The Landsat program is the longest EO program and has the biggest global user
community due to its easy access, especially after USGS made all the data free
online in 2008. Now the data are not only available in the USGS archive but also on
commercial cloud data centers like the Amazon Web Service (AWS) (Varia and
Mathew 2014) and Google Earth Engine (GEE) (Gorelick 2013). Total seven
satellites of the program so far (the sixth failed) have harvested millions of scenes
since 1972 and gave a long-term and complete view of the entire Earth. The 30-m
resolution is good enough for many general application scenarios. Therefore, the
Landsat datasets are massively used by all walks of life and benefits many industries.
Agriculture, no doubt, is one of them. Both academic and industrial communities of
agriculture are using the Landsat data. We have seen a bunch of papers and news
about it (Jurgens 1997; Ozelkan et al. 2016; Sheoran and Haack 2013; Zhong et al.
2014).
Terra and Aqua are another two popular EO satellites launched by NASA
(Savtchenko et al. 2004). Since their launch, Terra has worked for 17 years and
Aqua worked for 15 years. Both carry multiple EO instruments. We focus on
the MODIS instrument as the others aim at observing the atmosphere and ocean.
MODIS has a mature product hierarchy which classifies the products into several
levels. MODIS land products are related to agriculture. For example, the surface
reflectance product (09GA, 09GQ, 09A1, 09Q1) can reflect the true spectral characteristics of the crops. The land cover annual product (12Q1, 12C1, 12Q2) gives an
important reference to the surface change. The land surface temperature and emissivity product (11A1, 11A2, 11B1, 11_L2, 11C1, 11C2, 11C3, 21_L2, 21A1, 21A2)
estimates the temperature and emission in 1-km grid. The vegetation index products
(13Q1, 13A1, 13A2, 13C1, 13A3, 13C2) calculate NDVI (normalized difference
vegetation index) and EVI (enhanced vegetation index) on a 16-day interval at
multiple spatial resolutions (250 m, 500 m, 1 km, 0.05 degrees). The gross primary
production (GPP) and net primary production (NPP) products (17A2, 17A3) can
provide an accurate measure of the growth of the terrestrial vegetation including
crops regularly. The vegetation continuous field products (44B) estimate the portion
of vegetation cover in each pixel. The leaf area index (LAI) products (15A2H,
15A3H) calculate the LAI which is the one-sided green leaf area per unit ground
area in broadleaf canopies and half the total needle surface area per unit ground area
in coniferous canopies. The evapotranspiration products estimate global terrestrial
evapotranspiration from land surface. All the products are calculated on both Terra
(morning, code MOD) and Aqua (afternoon, code MYD) data. These products have
tight relations with vegetation, and the agricultural community has adopted some of
them to monitor the open crops (with no greenhouse). However, the resolution is low
(250 m ground resolution at the best) so only large-scale studies can apply.
SMAP, short for soil moisture active passive, is of concern to many people since
its launch. Because the soil moisture product is so rare, it has great demands in the
market. Soil moisture data could bring various benefits in improving weather forecasts, monitoring droughts, predicting floods, assisting crop productivity, and breaking down the water-energy-carbon cycles. Its significance to agriculture is selfevident. SMAP produces global maps of soil moisture with near-global coverage
4 Agro-geoinformatics Data Sources and Sourcing
45
community due to its easy access, especially after USGS made all the data free
online in 2008. Now the data are not only available in the USGS archive but also on
commercial cloud data centers like the Amazon Web Service (AWS) (Varia and
Mathew 2014) and Google Earth Engine (GEE) (Gorelick 2013). Total seven
satellites of the program so far (the sixth failed) have harvested millions of scenes
since 1972 and gave a long-term and complete view of the entire Earth. The 30-m
resolution is good enough for many general application scenarios. Therefore, the
Landsat datasets are massively used by all walks of life and benefits many industries.
Agriculture, no doubt, is one of them. Both academic and industrial communities of
agriculture are using the Landsat data. We have seen a bunch of papers and news
about it (Jurgens 1997; Ozelkan et al. 2016; Sheoran and Haack 2013; Zhong et al.
2014).
Terra and Aqua are another two popular EO satellites launched by NASA
(Savtchenko et al. 2004). Since their launch, Terra has worked for 17 years and
Aqua worked for 15 years. Both carry multiple EO instruments. We focus on
the MODIS instrument as the others aim at observing the atmosphere and ocean.
MODIS has a mature product hierarchy which classifies the products into several
levels. MODIS land products are related to agriculture. For example, the surface
reflectance product (09GA, 09GQ, 09A1, 09Q1) can reflect the true spectral characteristics of the crops. The land cover annual product (12Q1, 12C1, 12Q2) gives an
important reference to the surface change. The land surface temperature and emissivity product (11A1, 11A2, 11B1, 11_L2, 11C1, 11C2, 11C3, 21_L2, 21A1, 21A2)
estimates the temperature and emission in 1-km grid. The vegetation index products
(13Q1, 13A1, 13A2, 13C1, 13A3, 13C2) calculate NDVI (normalized difference
vegetation index) and EVI (enhanced vegetation index) on a 16-day interval at
multiple spatial resolutions (250 m, 500 m, 1 km, 0.05 degrees). The gross primary
production (GPP) and net primary production (NPP) products (17A2, 17A3) can
provide an accurate measure of the growth of the terrestrial vegetation including
crops regularly. The vegetation continuous field products (44B) estimate the portion
of vegetation cover in each pixel. The leaf area index (LAI) products (15A2H,
15A3H) calculate the LAI which is the one-sided green leaf area per unit ground
area in broadleaf canopies and half the total needle surface area per unit ground area
in coniferous canopies. The evapotranspiration products estimate global terrestrial
evapotranspiration from land surface. All the products are calculated on both Terra
(morning, code MOD) and Aqua (afternoon, code MYD) data. These products have
tight relations with vegetation, and the agricultural community has adopted some of
them to monitor the open crops (with no greenhouse). However, the resolution is low
(250 m ground resolution at the best) so only large-scale studies can apply.
SMAP, short for soil moisture active passive, is of concern to many people since
its launch. Because the soil moisture product is so rare, it has great demands in the
market. Soil moisture data could bring various benefits in improving weather forecasts, monitoring droughts, predicting floods, assisting crop productivity, and breaking down the water-energy-carbon cycles. Its significance to agriculture is selfevident. SMAP produces global maps of soil moisture with near-global coverage
4 Agro-geoinformatics Data Sources and Sourcing
45