, kawasan konservasi ikan terubuk merupakan perairan dangkal dengan tingkat kecerahan rendah (0,54-0,95 m); suhu perairan relatif tinggi berkisar 29,15-31,87 0C (rata-rata 300C) dan salinitas rata-rata tergolong rendah (28,77-29,22 ppt). Nilai sebaran pH dan oksigen terlarut/DO juga rendah yakni pH 6,3-8,9 (rata-rata pH 7) dan DO 3,90-4,98 mg/l (di bawah Baku Mutu Air Laut). Komposisi substrat dasar didominasi lumpur, dengan prosentase 67,4-89,8%, sehingga perairan ini umumnya keruh. Perairan ini tergolong subur (eutropik) dengan kelimpahan fitoplankton tinggi (23.584 - 95.616 sel/l) terdiri dari 32-52 genera. Produktivitas primer juga tinggi, rata-rata 288,87 mgC/m2/hari dengan potensi produksi ikan 3.680,2 ton/tahun. Terubuk Bengkalis (Tenualosa macrura) is an endemic tropical shad fish that live in Bengkalis waters.This species has been declared endangered due to over exploitation and environmental degradation. The current research aimed to analyze the environmental quality of the species. The data used in this research were consited of in-situ measurement and remote sensing data: Landsat 8 Satellite imagery for mangrove cover observation and MODIS (Moderate-resolution Imaging Spectroradiometer) imagery for Net Primary Productivity (NPP). The results showed that the cover of mangrove vegetation along the coast of Bengkalis Island dentified by Landsat 8 Satellite imagery was11.736,24 Ha. The total loss of cover mangrove vegetation is estimated about 4.470,83 Ha, decreased drastically in 12 years (2003-2015) with a loss rate of 372.5 Ha/year. The habitat of shad fish is shallow water category. The water quality was too turbid (brightness level 0.54-0.95 m); warm water temperature of 29.,15-31.87 0C (average 300C); and low salinity (28,77-29,22 ppt). The relatively low pH and dissolved oxygen content were determined: pH ranged between 6,3-8,9 (mean7) and the DO: 3,90-4,98 mg/l (under the Sea Water Quality Standard).The substrat was dominated by mud (67,4-89,8%) in Bengkalis Strait sub area due to the turbidity. However, these waters are euthropic level category with a high abundance of phytoplankton ranging from 23,584-95,616 cells/l and the species richness varies from 32-52 species. The primary productivity level of waters was also quite high was average of 288,87 mgC/m2/day resulting an estimation of potential of fish biomass value about 3.680,2 ton/year.

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Prediction of High-Quality MODIS-NPP Product Data

Remote Sensing ◽

10.3390/rs11121458 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1458

Author(s):

Zhenhua Liu ◽

Ting Wang ◽

Yonghua Qu ◽

Huiming Liu ◽

Xiaofang Wu ◽

...

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Ecological Indicator ◽

Discrete Wavelet ◽

Atmospheric Variability ◽

High Quality ◽

Aerosol Pollution ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data.

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Detecting Snowfall Events over Mountainous Areas Using Optical Imagery

Water ◽

10.3390/w10111514 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1514

Author(s):

Jiangeng Wang ◽

Yonghong Zhang ◽

Yinyi Cheng ◽

Xueliang Zhang ◽

Xuezhi Feng ◽

...

Keyword(s):

Water Cycle ◽

Northwest China ◽

Radiation Balance ◽

Mountainous Area ◽

Mountainous Areas ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Time Series Images ◽

Radiance Data

Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter.

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Influence of Straw Burning on Urban Air Pollutant Concentrations in Northeast China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16081379 ◽

2019 ◽

Vol 16 (8) ◽

pp. 1379 ◽

Cited By ~ 4

Author(s):

Zhenzhen Wang ◽

Jianjun Zhao ◽

Jiawen Xu ◽

Mingrui Jia ◽

Han Li ◽

...

Keyword(s):

Northeast China ◽

Arable Land ◽

Pollution Index ◽

Air Pollutant ◽

Thermal Anomalies ◽

Pollutant Concentrations ◽

Moderate Resolution ◽

Resolution Imaging ◽

Straw Burning ◽

Moderate Resolution Imaging Spectroradiometer

Northeast China is China’s primary grain production base. A large amount of crop straw is incinerated every spring and autumn, which greatly impacts air quality. To study the degree of influence of straw burning on urban pollutant concentrations, this study used The Moderate-Resolution Imaging Spectroradiometer/Terra Thermal Anomalies & Fire Daily L3 Global 1 km V006 (MOD14A1) and The Moderate-Resolution Imaging Spectroradiometer/Aqua Thermal Anomalies and Fire Daily L3 Global 1 km V006 (MYD14A1) data from 2015 to 2017 to extract fire spot data on arable land burning and to study the spatial distribution characteristics of straw burning on urban pollutant concentrations, temporal variation characteristics and impact thresholds. The results show that straw burning in Northeast China is concentrated in spring and autumn; the seasonal spatial distributions of PM2.5, PM10 andAir Quality Index (AQI) in 41 cities or regions in Northeast China correspond to the seasonal variation of fire spots; and pollutants appear in the peak periods of fire spots. In areas where the concentration coefficient of rice or corn is greater than 1, the number of fire spots has a strong correlation with the urban pollution index. The correlation coefficient R between the number of burned fire spots and the pollutant concentration has a certain relationship with the urban distribution. Cities are aggregated in geospatial space with different R values.

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A Climatological Assessment of Intense Desert Dust Episodes over the Broader Mediterranean Basin Based on Satellite Data

Remote Sensing ◽

10.3390/rs13152895 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2895

Author(s):

Maria Gavrouzou ◽

Nikolaos Hatzianastassiou ◽

Antonis Gkikas ◽

Christos J. Lolis ◽

Nikolaos Mihalopoulos

Keyword(s):

Mediterranean Basin ◽

Eastern Mediterranean ◽

Mean Value ◽

Early Spring ◽

Dust Aerosol ◽

Ozone Monitoring Instrument ◽

Central Mediterranean ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

A satellite algorithm able to identify Dust Aerosols (DA) is applied for a climatological investigation of Dust Aerosol Episodes (DAEs) over the greater Mediterranean Basin (MB), one of the most climatologically sensitive regions of the globe. The algorithm first distinguishes DA among other aerosol types (such as Sea Salt and Biomass Burning) by applying threshold values on key aerosol optical properties describing their loading, size and absorptivity, namely Aerosol Optical Depth (AOD), Aerosol Index (AI) and Ångström Exponent (α). The algorithm operates on a daily and 1° × 1° geographical cell basis over the 15-year period 2005–2019. Daily gridded spectral AOD data are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Collection 6.1, and are used to calculate the α data, which are then introduced into the algorithm, while AI data are obtained by the Ozone Monitoring Instrument (OMI) -Aura- Near-UV aerosol product OMAERUV dataset. The algorithm determines the occurrence of Dust Aerosol Episode Days (DAEDs), whenever high loads of DA (higher than their climatological mean value plus two/four standard deviations for strong/extreme DAEDs) exist over extended areas (more than 30 pixels or 300,000 km2). The identified DAEDs are finally grouped into Dust Aerosol Episode Cases (DAECs), consisting of at least one DAED. According to the algorithm results, 166 (116 strong and 50 extreme) DAEDs occurred over the MB during the study period. DAEDs are observed mostly in spring (47%) and summer (38%), with strong DAEDs occurring primarily in spring and summer and extreme ones in spring. Decreasing, but not statistically significant, trends of the frequency, spatial extent and intensity of DAECs are revealed. Moreover, a total number of 98 DAECs was found, primarily in spring (46 DAECs) and secondarily in summer (36 DAECs). The seasonal distribution of the frequency of DAECs varies geographically, being highest in early spring over the eastern Mediterranean, in late spring over the central Mediterranean and in summer over the western MB.

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