SMOS optical thickness changes in response to the growth and development of crops, crop management, and weather

Abstract

7The Soil Moisture and Ocean Salinity (SMOS) remote sensing satellite was launched by the European Space Agency in 2009. The L-band brightness temperature observed by SMOS has been used to produce estimates of both soil moisture and τ , the optical thickness of the land surface. Although τ should theoretically be proportional to the amount of vegetation present within a SMOS pixel, several initial investigations have not been able to confirm this expected behavior. However, when the noise in the SMOS τ product is removed, τ in the U.S. Corn Belt, a region of extensive row-crop agriculture, has a distinct shape that mirrors the growth and development of crops. We find that the peak value of SMOS τ occurs at approximately 1000 • C day (base 10 • C) growing degree days after the mean planting date of maize (corn). We can explain this finding in the following way:τ is directly proportional to the water column density of vegetation; maize contributes the most to growing season changes in τ in the Corn Belt; and maize reaches its maximum water column density at its third reproductive stage of development, at about 1000 • C day growing degree days.Consequently, SMOS τ could be used to monitor the phenology of crops in the Corn Belt at a spatial resolution similar to a U.S. county and a temporal frequency on the order of days. We also examined the magnitude of the change in SMOS τ over the growing season and hypothesized it would be related to the amount of accumulated solar radiation, but found this not to be the case.On the other hand, the change in magnitude was smallest for the year in which the most precipitation fell. These findings are rational since SMOS τ at the satellite scale is in fact a function of both vegetation and soil surface roughness, and soil surface roughness is reduced by precipitation.To fully explain changes in SMOS τ in the Corn Belt it appears that it will be necessary to use in situ and remotely-sensed observations along with agro-ecosystem models to account for land management decisions made by farmers that affect changes in soil surface roughness and all of the relevant biophysical processes that affect the growth and development of crops.

Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

“…Limitations aside, L2VOD provides useful information about the status of crop progress. SMOS L2VOD, similar to SMAP but retrieved using a large range of observed θ, peaks after having accumulated a thermal time of approximately 1000 • C · day post-planting in the U. S. Corn Belt [8]. This occurs between the second and third reproductive developmental stages of corn when M w , defined as the mass of water in vegetation tissue per ground area, of the mixed corn and soybean canopy is at a maximum [8].…”

“…The SMAP mission accuracy goal is to retrieve soil moisture within ± 0.04 m 3 m −3 of in situ observations for surfaces with a water column density, M w , of less than 5 kg m −2 [1]. While corn on its own can exceed this threshold during a growing season, pixels characterized by mixed corn and soybean in Iowa typically reach a maximum M w of 4 kg m −2 [8].…”

“…The times of largest ubRMSE correspond with the period when the climatological vegetation used by both SCA is potentially out-of-sync actual crop growth. SMOS Level 2 VOD (L2VOD), to which we assume SMAP L2VOD is similar, is proportional to corn and soybean development in the Corn Belt and therefore deviates from the climatological VOD due to annual variations in weather and farm management decisions [8]. Figure 2 presents a sample timeseries of the climatological VOD used by the SCA to correct for the effect of vegetation and the L2VOD retrieved by the DCA during soil moisture retrieval.…”

“…Case studies of the historic 1988 drought and 1993 floods in the U. S. Corn Belt have shown that ingesting soil moisture data during numerical weather prediction improves forecasts of event location and intensity [5,6]. L-band observations are additionally being utilized as agricultural products, with applications such as root-zone soil moisture [7] and preliminary monitoring of corn development [8].…”

Evaluation of Near-Surface Air Temperature From Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

“…Reanalysis products have also been exploited in agricultural studies. In particular, near-surface air temperature (T2M) data has proven to be a relevant input in the forecast of crop yields and production from land modeling and remote sensing approaches, as it allows to estimate parameters such as evapotranspiration [8], [9] and Growing Degree Day (GDD), which represents the amount of heat energy accumulated by an organism, a driving factor in phenological development [10], [11], and it has been widely used in local [12]- [14] and regional [15]- [19] scale crop forecasting models, and as a source of information in the early-season mapping of wheat areas [20]. Near-surface air temperature data is generally obtained from measurements made by weather stations.…”