A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

Abstract

Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. 3332The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

“…The SCAN site provides hourly data at depths of approximately 5, 10, 20, 50 and 100 cm. Although Al-Hamdan and Cruise [22] and Singh [28,43] provided extensive verification of entropy-based profiles in a laboratory setting, whereas Mishra et al [30] applied POME-generated profiles for crop yield estimations, no verification has been done up to this time using actual field soil moisture data. The verifications were done for all possible cases.…”

“…The field is under center pivot irrigation. Alabama is a subtropical, humid region in the Southeastern United States and receives over 1400 mm of precipitation annually [30]. However, less than 300 mm of precipitation occurs during the growing season, on average, and thus, water-intensive crops, such as corn, can benefit greatly from supplemental irrigation [48].…”

“…In many cases, the precipitation is not distributed uniformly throughout the year, so that growing season rainfall may not be sufficient to supply crop requirements on a sustainable basis [30]. The method chosen for the delivery of irrigation water is an important element in determining irrigation efficiency.…”

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

“…Several workable approaches have been developed including the Surface Energy Balance Algorithm for Land (SEBAL; Bastiaanssen et al, 1998), the Mapping Evapotranspiration with Internalized Calibration (METRIC; Allen et al, 2007), the Two Source Energy Balance model (TSEB; Norman et al, 1995), and the Atmosphere-Land Exchange Inverse model (ALEXI; Anderson et al, 1997Anderson et al, , 2007a and an associated disaggregation algorithm (DisALEXI; Anderson et al, 2004). These TIR-based ET mapping algorithms provide regional and global coverage efficiently and economically, motivating studies relating remote sensing-based ET estimates to crop productivity (Bastiaanssen and Ali, 2003;Mishra et al, 2013;Tadesse et al, 2015;Anderson et al, 2016aAnderson et al, , 2016bMladenova et al, 2017). Anderson et al (2007a) proposed the Evaporative Stress Index (ESI) as a new remote sensing drought indicator, which is based on temporal anomalies in f RET retrieved using TIR imagery from geostationary (GEO) satellites.…”

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

“…Because the regression relationship varies largely on a year-to-year basis due to inter-annual variations in climate, water availability, and management practices, the application of these models is limited to the studied regions and periods and is difficult under extreme conditions (e.g., flooding and drought) beyond historical records. Another approach applies satellite data to calibrate physiology-based crop models [19][20][21] that simulate the physical process of crop growth, where energy, water, carbon dioxide, and nutrients are converted into biomass [22,23]. These models have proven useful at the field scale but possess one limitation for large-scale application: they often require numerous inputs related to soil characteristics, management practices, and local weather conditions [24][25][26].…”